Circuit cutting decomposes a large quantum circuit into smaller subcircuits that are executed independently; the original circuit’s expectation values are then recovered by classically combining the measured subcircuit outcomes. While prior work characterises cutting overhead in terms of subcircuit counts and sampling complexity, its end-to-end impact on iterative, estimator-driven training pipelines remains insufficiently measured from a systems perspective. We propose DistributedEstimator , a cut-aware estimator execution pipeline that treats circuit cutting as a staged distributed workload. Each estimator query is instrumented across four phases: partitioning, subexperiment generation, parallel execution, and classical reconstruction. Using logged runtime traces and learning outcomes on two binary classification workloads (Iris and MNIST), we quantify cutting overheads, scaling limits, and sensitivity to injected stragglers, and evaluate whether accuracy and robustness are preserved under matched training budgets. Our measurements reveal that reconstruction constitutes a dominant fraction of per-query time—reaching a median of 53% and a 95th percentile of 58% at three cuts—thereby bounding achievable speed-up under increased parallelism. Despite these overheads, test accuracy is fully preserved on Iris and maintained without systematic degradation on MNIST across all evaluated cut configurations. Robustness under Gaussian noise and FGSM perturbations is similarly preserved, with several cut configurations exhibiting comparable or improved robustness relative to the uncut baseline. The exponential growth of subexperiment counts with each additional cut ( O ( 9 c ) for CNOT-based decomposition) represents a fundamental computational barrier that limits practical experimentation to small qubit counts with current methods. These results establish that practical scaling of circuit cutting for learning workloads requires reducing and overlapping reconstruction, designing scheduling policies for barrier-dominated critical paths, and developing computationally efficient reconstruction strategies for larger qubit counts.

Decentralized federated learning (DFL) enables collaborative model training across edge devices without centralized coordination, offering resilience against single points of failure. However, statistical heterogeneity arising from non-identically distributed local data creates a fundamental challenge: nodes must learn personalized models adapted to their local distributions while selectively collaborating with compatible peers. Existing approaches either enforce a single global model that fits no one well, or rely on heuristic peer selection mechanisms that cannot distinguish between peers with genuinely incompatible data distributions and those with valuable complementary knowledge. We present Murmura, a framework that leverages evidential deep learning to enable trust-aware model personalization in DFL. Our key insight is that epistemic uncertainty from Dirichlet-based evidential models directly indicates peer compatibility: high epistemic uncertainty when a peer's model evaluates local data reveals distributional mismatch, enabling nodes to exclude incompatible influence while maintaining personalized models through selective collaboration. Murmura introduces a trust-aware aggregation mechanism that computes peer compatibility scores through cross-evaluation on local validation samples and personalizes model aggregation based on evidential trust with adaptive thresholds. Evaluation on three wearable IoT datasets (UCI HAR, PAMAP2, PPG-DaLiA) demonstrates that Murmura reduces performance degradation from IID to non-IID conditions compared to baseline (0.9% vs. 19.3%), achieves 7.4$\times$ faster convergence, and maintains stable accuracy across hyperparameter choices. These results establish evidential uncertainty as a principled foundation for compatibility-aware personalization in decentralized heterogeneous environments.

Amid the widespread use of blockchain technology, the escalating frequency of cyberattacks exploiting its inherent security challenges underscores the critical necessity for a robust and adaptable security risk assessment approach. The distinctive attributes and intricate internal structure of blockchain not only attract malicious actors but also elevate the risk of ill-informed architectural design decisions, potentially introducing security vulnerabilities. This study addresses this imperative by conducting a systematic literature review, classifying publications that elucidate secure architectural design approaches and categorising those that delineate methods for assessing security risks associated with blockchain and smart contracts. The findings reveal four prevalent approaches supporting secure architectural design—decision models, taxonomies, design patterns and guidelines—alongside contributions in blockchain risk assessment encompassing risk identification, analysis and evaluation methods. Furthermore, the study identifies unresolved architectural design challenges and proposes future research directions in this evolving landscape.

The integration of edge and cloud computing combines low latency with high computational power, addressing the constraints of edge resources and high access latency inherent in cloud environments. This is essential for deploying Internet of Things (IoT) applications, which are mainly developed by Containers within these heterogeneous environments. However, the open, multi-user nature of edge computing, compounded by a lack of standardized practices, introduces substantial security challenges with severe economic implications. In response, we propose SecConEC, an economically driven framework designed to secure the deployment and execution of containerized IoT applications. We conducted systematic threat modeling using the STRIDE framework, explicitly incorporating quantitative economic risk assessment to identify and prioritize security threats based on their potential economic impacts. We particularly focus on tampering and resource hijacking threats. SecConEC implements robust yet lightweight mitigation and detection mechanisms informed by the MITRE ATT&CK framework through a Security Information and Event Management (SIEM) system. Also, SecConEC introduces a dynamic, security-aware scheduling mechanism that balances performance and security considerations, proactively mitigating economic risks associated with potential security threats. Extensive performance evaluation shows that SecConEC significantly mitigates prioritized threats, effectively securing IoT application deployment and execution in edge-cloud environments, while maintaining low service latency with a minimal performance overhead of 1.7%.

Computation offloading at lower time and lower energy consumption is crucial for resource-constrained mobile devices. This paper proposes an offloading decision-making model using federated learning. Based on the device configuration, task type, and input, the proposed decision-making model predicts whether the task is computationally intensive or not. If the predicted result is computationally intensive , then based on the network parameters the proposed decision-making model predicts whether to offload or locally execute the task. The experimental results show that the proposed method achieves above 90 % prediction accuracy in offloading decision-making, and reduces the response time and energy consumption of the user device by ∼ 11-31 %. A secure partial computation offloading method for federated learning is also proposed to deal with the Straggler effect of federated learning. The results present that the proposed partial computation offloading method for federated learning has achieved a prediction accuracy of above 98 % for the global model.

Mission critical (MC) applications such as defense operations, energy management, cybersecurity, and aerospace control require reliable, deterministic, and low-latency decision making under uncertainty. Although the classical Artificial Intelligence (AI) approaches are effective, they often struggle to meet the stringent constraints of robustness, timing, explainability, and safety in the MC domains. Quantum Artificial Intelligence (QAI), the fusion of artificial intelligence and quantum computing (QC), can potentially provide transformative solutions to the challenges faced by classical ML models. QAI is a broader umbrella than Quantum Machine Learning (QML) and additionally includes quantum optimization, search, and reasoning; we use QAI throughout the paper for the field at large, and QML only for learning-specific subroutines. The principal contributions of this work are: (i) a systematic survey of QAI methods analyzed through the lens of MC requirements like certification, robustness, and timing; (ii) a conceptual quantum cloud resource management and scheduling framework with deployment assumptions, complexity analysis, and failure-mode discussion; and (iii) an identification of the gaps between current QAI capabilities and MC systems requirements. We also propose a conceptual model for management of quantum resources and scheduling of applications driven by timeliness constraints. We discuss multiple challenges, including trainability limits, data access, and loading bottlenecks, verification of quantum components, and adversarial QAI. Finally, we outline future research directions toward achieving interpretable, scalable, and hardware-feasible QAI models for MC application deployment.

Generative AI has emerged as a transformative paradigm for diverse applications, yet the escalating scale of modern models exposes critical computational and memory bottlenecks in classical hardware. This paper investigates the intersection of quantum computing and generative artificial intelligence (QGAI) to address these limitations and scale modern generative models. As models grow to billions of parameters, classical systems face bottlenecks in memory, energy, and training efficiency, while quantum computing offers exponential representational benefits for high-dimensional data. The paper analyzes five core quantum generative architectures-Quantum Circuit Born Machines, Quantum Generative Adversarial Networks, Quantum Boltzmann Machines, Quantum Variational Autoencoders, and Quantum Diffusion models, highlighting their design principles, learning mechanisms, and applications. QGAI models have demonstrated significant promise in domains such as drug discovery, human-machine interaction, IoT security, and financial modelling. Despite these advances, QGAI remains constrained by qubit noise, barren plateaus, and integration challenges. We conclude by identifying ten open research challenges and propose directions for achieving scalable, interpretable, and energy-efficient quantum generative learning.

The deployment of billions of Internet of Things (IoT) devices is driving unprecedented data generation at the network edge, demanding high computational power for real-time deep learning (DL) while raising serious concerns about energy consumption. While edge computing offers a viable paradigm for decentralized DL by preserving data privacy and reducing latency, the substantial energy costs of DL training and inference pose a major challenge for resource-constrained edge devices. This work provides a comprehensive review of state-of-the-art studies that address energy efficiency at the intersection of DL and edge computing. Moving beyond isolated solutions, we analyze the critical need for a co-design approach integrating hardware and software with adaptive resource management to build sustainable systems. The paper systematically examines hardware-level optimizations and software-level techniques for reducing energy consumption while maintaining model accuracy. Furthermore, it investigates how adaptive management of compute, memory, and communication resources is key to dynamic energy savings. Finally, the paper synthesizes recent trends, identifies emerging opportunities, and discusses open challenges, positioning hardware-software co-design as the most promising approach for achieving scalable and energy-efficient deep learning in edge computing.

The rise of Internet of Things (IoT) applications has led to massive data generation. However, dealing with such massive data is challenging. Nowadays, data pipelines are popular mechanisms used to properly deal with data operations at scale in the IoT continuum. Serverless Data Pipelines (SDP) is one such approach to performing event-driven data analysis on data streams. Data pipelines are composed of many components, and scaling the entire pipeline without leaving any bottlenecks is challenging. This study aims to assess the performance of scaling mechanisms in handling stochastic workloads efficiently and understanding critical resource utilization in fog environments. We applied workload-based techniques (Request per Second, Queue Length, Message Rate) and resource-based scaling (CPU) on SDP components of two IoT applications: Aeneas (long-running functions) and PuhatuMonitoring (short-running functions). Using Azure serverless workload patterns, we compared scaling approaches in real-time fog environments, evaluating QoS metrics like processing time and CPU utilization. Our analysis of suitability, using the weighted average scoring method on two QoS metrics, revealed that for compute-intensive tasks, the resource-based scaling approach works effectively for jump, steady, spike, and fluctuation workloads. For short execution time tasks, workload-based scaling suits all four workloads.

With the rapid growth of artificial intelligence and large-scale model computing, the demand for GPUs in datacenters continues to increase, especially for large-scale training and inference tasks. Heterogeneous multi-GPU systems, which integrate GPUs with varying types and computational capabilities, have become critical computing resources. This leads to two main challenges. First, due to the differences in GPU performance and power consumption, task scheduling involves a complex multi-objective optimization to balance energy efficiency and performance. More importantly, the lack of coordinated mechanisms for multi-task sharing and energy-efficient resource management across heterogeneous GPUs can result in GPU overload or underutilization, leading to wasted resources and potential system risks.To address these challenges, we propose HESHARE, an energy-aware and efficient heterogeneous GPU framework for datacenters. First, we design an energy-aware task scheduling strategy that optimizes task allocation across different GPUs to achieve a balance between energy consumption and performance. Second, we introduce a GPU sharing optimization mechanism that adaptively configures MPS and DVFS settings for each GPU, enhancing resource utilization, reducing overall energy consumption, and ensuring task performance. Compared to the state-of-the-art framework, we reduce average energy costs by 26% and improve job completion time by 31%, achieving a balance between energy efficiency and performance.

Stream grouping strategy plays an important role in stateful stream computing environments. Many existing grouping strategies overlook various cost factors associated with grouping while balancing stream load. To overcome this limitation, we propose Pd-Stream, a popularity-aware discriminative grouping strategy that identifies the hot keys in dynamic real-time streams and assigns them to instances with high balance and low cost. Our solution includes: (1) A stream application model is constructed, along with a skewed data stream model and a data stream grouping model. Data stream grouping optimization problems are formalized. (2) A hot key probability estimation algorithm is designed, which estimates real-time probabilities of hot keys based on their popularity within the sampling window. (3) An instance assignment algorithm is designed using dynamic routing. This algorithm determines the minimal number of candidate instances based on the probabilities of hot keys, and selects the target instance with the lowest load through a dynamic routing table. Experimental results show that Pd-Stream provides near-optimal load balancing with low memory, achieving load imbalance as low as$10^{-5}$and replication factor as low as 1.74. It outperforms state-of-the-art works, reducing latency by 27%–46% and improving throughput by 23%–52%.

The rapid proliferation of Internet of Things (IoT) applications across heterogeneous Cloud-Edge-IoT environments presents significant challenges in distributed scheduling optimization. Existing approaches face issues, including fixed neural network architectures that are incompatible with computational heterogeneity, non-Independent and Identically Distributed (non IID) data distributions across IoT scheduling domains, and insufficient cross-domain collaboration mechanisms. This pa per proposes KD-AFRL, a Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning framework that addresses multi-domain IoT application scheduling through three core innovations. First, we develop a resource-aware hybrid architecture generation mechanism that creates dual-zone neural networks enabling heterogeneous devices to participate in collaborative learning while maintaining optimal resource utilization. Second, we propose a privacy-preserving environment-clustered federated learning approach that utilizes differential privacy and K-means clustering to address non-IID challenges and facilitate effective collaboration among compatible domains. Third, we introduce an environment-oriented cross-architecture knowledge distillation mechanism that enables efficient knowledge transfer between heterogeneous models through temperature-regulated soft targets. Comprehensive experiments with real Cloud-Edge IoT infrastructure demonstrate KD-AFRL's effectiveness using diverse IoT applications. Results show significant improvements over the best baseline, with 21% faster convergence and 15.7%, 10.8%, and 13.9% performance gains in completion time, energy consumption, and weighted cost, respectively. Scalability experiments reveal that KD-AFRL achieves 3-5 times better performance retention compared to existing solutions as the number of domains increases.

The transition from monolithic architecture to microservices has enhanced flexibility in application design and its scalable execution. This approach typically uses a computing cluster managed by a container orchestration platform to deploy microservices. However, this shift introduces significant challenges, particularly in the efficient scheduling of containerized services. These challenges are compounded by unpredictable scenarios such as dynamic incoming workloads with various execution traffic and variable communication delays among cluster nodes. Existing works often overlook the real-time traffic impacts of dynamic requests on running microservices, as well as the varied communication delays across cluster nodes. Consequently, even optimally deployed microservices could suffer from significant performance degradation over time. To address these issues, we propose a network and traffic-aware adaptive scheduling framework,TraDE, which can adaptively redeploy microservice instances to maintain desired performance amid changing traffic and network conditions within the hosting cluster. We have implementedTraDEas an extension to the Kubernetes platform. Additionally, we deployed realistic microservice applications in a real compute cluster and conducted extensive experiments to assess our framework's performance in various scenarios. The results demonstrate the effectiveness ofTraDEin rescheduling running microservices to enhance end-to-end performance while maintaining a high goodput ratio. Compared with the existing method NetMARKS,TraDEoutperforms it by reducing the average response time of the application by up to 48.3%, and improving the throughput by up to 1.2-1.5× across workloads while maintaining a goodput ratio of 95.36%, and showing robust adaptive capability to meet QoS targets under sustained workloads and dynamic networking conditions.

Edge computing environments host increasingly complex microservice-based IoT applications, which are prone to performance anomalies that can propagate across dependent services. Identifying the true source of such anomalies—known as Root Cause Localization (RCL)—is essential for timely mitigation. However, existing RCL approaches are designed for cloud environments and rely on centralized analysis, which increases latency and communication overhead when applied at the edge. This paper proposes a decentralized RCL approach that executes localization directly at the edge device level using the Personalized PageRank (PPR) algorithm. The proposed method first groups microservices into communication- and colocation-aware clusters, thereby confining most anomaly propagation within cluster boundaries. Within each cluster, PPR is executed locally to identify the root cause, significantly reducing localization time. For the rare cases where anomalies propagate across clusters, we introduce an inter-cluster peer-to-peer approximation process, enabling lightweight coordination among clusters with minimal communication overhead. To enhance the accuracy of localization in heterogeneous edge environments, we also propose a novel anomaly scoring mechanism tailored to the diverse anomaly triggers that arise across microservice, device, and network layers. Evaluation results on the publicly available edge dataset, MicroCERCL, demonstrate that the proposed decentralized approach achieves comparable or higher localization accuracy than its centralized counterpart while reducing localization time by up to 34%. In addition, scalability experiments conducted on large-scale datasets generated using the iAnomaly framework show that the proposed decentralized approach consistently outperforms the centralized baseline in terms of localization efficiency under increasing system scale.

To meet strict Service-Level Objectives (SLO), contemporary Large Language Models (LLMs) decouple the prefill and decoding stages and place them on separate GPUs to mitigate the distinct bottlenecks inherent to each stage. However, the heterogeneity of LLM workloads causes producer-consumer imbalance between the two instance types in such disaggregated architecture. To address this problem, we propose DOPD (Dynamic Optimal Prefill/Decoding), a dynamic LLM inference system that adjusts instance allocations to achieve an optimal prefill-to-decoding (P/D) ratio based on real-time load monitoring. Combined with an appropriate request-scheduling policy, DOPD effectively resolves imbalances between prefill and decoding instances and mitigates resource allocation mismatches due to mixed-length requests under high concurrency. Experimental evaluations show that, compared with vLLM and DistServe (representative aggregation-based and disaggregation-based approaches), DOPD improves overall system goodput by up to$1.5\times$, decreases P90 time-to-first-token (TTFT) by up to 67.5%, and decreases P90 time-per-output-token (TPOT) by up to 22.8%. Furthermore, our dynamic P/D adjustment technique performs proactive reconfiguration based on historical load, achieving over 99% SLO attainment while using fewer additional resources.

Serverless platforms are increasingly being used to process continuous streams of data. However, data processing units (expressed as serverless 'functions') in such platforms cannot maintain state internally and, instead, rely on remote storage. Stateful serverless is an emerging paradigm that seeks to reduce the latency associated with remote storage access by introducing state servers on worker nodes, i.e., where the serverless functions run. While existing stateful serverless frameworks are successful in reducing latency compared to their stateless counterparts, there are multiple challenges that still need to be addressed in order to meet the real-time data processing requirements of streaming applications. As a result, this paper proposes ScalaSSC, a novel framework for data processing request scheduling and operator state management tailored to stream processing applications within stateful serverless computing. ScalaSSC co-locates requests with the state they act upon to avoid cross-worker state access. To reduce state access contention among executors, ScalaSSC not only introduces the concept of state parallelism to serverless computing, but also processes requests acting upon the same state in the same batch. During batch execution, when multiple states are required, batch APIs are provided to access all states in a single operation. Experimental results show that ScalaSSC achieves up to 845 times higher throughput than another stateful serverless system while maintaining similar end-to-end latency. It also achieves a throughput 14 times higher than that of Amazon Lambda while maintaining end-to-end latency at the microsecond level, in contrast to Lambda's latency at the second level.

Quantum networks are set to transform distributed communication and computation. However, achieving energy efficiency and resource optimization in large-scale networks is notably challenging because of the complexity of creating and routing quantum entanglement. This study presents a new quantum routing framework, Quantum Fidelity-Energy Optimized Routing (QFER), which dynamically balances energy use and fidelity loss to improve entanglement generation in diverse quantum networks. It employs a multiobjective optimization strategy that considers fidelity degradation, energy efficiency, and success probability, considering device variability, network traffic, and coherence limits. Real-time path optimization enables effective entanglement creation and optimal resource use in multi-hop networks. This approach significantly improves network scalability, throughput, and energy efficiency, supporting the development of resilient and sustainable quantum Internet frameworks that can meet the needs of advanced quantum communication systems.

Multi-objective collaborative optimization is essential for improving performance in stream computing systems. However, existing approaches often neglect the interdependencies among communication overhead, load balancing, and energy consumption, and lack predictive capabilities, resulting in delayed scheduling decisions that degrade system latency and throughput. To overcome these limitations, we propose a prediction-driven collaborative framework, named Pc-Stream, which proactively identifies overloaded compute nodes and triggers task migrations in advance. This paper presents this strategy through two key components: (1) A temperature-driven neighborhood adjustment method for task topology partitioning. This method dynamically adjusts the number of migrated tasks based on a predefined temperature. Tasks with high communication volume are batchmigrated to nodes with lower utilization rates during the hightemperature phase, and migrated individually during the lowtemperature phase. (2) A sliding window mechanism that generates multiple sub-sequences for training multiple predictive models. These models enable the system to monitor load trends and proactively migrate tasks from overloaded nodes to those with sufficient resources, thereby reducing communication costs and improving load balance. Experimental results demonstrate that, under dynamic and fluctuating data stream conditions, Pc-Stream significantly enhances overall system performance: reducing average system latency by 49.9 %, and increasing average throughput by 16.9 %.

The rapid evolution of Internet of Things (IoT) environments has created an urgent need for secure and trust-worthy distributed computing systems, particularly when dealing with heterogeneous devices and applications where centralized trust cannot be assumed. This paper proposes TrustMesh, a novel blockchain-enabled framework that addresses these challenges through a unique three-layer architecture combining permissioned blockchain technology with a novel multi-phase Practical Byzantine Fault Tolerance (PBFT) consensus protocol. The key innovation lies in TrustMesh’s ability to support non-deterministic scheduling algorithms while maintaining Byzantine fault tolerance, features traditionally considered mutually exclusive in blockchain systems. The framework supports a sophisticated resource management approach that enables flexible scheduling decisions while preserving the security guarantees of blockchain-based verification. Our experimental evaluation using a real-world cold chain monitoring scenario demonstrates that TrustMesh successfully maintains Byzantine fault tolerance with fault detection latencies under 150 milliseconds, while maintaining consistent framework overhead across varying computational workloads even with network scaling. These results establish TrustMesh’s effectiveness in balancing security, performance, and flexibility requirements in trustless IoT environments, advancing the state-of-the-art in secure distributed computing frameworks.

ABSTRACT The importance of fault tolerance strategies for distributed streaming computing systems becomes more evident due to the increased diversity of failures. Checkpointing is considered a general and efficient method for ensuring fault tolerance. However, determining the checkpoint interval poses a challenge: shorter checkpoint intervals lead to higher overhead, while longer intervals result in extended fault recovery time. Therefore, optimizing the checkpoint interval becomes crucial for the efficient operation of streaming applications. There has been relatively limited exploration and analysis of optimal checkpoint interval settings in the context of stream computing. Many existing works considered adjusting this interval based on a single factor. This article proposes a checkpoint adaptive strategy with high availability, named Ca‐Stream. It considers multiple factors when adjusting checkpoint intervals. Specifically, it addresses the following aspects: (1) Using linear regression to predict the system's fault rate and dynamically adjusting the checkpoint interval based on these predictions. (2) Monitoring CPU time and memory consumption per task to dynamically trigger checkpoints, achieving high reliability, especially in resource‐constrained scenarios. (3) Detecting task execution times on nodes and volume of input data for tasks to identify slow tasks within the cluster. Experiments conducted on a Flink system demonstrate Ca‐Stream's benefits. It reduces checkpoint consumption time by over 38%, system recovery latency by 33%, CPU occupancy by up to 47%, and memory occupancy by 37% compared to Flink's approaches.

In recent years, FPGA platforms have shown significant potential for accelerating artificial intelligence (AI) applications, particularly in Embedded AI. While various studies have explored adaptive AI deployment on FPGAs, there remains a gap in methodologies fully integrating software adaptability with FPGA hardware reconfigurability. This article presents a novel end-to-end co-design methodology for deploying adaptable and scalable Convolutional Neural Networks (CNNs) on FPGA platforms. The framework enhances computational performance and reduces latency by dynamically modifying hardware acceleration units by combining CNN architecture adaptability with dynamic partial reconfiguration of FPGA hardware. The proposed methodology enables automated synthesis and runtime customization of both hardware accelerators and CNN architectures, eliminating the need for iterative synthesis. This approach has been implemented and tested on a Xilinx XC7020 FPGA board for a CNN-based image classifier, achieving superior computation performance (0.68s/image) and accuracy (97%) compared to state-of-the-art alternatives.

With the acknowledged benefits of microservices architectures, such as scalability, flexibility, improved maintenance, and deployment, legacy software systems are increasingly being reengineered into microservices. Recently, a plethora of methods, techniques, tools, and evaluation criteria for reengineering software systems into microservices have been proposed without being systematized. The objective of this work is to conduct an in-depth systematic literature review to identify and analyze methods, techniques, and tools for reengineering software systems into microservices and the ways for evaluating such reengineering initiatives and their results. A systematic literature review of works on reengineering software systems into microservices was performed, yielding 117 primary studies. The review focused on addressing key research questions concerning the evolution of microservices reengineering, methodologies employed, tools available, and the challenges faced in the reengineering process. We used a taxonomy development method to systematize knowledge in these areas. The analysis revealed multiple reengineering approaches: static, dynamic, hybrid, and artifact-driven. Significant evaluation criteria identified include coupling, cohesion, and modularity. Key paradigms for microservices reengineering, such as domain-driven design and interface analysis, were identified and discussed. The study also highlights that incremental and iterative transitions are favored in practice. This study provides a structured overview of the current state of research on reengineering software systems into microservices. It highlights challenges in existing reengineering methodologies. Future directions include validating behavioral equivalence of original and reengineered systems, automating microservices generation, and refining database layer partitioning. The findings emphasize the need for further work to enhance the reengineering process and evaluation of the transition between monolithic and microservices architectures.

Water quality forecasting is a time series analysis task involving estimating future water conditions, vital in environmental management and pollution control. However, existing time series analysis methods focus only on historical observational data, neglecting information from other modalities, leading to incomplete feature extraction and affecting forecasting accuracy and robustness. In addition, the complex spatial dependencies between water quality monitoring stations and the nonlinear fluctuations in water quality indicators caused by meteorological factors present additional challenges. This work proposes a Spatio-Temporal Multimodal Fusion architecture for long-term water quality forecasting, named STMF, to address these issues. It first captures spatio-temporal dependencies by integrating temporal features with upstream-downstream relationships among monitoring stations. Then, STMF further designs a Low-rank Cross-modal Interaction Fusion (LRCIF) method, which fuses spatio-temporal features with precipitation features from the remote sensing image, as an additional modality, effectively leveraging complementary information from multiple data sources to enhance the accuracy and stability of water quality forecasting. Experimental results on real-world water quality datasets demonstrate that the proposed STMF significantly outperforms existing state-of-the-art methods in prediction accuracy. In particular, for long-term forecasting tasks with a 192-step horizon, STMF improves MSE and MAE by 14% and 12%, respectively, compared to unimodal models. It further validates the effectiveness of the multimodal fusion strategy. Overall, STMF offers an effective solution for water quality monitoring and management.

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm calledGeneticSimulated-annealing-basedParticle swarm optimization withAuto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs.

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy.

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption.

The growing IoT landscape requires effective server deployment strategies to meet demands including real-time processing and energy efficiency. This is complicated by heterogeneous, dynamic applications and servers. To address these challenges, we propose ReinFog, a modular distributed software empowered with Deep Reinforcement Learning (DRL) for adaptive resource management across edge/fog and cloud environments. ReinFog enables the practical development/deployment of various centralized and distributed DRL techniques for resource management in edge/fog and cloud computing environments. It also supports integrating native and library-based DRL techniques for diverse IoT application scheduling objectives. Additionally, ReinFog allows for customizing deployment configurations for different DRL techniques, including the number and placement of DRL Learners and DRL Workers in large-scale distributed systems. Besides, we propose a novel Memetic Algorithm for DRL Component (e.g., DRL Learners and DRL Workers) Placement in ReinFog named MADCP, which combines the strengths of Genetic Algorithm, Firefly Algorithm, and Particle Swarm Optimization. Experiments reveal that the DRL mechanisms developed within ReinFog have significantly enhanced both centralized and distributed DRL techniques implementation. These advancements have resulted in notable improvements in IoT application performance, reducing response time by 45%, energy consumption by 39%, and weighted cost by 37%, while maintaining minimal scheduling overhead. Additionally, ReinFog exhibits remarkable scalability, with a rise in DRL Workers from 1 to 30 causing only a 0.3-second increase in startup time and around 2 MB more RAM per Worker. The proposed MADCP for DRL component placement further accelerates the convergence rate of DRL techniques by up to 38%.

Quantum computing holds the promise of solving problems difficult for classical computers. However, we are still in the era of Noisy Intermediate-Scale Quantum (NISQ) computers, necessary to establish effective distributed quantum communication protocols to distribute complex quantum computing tasks across different quantum computers for execution. Significant progress has been made in quantum communication technology, particularly in quantum path creation and resource scheduling. The establishment of quantum paths relies on quantum entanglement and quantum relay technologies, achieving long-distance, high-fidelity quantum state transmission through entanglement swapping between multiple relays. However, resources in quantum communication networks are limited and expensive, making efficient resource scheduling strategies crucial for improving overall network efficiency. To address these issues, we design a network protocol that includes the Historical Weighted Fidelity Routing (HWFR) algorithm and the Dynamic Multi-Priority Quantum Scheduling (DMPQS) algorithm to enhance communication reliability across quantum computers. Both algorithms aim to enhance the reliability of quantum links, optimize resource utilization, and adapt to dynamic changes in the links. The former algorithm dynamically selects the optimal path by considering factors such as link length, noise level, entanglement success rate, and quantum relay resource constraints, ensuring high-fidelity and reliable quantum communication. The latter dynamically adjusts request priorities based on the urgency of quantum service requests and fidelity requirements, optimizing resource utilization. Experimental results show that the proposed protocol performs excellently in terms of an average response time of requests and link utilization, effectively improving the utilization efficiency of network resources and the overall performance of the system.

ABSTRACT Background Cloud Computing has established itself as an efficient and cost‐effective paradigm for the execution of web‐based applications, and scientific workloads, that need elasticity and on‐demand scalability capabilities. However, the evaluation of novel resource provisioning and management techniques is a major challenge due to the complexity of large‐scale data centers. Therefore, Cloud simulators are an essential tool for academic and industrial researchers, to investigate the effectiveness of novel algorithms and mechanisms in large‐scale scenarios. Aim This paper proposes CloudSim 7G, the seventh generation of CloudSim, which features a re‐engineered and generalized internal architecture to facilitate the integration of multiple CloudSim extensions within the same simulated environment. Methods As part of the new design, we introduced a set of standardized interfaces to abstract common functionalities and carried out extensive refactoring and refinement of the codebase. Results The result is a substantial reduction in lines of code with no loss in functionality, significant improvements in run‐time performance and memory efficiency (up to 25\% less heap memory allocated), as well as increased flexibility, ease‐of‐use, and extensibility of the framework. Conclusion These improvements benefit not only CloudSim developers but also researchers and practitioners using the framework for modeling and simulating next‐generation Cloud Computing environments.

ABSTRACT Background In recent years, Large Models (LMs) have been rapidly developed, including large language models, visual foundation models, and multimodal LMs. They are updated and iterated at a very fast pace. These LMs can accomplish many tasks, e.g., daily work assistant, intelligent customer service, and intelligent factory scheduling. Their development has contributed to various industries in human society. Aims The architectural flaws of LMs lead to several problems, including illusions and difficulty in locating errors, limiting their performance. Solving these problems properly can facilitate their further development. Methods This work first introduces the development of LMs and identifies their current problems, including data and energy consumption, catastrophic forgetting, reasoning ability, localization fault, and ethical problems. Then, potential solutions to these problems are provided, including increase data and computation capability, neural‐symbolic synergy, and data orientation to human pattern. Discussion This work discusses developing vertical domain LMs on top of some base LMs. In addition, this work introduces three typical real‐world applications of LMs, including autonomous driving, smart industrial productions, and intelligent medical assistance. Conclusion By embracing the advantages of LMs and solving their fundamental problems, many industries are expected to achieve promising prospects in the future.

ABSTRACT Background Early response systems and efficient resource allocation are vital for ensuring timely and reliable accident detection and prevention, especially in high‐density urban environments such as Kolkata, India. Traditional cloud‐based systems often face challenges in latency, energy consumption, and execution cost. Methods This study proposes an Early Response, Zone‐Based Accident Detection and Resource Allocation framework utilizing a multi‐tier fog–cloud architecture. Historical accident data from 2017 to 2023 are used to identify accident‐prone zones. Advanced machine learning models, including Decision Tree, Random Forest, and XGBoost, are employed to predict accident counts. Optimal emergency routes to nearby ambulance centers, police stations, and hospitals are calculated using a shortest‐path algorithm. The framework is implemented and evaluated using the iFogSim2 simulator. Spatial analysis and visualization are performed using Quantum Geographic Information System (QGIS). Results The proposed architecture shows significant performance improvements, including a 16.77% reduction in energy consumption and an 89.66% reduction in execution cost. Latency and execution times are improved by 11%–20% and 12.08%, respectively. The predictive models demonstrate high accuracy in forecasting accident counts, supporting efficient emergency resource deployment. Conclusion The zone‐prioritized fog computing framework enhances emergency response efficiency by minimizing latency and energy consumption while offering scalable, real‐time accident detection capabilities. The integration of spatial visualization and predictive modeling provides policymakers and urban planners with critical, actionable insights to improve road safety across metropolitan regions. This solution offers a promising direction for safety‐critical applications in smart city environments.

ABSTRACT Narrowband Internet of Things (NB‐IoT) is LPWAN operating using narrowband spectrum in IoTs, requiring low data rates and long battery life. Since NB‐IoT does not support handover, the requirement to sustain network connectivity during mobility may result in fake base station connections, hence applications are limited to stationary use‐cases. Researchers propose extending NB‐IoT in mobile applications, owing to higher signal quality and battery life, despite DoS attacks due to low bandwidth. As NB‐IoT is resource‐constrained, resources must be allocated based on application with data processing offloaded to optimise performance. Cloud servers, being centralised and memory‐intensive, may result in increased computational delay, lower throughput, and DoS attacks in NB‐IoT. Hence, in this paper, we implement fog computing, a decentralised technology, providing scalability, reduced bandwidth, and enhanced privacy, to provide distributed processing. Additionally, we develop secure handover protocols for private and service‐provider‐controlled fog networks under normal and cell‐splitting conditions to minimise fake base station attacks and provide seamless handover. We introduce reputation‐based mechanisms to determine device integrity and differentiate faulty behaviour and attacks. Further, we implement real‐time application‐aware resource allocation and QoS‐based load‐balancing using deep learning to distribute data processing between devices, fog and cloud servers. We simulate and prototype protocols on iFogSim2 and Raspberry Pi 4. Security of the fog computing framework is validated against various attacks and formally verified using Scyther. Evaluation shows that our approach consumes 12% and 43.75% lower power and communication overhead and approximately 6 and 16 times lower execution time and memory compared with existing solutions, thus making our approach lightweight.

ABSTRACT Introduction Federated learning has become an emerging technology in data analysis for IoT applications. Methods This paper implements centralized and decentralized federated learning frameworks for crop yield prediction based on Long Short‐Term Memory Network and Gated Recurrent Unit. For centralized federated learning, multiple clients and one server are considered, where the clients exchange their model updates with the server that works as the aggregator to build the global model. For the decentralized framework, a collaborative network is formed among the devices either using ring topology or using mesh topology. In this network, each device receives model updates from the neighboring devices and performs aggregation to build the upgraded model. Results The performance of the centralized and decentralized federated learning frameworks is evaluated in terms of prediction accuracy, precision, recall, F1‐Score, and training time. The experimental results show that 93% prediction accuracy is achieved using the centralized and decentralized federated learning‐based frameworks. The results also show that using centralized federated learning, the response time can be reduced by 75% than the cloud‐only framework. Conclusion Centralized and decentralized federated learning architectures show good performance in terms of prediction accuracy and loss. The training time, including communication for both case studies, is also not very high, as observed from the results. Further, as no raw data is shared, the data privacy is protected. Finally, the future research directions of the use of federated learning in crop yield prediction are proposed.

Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning (ICPTL)-based model training and (2) cluster-level model (CM) training. These approaches aim to find a tradeoff between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL’s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.

Serverless computing has gained popularity for its ability to shift the burden of server management from developers to cloud providers, which allows providers to exercise greater control over resource management, optimizing configurations to enhance efficiency and performance. The diversity of serverless computing tasks, from short-lived, event-driven tasks to more complex workloads, highlights the growing importance of efficient file I/O performance for I/O-intensive workloads, yet effectively handling ephemeral storage for I/O-intensive tasks remains a challenge. Traditional file system approaches often introduce substantial latency and fail to fully leverage available memory resources within the execution environment, limiting performance and efficiency. Our work stems from the observation of the under-utilization of memory resources in serverless computing platforms and the potential efficiency improvement of I/O operations using an in-memory file system. Based on this observation, we propose Ephemera , a system designed to enhance ephemeral storage efficiency and memory utilization. Ephemera satisfies three design goals: transparent memory I/O integration , heterogeneous tasks resource synergy , and harmonized cluster workload orchestration . Ephemera integrates three components: the Runtime Daemon, responsible for managing a container’s in-memory file system; the Tenant Manager, facilitating memory configuration sharing across containers; and the Cluster Controller, optimizing workload balancing. Our experiments demonstrate that Ephemera significantly improves performance for I/O-intensive tasks compared to traditional file systems. Specifically, Ephemera decreases I/O processing time by 50% on average and reduces latency by up to 95.73% in certain scenarios with negligible overhead.

In recent years, deploying deep learning models on edge devices has become pervasive, driven by the increasing demand for intelligent edge computing solutions across various industries. From industrial automation to intelligent surveillance and healthcare, edge devices are being leveraged for real-time analytics and decision-making. Existing methods face two challenges when deploying machine learning models on edge devices. The first challenge is handling the execution order of operators with a simple strategy, which can lead to a potential waste of memory resources when dealing with directed acyclic graph structure models. The second challenge is that they usually process operators of a model one by one to optimize the inference latency, which may lead to the optimization problem getting trapped in local optima. We present MemoriaNova, comprising BTSearch and GenEFlow, to solve these two problems. BTSearch is a graph state backtracking algorithm with efficient pruning and hashing strategies designed to minimize memory overhead during inference and enlarge latency optimization search space. GenEFlow, based on genetic algorithms (GA), integrates latency modeling, and memory constraints to optimize distributed inference latency. This innovative approach considers a comprehensive search space for model partitioning, ensuring robust and adaptable solutions. We implement BTSearch and GenEFlow and test them on 11 deep-learning models with different structures and scales. The results show that BTSearch can reach 12% memory optimization compared with the widely used random execution strategy. At the same time, GenEFlow reduces inference latency by 33.9% in distributed systems with four-edge devices.

Deploying distributed training workloads of deep learning models atop serverless architecture alleviates the burden of managing servers from deep learning practitioners. However, when supporting deep model training, the current serverless architecture faces the challenges of inefficient communication patterns and rigid resource configuration that incur subpar and unpredictable training performance. In this paper, we proposeFasDL, an efficient serverless-based deep learning training architecture to solve these two challenges.FasDLadopts a novel training frameworkK-REDUCEto release the communication overhead and accelerate the training. Additionally, FasDL builds a lightweight mathematical model forK-REDUCEtraining, offering predictable performance and supporting subsequent resource configuration. It achieves the optimal resource configuration by formulating an optimization problem related to system-level and application-level parameters and solving it with a pruning-based heuristic search algorithm. Extensive experiments on AWS Lambda verify a prediction accuracy over 94% and demonstrate performance and cost advantages over the state-of-art architecture LambdaML by up to 16.8% and 28.3% respectively.

Cloud platforms commonly exploit workload temporal flexibility to reduce their carbon emissions. They suspend/resume workload execution for when and where the energy is greenest. However, increasingly prevalent delay-intolerant real-time workloads challenge this approach. To this end, we present a framework to harvest green renewable energy for real-time workloads in cloud systems. We useRenewables-driven coresin servers to dynamically switch CPU cores between real-time and low-power profiles, matching renewable energy availability. We then develop a VM Execution Model to guarantee running VMs are allocated with cores in thereal-time power profile. If such cores are insufficient, we conduct criticality-aware VM evictions as needed. Furthermore, we develop a VM Packing Algorithm to utilize available cores across the servers. We introduce theGreen Coresconcept in our algorithm to convert renewable energy usage into a server inventory attribute. Based on this, we jointly optimize for renewable energy utilization and reduction of VM eviction incidents. We implement a prototype of our framework in OpenStack asopenstack-gc. Using an experimentalopenstack-gccloud and a large-scale simulation testbed, we expose our framework to VMs running RTEval, a real-time evaluation program, and a 14-day Azure VM arrival trace. Our results show: i) a 6.52× reduction in coefficient of variation of real-time latency over an existing workload temporal flexibility-based solution, and ii) a joint 79.64% reduction in eviction incidents with a 34.83% increase in energy harvest over the state-of-the-art packing algorithms. We open sourceopenstack-gcat https://github.com/tharindu-b-hewage/openstack-gc.

Load imbalance can lead to the emergence of stragglers, i.e., join instances that significantly lag behind others in processing data streams. Currently, state-of-the-art solutions are capable of balancing the load between join instances to mitigate stragglers by managing hot keys and random partitioning. However, these solutions rely on either complicated routing strategies or resource-inefficient processing structures, making them susceptible to frequent changes in load between instances. Therefore, we present Ls-Stream, a data stream scheduler that aims to support dynamic workload assignment for join instances to lighten stragglers. This paper outlines our solution from the following aspects: (1) The models for partitioning, communication, matrix, and resource are developed, formalizing problems like imbalanced load between join instances and state migration costs. (2) Ls-Stream employs a two-level routing strategy for workload allocation by combining hash-based and key-based data partitioning, specifying the destination join instances for data tuples. (3) Ls-Stream also constructs a fine-grained model for minimizing the state migration cost. This allows us to make tradeoffs between data transfer overhead and migration benefits. (4) Experimental results demonstrate significant improvements made by Ls-Stream: reducing maximum system latency by 49.3% and increasing maximum throughput by more than 2x compared to existing state-of-the-art works.

istributedistributedD parallel training of large-scale deep neural networks (DNN) has attracted the attentions of both artificial intelligence and high-performance distributed computing. One of efficient approaches is the micro-batch-based pipeline parallelism (MBPP), e.g., GPipe and Terapipe. Based on the MBPP, we establish a time-cost model with the basic time function of layers, which considers computing time and communication time simultaneously as well as considers they are nonlinear with the amount of input data. Focusing on the jointly optimal solutions of network division and data partition, we propose a Cross-Search algorithm with Improved Multi-dimensional Dichotomy (CSIMD). Through theoretical derivation, we prove improved multi-dimensional dichotomy (IMD) has appreciable theoretical optimality and linear computational complexity significantly faster than the state-of-the-art methods including dynamic programming and recursive algorithm. Extensive experiments on both CNN-based and transformer-based neural networks demonstrate our proposed CSIMD can obtain optimal network division and data partition schemes under MBPP. On average, the training speeds of CSIMD in CNN- and transformer-based DNNs are respectively (2.0, 2.5)× and (2.66, 5.48)× of (MBPP-R, MBPP-E).

The increasing need for large-scale deep neural networks (DNN) has made parallel training an area of intensive focus. One effective method, microbatch-based pipeline parallelism (notably GPipe), accelerates parallel training in various architectures. However, existing parallel training architectures normally use equal data partitioning (EDP), where each layer's process maintains identical microbatch-sizes. EDP may hinder training speed because different processes often require varying optimal microbatch-sizes. To address this, we introduce UMPIPE, a novel framework for unequal microbatches-based pipeline parallelism. UMPIPE enables unequal data partitions (UEDP) across processes to optimize resource utilization. We develop a recurrence formula to calculate the time cost in UMPIPE by considering both computation and communication processes. To further enhance UMPIPE's efficiency, we propose the Dual-Chromosome Genetic Algorithm for UMPIPE (DGAP) that accounts for the independent time costs of forward and backward propagation. Furthermore, we present TiDGAP, a two-level improvement on DGAP. TiDGAP accelerates the process by simultaneously calculating the end time for multiple individuals and microbatches using matrix operations. Our extensive experiments validate the dual-chromosome strategy's optimization benefits and TiDGAP's acceleration capabilities. TiDGAP can achieve better training schemes than baselines, such as the local greedy algorithm and the global greedy-based dynamic programming. Compared to (GPipe, PipeDream), UMPIPE achieves increases in training speed:$(13.89,11.09)\%$for GPT1-14,$(17.11, 7.96)\%$for VGG16 and$\geq (170,100)\%$for simulation networks.

In Kubernetes-based Cloud Native platforms, allocating containers to micro-services elastically according to workload changes is benefical to minimizing resource cost while stabling response times. However, inaccurate performance models for multi-container systems, along with coarse-grained container-based allocation, cause performance fluctuations. In this paper, deep learning, traditional Jackson Queuing Network (JQN) and feedback control are integrated to devise a container provisioning algorithm which leverages the neural networks’ ability to fit nonlinear performance models, the real-time responsiveness of feedback control, and the precise prediction of micro-service interactions offered by the JQN. The proposal is evaluated on a real Kubernetes based Cloud Native cluster. Experimental results illustrate that the container cost is decreased by 10.94%$\sim$11.36% while satifisfying Service Level Agreements (SLA) in terms of 95thaccessing-path response times.

Serverless computing has gained popularity as a novel cloud execution model for applications in recent times. Businesses constantly try to leverage this new paradigm to add value to their revenue streams. The serverless eco-system accommodates many application domains successfully. However, its inherent properties such as cold start delays and relatively high per unit charges appear as a shortcoming for certain application workloads, when compared to a traditional Virtual Machine (VM) based execution scenario. A few research works exist, that study how serverless computing could be used to mitigate the challenges in a VM based cluster environment, for certain applications. In contrast, this work proposes a generalized framework for determining which workloads are best able to reap benefits of a serverless computing environment. In essence, we present a potential hybrid scheduling solution for exploiting the benefits of both a serverless and a VM based serverful computing environment. Our proposed framework leverages the actor-critic based deep reinforcement learning architecture coupled with the proximal policy optimization technique, in determining the best scheduling decision for workload executions. Extensive experiments conducted demonstrate the effectiveness of such a solution, in terms of user cost and application performance, with improvements of up to 44% and 11% respectively.

With the continuous increase of IoT applications, their effective scheduling in edge and cloud computing has become a critical challenge. The inherent dynamism and stochastic characteristics of edge and cloud computing, along with IoT applications, necessitate solutions that are highly adaptive. Currently, several centralized Deep Reinforcement Learning (DRL) techniques are adapted to address the scheduling problem. However, they require a large amount of experience and training time to reach a suitable solution. Moreover, many IoT applications contain multiple interdependent tasks, imposing additional constraints on the scheduling problem. To overcome these challenges, we propose a Transformer-enhanced Distributed DRL scheduling technique, called TF-DDRL, to adaptively schedule heterogeneous IoT applications. This technique follows the Actor-Critic architecture, scales efficiently to multiple distributed servers, and employs an off-policy correction method to stabilize the training process. In addition, Prioritized Experience Replay (PER) and Transformer techniques are introduced to reduce exploration costs and capture long-term dependencies for faster convergence. Extensive results of practical experiments show that TF-DDRL, compared to its counterparts, significantly reduces response time, energy consumption, monetary cost, and weighted cost by up to 60%, 51%, 56%, and 58%, respectively.

Effective task scheduling in stream computing systems can reduce the latency by minimizing inter-node communication. However, this approach often requires restarting tasks to change their deployment locations, resulting in significant system overhead and making it inadequate especially in dynamically changing data stream environments. To address this issue, we propose Ns-Stream, a hierarchical data scheduler that dynamically adjusts data distribution weights between near-source and off-source tasks. Our solution includes: (1) We observe that communication overhead from off-source data processing significantly impacts system latency when tasks' resources are ample. However, as the resources become limited, the computational power required by tasks becomes the key constraint on system performance. (2) During initialization scheduling, we deploy tasks with potential communication to the same node using the graph convolutional network, thus avoiding the need for runtime task scheduling. (3) We dynamically adjust data distribution weights between near-source and off-source tasks based on their computing capabilities, prioritizing local processing of data tuples (within the same worker and node) to optimize resource utilization and reduce data transmission overhead. (4) Experimental results demonstrate significant improvements made by Ns-Stream: reducing maximum system latency by 40% and increasing maximum throughput by 55% compared to existing state-of-the-art works.

The quantum cloud computing paradigm presents unique challenges in task placement due to the dynamic and heterogeneous nature of quantum computation resources. Traditional heuristic approaches fall short in adapting to the rapidly evolving landscape of quantum computing. This paper proposes DRLQ, a novel Deep Reinforcement Learning (DRL)- based technique for task placement in quantum cloud computing environments, addressing the optimization of task completion time and quantum task scheduling efficiency. It leverages the Deep Q Network (DQN) architecture, enhanced with the Rainbow DQN approach, to create a dynamic task placement strategy. This approach is one of the first in the field of quantum cloud resource management, enabling adaptive learning and decision-making for quantum cloud environments and effectively optimizing task placement based on changing conditions and resource availability. We conduct extensive experiments using the QSimPy simulation toolkit to evaluate the performance of our method, demonstrating substantial improvements in task execution efficiency and a reduction in the need to reschedule quantum tasks. Our results show that utilizing the DRLQ approach for task placement can significantly reduce total quantum task completion time by 37.81 % to 72.93% and prevent task rescheduling attempts compared to other heuristic approaches.

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction.

Cloud data centres (CDCs) are the backbone infrastructures of modern digital society, but they also consume huge amounts of energy and generate heat.To manage CDC resources efficiently, we must consider the complex interactions between diverse workloads and data centre components.However, most existing resource management systems rely on simple and static rules that fail to capture these complex interactions.Therefore, we require new data-driven Machine learning-based resource management approaches that can efficiently capture the interdependencies between parameters and guide resource management systems.This review describes the in-depth analysis of the existing resource management approaches in CDCs for energy and thermal efficiency.It mainly focuses on learning-based resource management systems in data centres and also identifies the need for integrated computing and cooling systems management.A taxonomy on energy and thermal efficient resource management in data centres is proposed.Furthermore, based on this taxonomy, existing resource management approaches from server level, data centre level, and cooling system level are discussed.Finally, key future research directions for sustainable Cloud computing services are proposed.

In cross-domain stream computing, assigning highly dependent tasks to different domains causes poor performance. Existing methods ignore cross-domain and focus on load balancing and resource allocation. To address this scheduling challenge, this paper proposes a task dependency-aware scheduling strategy named Td-Stream. This strategy is discussed in the following aspects: (1) Impact analysis: Analyzing the adverse impact of communication dependencies between tasks on system performance under traditional scheduling methods in cross-domain environments. (2) Model construction: Constructing models for stream topology, task dependency, resource cost.(3) Cross-domain task allocation: Introducing a cross-domain dependent task allocation method that incorporates a resource elasticity mechanism. Experimental results demonstrate significant improvements made by Td-Stream compared to existing state-of-theart works.

The application of Transformer-based large models has achieved numerous success in recent years. However, the exponential growth in the parameters of large models introduces formidable memory challenge for edge deployment. Prior works to address this challenge mainly focus on optimizing the model structure and adopting memory swapping methods. However, the former reduces the inference accuracy, and the latter raises the inference latency. This paper introduces PIPELoAD, a novel memory-efficient pipeline execution mechanism. It reduces memory usage by incorporating dynamic memory management and minimizes inference latency by employing parallel model loading. Based on PIPELoAD mechanism, we present Hermes, a framework optimized for large model inference on edge devices. We evaluate Hermes on Transformer-based models of different sizes. Our experiments illustrate that Hermes achieves up to 4.24 x increase in inference speed and 86.7% lower memory consumption than the state-of-the-art pipeline mechanism for BERT and ViT models, 2.58 x increase in inference speed and 90.3% lower memory consumption for GPT-style models.

Summary An effective scheduling strategy is critical for achieving better performance in real‐time stream processing systems. How to quickly and efficiently process real‐time data stream is always challenging, especially when clusters are collaborating in a Geo‐Distributed computing environment. To address these challenges, we propose an elastic scheduling strategy with Latency Constraints in Geo‐Distributed stream computing environments called Lc‐Stream. This article discusses our work from the following aspects: (1) An optimized data stream redirection method that is proposed based on queuing network algorithm, along with a computing resource model, a latency constrained scheduling model and a communication energy consumption model. (2) An updated node selection method based on the inter‐layer task correlation, to reduce the communication latency between groups at the executor granularity. (3) A network cluster distribution for Geo‐Distributed computing environment to ensure energy saving under low transmission latency. Experimental results show that compared to R‐Storm, Lc‐Stream reduces total latency by over 19% and increases throughput by over 37% in typical cross‐domain multi‐task topologies. Compared to Ts‐Stream, Lc‐Stream also reduces total latency by over 15% and increases throughput by over 21%. At the same time, it helps to balance the load among the systems and avoid overuse of compute nodes.

Quantum computing is rapidly reaching a point in which its application design and engineering aspects must be seriously considered. However, quantum software engineering is still in its infancy, with numerous challenges, especially in dealing with the diversity of quantum programming languages and noisy intermediate-scale quantum (NISQ) systems. To alleviate these challenges, we propose QFaaS, a holistic Quantum Function-as-a-Service framework, which leverages the advantages of the serverless model, DevOps lifecycle, and the state-of-the-art software techniques to advance practical quantum computing for next-generation application development in the NISQ era. Our framework provides essential elements of a serverless quantum system to streamline service-oriented quantum application development in cloud environments, such as combining hybrid quantum–classical computation, automating the backend selection, cold start mitigation, and adapting DevOps techniques. QFaaS offers a full-stack and unified quantum serverless platform by integrating multiple well-known quantum software development kits (Qiskit, Q#, Cirq, and Braket), quantum simulators, and cloud providers (IBM Quantum and Amazon Braket). This paper proposes the concept of quantum function-as-a-service, system design, operation workflows, implementation of QFaaS, and lessons learned on the benefits and limitations of quantum serverless computing. We also present practical use cases with various quantum applications on today’s quantum computers and simulators to demonstrate our framework capability to facilitate the ongoing quantum software transition.

Edge/fog computing, as a distributed computing paradigm, satisfies the low-latency requirements of ever-increasing number of IoT applications and has become the mainstream computing paradigm behind IoT applications. However, because large number of IoT applications require execution on the edge/fog resources, the servers may be overloaded. Hence, it may disrupt the edge/fog servers and also negatively affect IoT applications’ response time. Moreover, many IoT applications are composed of dependent components incurring extra constraints for their execution. Besides, edge/fog computing environments and IoT applications are inherently dynamic and stochastic. Thus, efficient and adaptive scheduling of IoT applications in heterogeneous edge/fog computing environments is of paramount importance. However, limited computational resources on edge/fog servers imposes an extra burden for applying optimal but computationally demanding techniques. To overcome these challenges, we propose a Deep Reinforcement Learning-based IoT application Scheduling algorithm, called DRLIS to adaptively and efficiently optimize the response time of heterogeneous IoT applications and balance the load of the edge/fog servers. We implemented DRLIS as a practical scheduler in the FogBus2 function-as-a-service framework for creating an edge-fog-cloud integrated serverless computing environment. Results obtained from extensive experiments show that DRLIS significantly reduces the execution cost of IoT applications by up to 55%, 37%, and 50% in terms of load balancing, response time, and weighted cost, respectively, compared with metaheuristic algorithms and other reinforcement learning techniques.

Resource usage prediction in cloud data centers is critically important. It can improve providers’ service quality and avoid resource wastage and insufficiency. However, the time series of resource usage in cloud environments is characterized by multidimensional, nonlinear, and high-volatility characteristics. Achieving high-accuracy prediction for time series with such characteristics is necessary but difficult. Traditional prediction methods based on regression algorithms and recurrent neural networks cannot effectively extract nonlinear features from data sets. Besides, many deep learning models suffer from gradient explosion or gradient vanishing during the training stage. Current commonly used prediction methods fail to uncover some vital information about the frequency domain features in the time series. To resolve these challenges, we design a Forecasting method based on the Integration of a Savitzky–Golay (SG) filter, a frequency enhanced decomposed transformer (FEDformer) model, and a frequency-enhanced channel attention mechanism (FECAM), named FISFA. It adopts the SG filter to reduce noise and smooth sequences in the raw sequences of resources. Then, we develop a hybrid transformer-based model integrating FEDformer and the FECAM, effectively capturing the frequency domain patterns. Besides, a meta-heuristic optimization algorithm, i.e., genetic simulated annealing-based particle swarm optimizer, is proposed to optimize key hyperparameters of FISFA. Then, FISFA predicts the future needs for multidimensional resources in highly fluctuating traces in real-life cloud environments. Experimental results demonstrate that FISFA achieves higher accuracy and performs more efficient prediction than several benchmark forecasting methods with realistic data sets collected from Alibaba and Google cluster traces. FISFA improves the prediction accuracy on average by 32.14%, 25.49%, and 27.71% over vanilla long short-term memory, transformer, and Informer methods, respectively.

Mobile edge computing (MEC) paradigm supports cloud-like computing capabilities at the edge of the network and offers low-latency services. Proxy servers of MEC with mobility and limited computing, e.g., flying unmanned aerial vehicles (UAVs) have emerged as competitors in providing services. This work considers a task offloading problem for an UAV-assisted MEC system and designs an integrated cloud-edge network with multiple mobile users (MUs) and layered UAVs to improve MEC with a network of UAVs. In our system, edge UAVs (EUAVs) and the cloud collaborate to provide caching and computing services for MUs. We consider static and dynamic applications that support task offloading. Our proposed approach minimizes the weighted cost of latency and energy consumption by jointly optimizing caching and offloading, deployment of EUAVs, and allocation of computation resources. Simultaneously, this work also considers UAVs’ caching and computation capacities while meeting MUs’ latency and energy constraints. Thus, a constrained mixed integer nonlinear program for a layered UAV-assisted hybrid cloud-edge system is formulated. To solve it, this work designs a hybrid metaheuristic algorithm named adaptive and genetic simulated annealing (SA)-based particle swarm optimization (AGSP). Experimental results with a real-life dataset verify that the AGSP’s system energy consumption and task latency are reduced by at least 7.4% and 8.46%, respectively, compared with the state-of-the-art algorithms, thus proving that AGSP greatly enhances the energy and latency of the system.

Multi-access edge computing systems (MECs) bring the capabilities of cloud computing closer to the radio access network (RAN), in the context of 4G and 5G telecommunication systems, and converge with existing radio access technologies like satellite or WiFi. An MEC is a cloud server that runs at the mobile network’s edge and is installed and executed using virtual machines (VMs), containers, and/or functions. A cloudlet is similar to an MEC that consists of many servers which provide real-time, low-latency, computing services to connected users in close proximity. In connected vehicles, services may be provisioned from the cloud or edge that will be running users’ applications. As a result, when users travel across many MECs, it will be necessary to transfer their applications in a transparent manner so that performance and connectivity are not negatively affected. In this paper, we propose an effective strategy for migrating connected users’ services from one edge to another or, more likely, to a remote cloud in an MEC. A mathematical model is presented to estimate the expected times to allocate and migrate services. Our evaluations, based on real workload traces and mobility patterns, suggest that the proposed strategy “ApMove" migrates connected services while ensuring their performance ( 0.004% – 2.99% loss), reduced runtimes, therefore, users’ costs ( 4.3% – 11.63%), and minimizing the response time ( 7.45% – 9.04%). Furthermore, approximately 17.39% migrations are avoided. We also study the impacts of variations in the car’s speed and network transfer rates on service migration durations, latencies, and service execution times.

MicroService Architecture (MSA) is gaining rapid popularity for developing large-scale IoT applications for deployment within distributed and resource-constrained Fog computing environments. As a cloud-native application architecture, the true power of microservices comes from their loosely coupled, independently deployable and scalable nature, enabling distributed placement and dynamic composition across federated Fog and Cloud clusters. Thus, it is necessary to develop novel placement algorithms that utilise these microservice characteristics to improve the performance of the applications. However, existing Fog computing frameworks lack support for integrating such placement policies due to their shortcomings in multiple areas, including MSA application placement and deployment across multi-fog multi-cloud environments, dynamic microservice composition across multiple distributed clusters, scalability of the framework to operate within federated environments, support for deploying heterogeneous microservice applications, etc. To this end, we design and implement MicroFog, a Fog computing framework compatible with cloud-native technologies such as Docker, Kubernetes and Istio. MicroFog provides an extensible and configurable control engine that executes placement algorithms and deploys applications across federated Fog environments. Furthermore, MicroFog provides a sufficient abstraction over container orchestration and dynamic microservice composition, thus enabling users to easily incorporate new placement policies and evaluate their performance. The capabilities of the MicroFog framework, such as the scalability and flexibility of the design and deployment architecture of MicroFog and its ability to ensure the deployment and composition of microservices across distributed fog-cloud environments, are validated using multiple use cases. Experiments also demonstrate MicroFog’s ability to integrate and evaluate novel placement policies and load-balancing techniques. To this end, we integrate multiple microservice placement policies to demonstrate MicroFog’s ability to support horizontally scaled placement, service discovery and load balancing of microservices across federated environments, thus reducing the application service response time up to 54%.

Abstract The advent of edge computing holds immense promise for advancing the digitization of society, ushering in critical applications that elevate the overall quality of life. Yet, the practical implementation of the edge paradigm proves more challenging than anticipated, encountering disruptions primarily due to the constraints of applying conventional cloud‐based strategies at the network's periphery. Increasingly influenced by sustainability commitments, industry regulations currently view edge computing as a potential threat, primarily due to the energy inefficiency of solutions situated in close proximity to data generation sources and the rising density of computing. This paper presents a proactive strategy to transform the perceived threat into an opportunity, steering the sustainable evolution of future edge infrastructures to make them both environmentally and economically competitive for accelerated adoption. The vision outlined addresses key challenges associated with edge deployment and operation, emphasizing energy efficiency, fault‐tolerant automation, and collaborative orchestration. The proposed approach integrates two‐phase immersion cooling, formal modeling, machine learning, and federated management to effectively harness heterogeneity, propelling the sustainability of edge computing. To substantiate the efficacy of this approach, the paper details initial efforts towards establishing the sustainability of an edge infrastructure designed for an Advanced Driver Assistance Systems application.

Abstract Crop yield prediction is a crucial area in agriculture that has a large impact on the economy of a country. This article proposes a crop yield prediction framework based on Internet of Things and edge computing. We have used a fifth generation network device referred to as femtolet as the edge device. The femtolet is a small cell base station that has high storage and high processing ability. The sensor nodes collect the soil and environmental data, and then the collected data is sent to the femtolet through the microcontrollers. The femtolet retrieves the weather‐related data from the cloud, and then processes the sensor data and weather‐related data using Bi‐LSTM. The femtolet after processing the data sends the generated results to the cloud. The user can access the results from the cloud to predict the suitable crop for his/her land. This is observed that the suggested framework provides better accuracy, precision, recall, and F1‐score compared to the state‐of‐the‐art crop yield prediction frameworks. This is also demonstrated that the use of femtolet reduces the latency by ˜25% than the conventional edge‐cloud framework.

Abstract Cloud computing environment simulators enable cost‐effective experimentation of novel infrastructure designs and management approaches by avoiding significant costs incurred from repetitive deployments in real Cloud platforms. However, widely used Cloud environment simulators compromise on usability due to complexities in design and configuration, along with the added overhead of programming language expertise. Existing approaches attempting to reduce this overhead, such as script‐based simulators and graphical user interface (GUI) based simulators, often compromise on the extensibility of the simulator. Simulator extensibility allows for customization at a fine‐grained level, thus reducing it significantly affects flexibility in creating simulations. To address these challenges, we propose an architectural framework to enable human‐readable script‐based simulations in existing Cloud environment simulators while minimizing the impact on simulator extensibility. We implement the proposed framework for the widely used Cloud environment simulator, the CloudSim toolkit, and compare it against state‐of‐the‐art baselines using a practical use case. The resulting framework, called CloudSim Express, achieves extensible simulations while surpassing baselines with over a % reduction in code complexity and an 89.42% reduction in lines of code.

Summary Quantum computing resources are predominantly accessible through cloud services, with a potential future shift to edge networks. This paradigm and the increasing global interest in quantum computing have amplified the need for efficient, adaptable resource management strategies and service models for quantum systems. However, many limitations in the quantum resources' quantity, quality, availability, and cost pose significant challenges for conducting research in practical environments. To address these challenges, we proposed iQuantum, a holistic and lightweight discrete‐event simulation toolkit uniquely tailored to model hybrid quantum computing environments. We also present a detailed system model for prototyping and problem formulation in quantum resource management. Through rigorous empirical validation and evaluations using large‐scale quantum workload datasets, we demonstrate the flexibility and applicability of our toolkit in various use cases. iQuantum provides a versatile environment for designing and evaluating quantum resource management policies such as quantum task scheduling, backend selection, hybrid task offloading, and orchestration in the quantum cloud‐edge continuum. Our work endeavors to create substantial contributions to quantum computing modeling and simulation, empowering the creation of future resource management strategies and quantum computing's broader applications.

Special issue on efficient management of microservice-based systems and applicationsThe advent of microservice architecture marks a transition from conventional monolithic applications to a landscape of loosely linked, lightweight, and autonomous microservice components.The primary objective is to ensure strong environmental uniformity, portability across various operating systems, and robust resource isolation.Leading cloud service providers such as Amazon, Microsoft, Google, and Alibaba have widely embraced microservices within their infrastructures.This adoption is geared toward automating application management and optimizing system performance.Consequently, addressing the automation of tasks like deployment, maintenance, auto-scaling, and networking of microservices becomes pivotal.This underscores the importance of efficient management of systems and applications built on microservices as a critical research challenge.Efficient management methods must not only ensure the quality of service (QoS) across multiple microservices units (containers) but also provide greater control over individual components.However, the dynamic and varied nature of microservice applications and environments significantly amplifies the complexity of these management approaches.Each microservice unit can be deployed and operated independently, catering to distinct functionalities and business objectives.Furthermore, microservices can interact and combine through lightweight communication techniques to form a complete application.The expanding scale of microservice-based systems and their intricate interdependencies pose challenges in terms of load distribution and resource management at the infrastructure level.Furthermore, as cloud workloads surge in resource demands, bandwidth consumption, and QoS requirements, the traditional cloud computing environment extends to fog and edge infrastructures that are in close proximity to end users.As a result, current microservice management approaches need further enhancement to address the mounting resource diversity, application distribution, workload profiles, security prerequisites, and scalability demands across hybrid cloud infrastructures.Keeping this in mind, this special issue addressed some of the aspects related to efficient management of microservice-based systems and applications with the focus on various challenges faced, and promising solutions to address such challenges by using software engineering, machine learning and deep learning techniques.We have received 21 submissions in this issue, and we accepted six high-quality submissions for publication after a rigorous review process with at least three reviewers for each paper.The authors are from diverse countries, including the USA, China, UK, Germany, India, Brazil, etc.Each of the accepted papers is summarized as follows.In the first article, Batista et al. 1 presented two strategies for handling asynchronous workloads associated with tax integration in a multi-tenant microservice architecture specific to the company's context.The initial approach utilizes polling, employing a queue as a distributed lock.The second approach, named the single active consumer, adopts a push-based technique, leveraging the message broker's logic for message delivery.These methodologies are designed to optimize resource allocation in scenarios involving an increasing number of container replicas and tenants.In the second article, Kumar et al. 2 introduced a resource allocation model designed to enhance the QoS in microservice deployment.Utilizing a Fine-tuned Sunflower Whale Optimization Algorithm, the model strategically deploys container-based services on physical machines, optimizing their execution capacity by efficiently utilizing CPU and memory resources.The primary objective of this proposed technique is to achieve an efficient distribution of workload, preventing resource wastage and ultimately enhancing QoS parameters.In the third paper, Zhu et al. 3 introduced RADF, a semi-automatic approach for decomposing a monolith into serverless functions by analyzing the inherent business logic present in the application's interface.The proposed method employs a two-stage refactoring strategy, initially performing a coarse-grained decomposition followed by a fine-grained one.This approach streamlines the decomposition process into smaller, more manageable steps, providing adaptability to generate a solution at either the microservice or function level.In the fourth paper, Würz et al. 4 identified the principal tasks and subtasks of the application for the purpose of partitioning.Subsequently, they outlined the program flow to ascertain which application tasks could be transformed into functions and elucidated their interdependencies.In the concluding step, they precisely specified individual functions

Abstract The number of Internet of Things (IoT) applications, especially latency‐sensitive ones, have been significantly increased. So, cloud computing, as one of the main enablers of the IoT that offers centralized services, cannot solely satisfy the requirements of IoT applications. Edge/fog computing, as a distributed computing paradigm, processes, and stores IoT data at the edge of the network, offering low latency, reduced network traffic, and higher bandwidth. The edge/fog resources are often less powerful compared to cloud, and IoT data is dispersed among many geo‐distributed servers. Hence, Federated Learning (FL), which is a machine learning approach that enables multiple distributed servers to collaborate on building models without exchanging the raw data, is well‐suited to edge/fog computing environments, where data privacy is of paramount importance. Besides, to manage different FL tasks on edge/fog computing environments, a lightweight resource management framework is required to manage different incoming FL tasks while does not incur significant overhead on the system. Accordingly, in this article, we propose a lightweight FL framework, called FLight, to be deployed on a diverse range of devices, ranging from resource‐limited edge/fog devices to powerful cloud servers. FLight is implemented based on the FogBus2 framework, which is a containerized distributed resource management framework. Moreover, FLight integrates both synchronous and asynchronous models of FL. Besides, we propose a lightweight heuristic‐based worker selection algorithm to select a suitable set of available workers to participate in the training step to obtain higher training time efficiency. The obtained results demonstrate the efficiency of the FLight. The worker selection technique reduces the training time of reaching 80% accuracy by 34% compared to sequential training, while asynchronous one helps to improve synchronous FL training time by 64%.

With the advance of smart manufacturing and information technologies, the volume of data to process is increasing accordingly. Current solutions for big data processing resort to distributed stream processing systems, such as Apache Flink and Spark. However, such frameworks face challenges of resource underutilization and high latency in big data application scenarios. In this article, we propose SPSC, a serverless-based stream computing framework where events are discretized into the atomic stream and stateless Lambda functions are taken as context-irrelevant operators, achieving task parallelism and inherent data parallelism in processing. Also, we implement a prototype of the framework on Amazon Web service (AWS) using AWS Lambda, AWS simple queue service, and AWS DynamoDB. The evaluation shows that compared with Alibaba's real-time computing Flink version, SPSC outperforms by 10.12% when the overhead is close.

In the Internet of Things (IoT) environment, a wide variety of mobile devices (MDs) have become part of it, leading to a dramatic increase in the amount of task data. However, due to the limited battery capacity and computing resources of MDs, a lot of effort is required to be taken on how to process more data with less energy. In this paper, we take into account the low utilization of spectrum resources and the short battery life of the equipment, and a backscatter communication-mobile edge computing (BC-MEC) network system based on Non-orthogonal multiple access (NOMA) communication mode is proposed. In order to maximize the computation energy efficiency (CEE) of the system, we jointly optimize the backscatter coefficient of each MD, the backscatter communication duration, the direct offloading duration, the MEC server processing time, the local processing time, the direct offloading power of each MD, the calculation frequency of the MEC server, and the local calculation frequency of each MD. We then formulate it as a joint fractional optimization problem, which is a non-convex optimization problem that is difficult to solve by heuristic algorithms with high computational complexity. To this end, we transform such a problem into a convex problem and apply the Lagrangian dual method to solve it efficiently. Furthermore, in order to meet different user requirements, two effective iterativeDinkelbach algorithms based onBackscatterCoefficientUpdates (DBCU) are proposed to solve this problem. Extensive simulation results demonstrate the superiority of our proposed approach, which improves the system CEE by at least 10% compared to state-of-the-art methods.

Services provided by mobile edge clouds offer low-latency responses for large-scale and real-time applications. Dynamic service management algorithms generate live service migration requests to support user mobility and ensure service latency in mobile edge clouds. To handle these migration requests, multiple migration planning and scheduling algorithms are necessary to calculate the migration order and optimize the performance and overhead of multiple migrations. However, current planning and scheduling algorithms in cloud data centers are not suitable for dynamic and large-scale scenarios in edge computing, as the network topology expands and the number of migration requests increases. Edge computing requires near real-time scheduling to handle user mobility-induced live migrations. To address this issue, this paper presents an efficient multiple migration planning and scheduling framework for edge computing. The framework includes a lifecycle management framework and innovative iterative Maximal Independent Set-based scheduling algorithms based on the resource dependency graph of multiple migrations. Our solution is shown to efficiently schedule live migrations at scale using real-world taxi traces and telecom base station coordinates. It can achieve significant processing speedups over existing migration planning algorithms in clouds, up to 3000 times, while ensuring multiple and individual migration performance for time-critical services.

The integration of software-defined networking (SDN) and cloud radio access networks (CRANs) into vehicular ad hoc networks (VANETs) presents intricate challenges to achieving stringent service level objectives (SLOs). These objectives include optimizing data flow and resource management, achieving low latency and rapid response times, and ensuring network resilience under fluctuating conditions. Traditional load balancing and clustering approaches, designed for more static environments, fall short in the dynamic and variable context of VANETs. This necessitates a paradigm shift towards more adaptive and robust strategies to meet these advanced SLOs reliably. This paper proposes a software-defined vehicular fog computing (SDFC) framework that refines resource allocation in VANETs. Our SDFC framework utilizes an intelligent controller placement that strategically positions decision-making entities within the network to optimize data flow and resource distribution. This placement is governed by a dynamic clustering algorithm that responds to variable network conditions, an advancement over the static mappings used by traditional methods. By incorporating parallel processing principles, the framework ensures that computational tasks are distributed effectively across network nodes, reducing bottlenecks and enhancing overall network agility. Empirical evaluations (testbed) and simulation results of our framework indicate a substantial increase in network efficiency: a 28% improvement in average response time, a 23% decrease in network latency, and a 25% faster convergence to optimal resource distribution compared to state-of-the-art methods. These improvements testify to the framework's ability to underscore its potential to refine operational efficacy within VANETs.

The fog computing paradigm is rapidly gaining popularity for latency-critical and bandwidth-hungry IoT application deployment. Meanwhile, MicroService Architecture (MSA) is increasingly adopted for developing IoT applications due to its high scalability and extensibility. For mission-critical IoT services in fog, reliability remains one of the most critical QoS requirements due to less dependability of fog resources. Granular microservices with independent deployment and scaling exhibit great potential in utilising resource-constrained fog resources to improve reliability through redundant placement. However, current research on service placement lacks reliability-aware holistic approaches that combine the MSA features and failure characteristics of fog resources under independent and correlated failures. Hence, we analyse MSA and formulate the reliability-aware placement problem by modelling composite services as k-out-of-n serial-parallel systems in a throughput-aware manner for placement under fog resource failures. Our proposed Reliability-aware Placement Method (RPM) is a hierarchical policy combining improved PSO and NSGA-II algorithms. We integrate it with Monte Carlo reliability calculations to produce redundant placements reaching a trade-off between reliability and cost. The performance results reveal that compared to the benchmarks, our algorithm shows significant improvements in reliability satisfaction (up to 25%) and time to first failure (up to 40%), thus providing a robust placement method.

Edge computing (EC) enables low-latency services by pushing computing resources to the network edge. Due to the geographic distribution and limited capacities of edge servers, EC systems face the challenge of edge distributed denial-of-service (DDoS) attacks. Existing systems designed to fight cloud DDoS attacks cannot mitigate edge DDoS attacks effectively due to new attack characteristics. In addition, those systems are typically activated upon detected attacks, which is not always realistic in EC systems. DDoS mitigation needs to be cohesively integrated with workload migration at the edge to ensure timely responses to edge DDoS attacks. In this paper, we present EdgeShield, a novel DDoS mitigation system that leverages edge servers’ computing resources collectively to defend against edge DDoS attacks without the need for attack detection. Aiming to maximize system throughput over time without causing significant service delays, EdgeShield monitors service delays and migrates workloads across an EC system with adaptive mitigation strategies. The experimental results show that EdgeShield significantly outperforms state-of-the-art solutions in both system throughput and service delays.

The ever-increasing demand for the cloud computing paradigm has resulted in the widespread deployment of multiple datacenters, the operations of which consume very high levels of energy. The carbon footprint resulting from these operations threatens environmental sustainability while the increased energy costs have a direct impact on the profitability of cloud providers. Using renewable energy sources to satisfy the energy demands of datacenters has emerged as a viable approach to overcome the aforementioned issues. The problem of scheduling workflows across multi-cloud environments powered through a combination of brown and green energy sources includes multiple levels of complexities. First, the general case of workflow scheduling in a distributed system itself is NP-hard. The need to schedule workflows across geo-distributed cloud datacenters adds a further layer of complexity atop the general problem. The problem becomes further challenging when the datacenters are powered through renewable sources which are inherently intermittent in nature. Consequently, traditional workflow scheduling algorithms and single-agent reinforcement learning algorithms are incapable of efficiently meeting the decentralized and adaptive control required for addressing these challenges. To this end, we have leveraged the recent advancements in the paradigm of MARL (Multi-Agent Reinforcement Learning) for designing and developing a multi-agent RL framework for optimizing the green energy utilization of workflow executions across multi-cloud environments. The results of extensive simulations demonstrate that the proposed approach outperforms the comparison algorithms with respect to minimizing energy consumption of workflow executions by 47% while also keeping the makespan of workflows in par with comparison algorithms. Furthermore, with the proposed optimizations, the multi-agent technique learnt 5 times faster than a generic multi-agent algorithm.

Function-as-a-Service (FaaS) introduces a lightweight, function-based cloud execution model that finds its relevance in a range of applications like IoT-edge data processing and anomaly detection. While cloud service providers (CSPs) offer a near-infinite function elasticity, these applications often experience fluctuating workloads and stricter performance constraints. A typical CSP strategy is to empirically determine and adjust desired function instances or resources, known asautoscaling, based on monitoring-based thresholds such as CPU or memory, to cope with demand and performance. However, threshold configuration either requires expert knowledge, historical data or a complete view of the environment, making autoscaling a performance bottleneck that lacks an adaptable solution. Reinforcement learning (RL) algorithms are proven to be beneficial in analysing complex cloud environments and result in an adaptable policy that maximizes the expected objectives. Most realistic cloud environments usually involve operational interference and have limited visibility, making them partially observable. A general solution to tackle observability in highly dynamic settings is to integrate Recurrent units with model-free RL algorithms and model a decision process as a Partially Observable Markov Decision Process (POMDP). Therefore, in this paper, we investigate model-free Recurrent RL agents for function autoscaling and compare them against the model-free Proximal Policy Optimisation (PPO) algorithm. We explore the integration of aLong-Short Term Memory(LSTM) network with the state-of-the-art PPO algorithm to find that under our experimental and evaluation settings, recurrent policies were able to capture the environment parameters and show promising results for function autoscaling. We further compare a PPO-based autoscaling agent with commercially used threshold-based function autoscaling and posit that a LSTM-based autoscaling agent is able to improve throughput by 18%, function execution by 13% and account for 8.4% more function instances.

Microservices have transformed monolithic applications into lightweight, self-contained, and isolated application components, establishing themselves as a dominant paradigm for application development and deployment in public clouds such as Google and Alibaba. Autoscaling emerges as an efficient strategy for managing resources allocated to microservices’ replicas. However, the dynamic and intricate dependencies within microservice chains present challenges to the effective management of scaled microservices. Additionally, the centralized autoscaling approach can encounter scalability issues, especially in the management of large-scale microservice-based clusters. To address these challenges and enhance scalability, we propose an innovative distributed resource provisioning approach for microservices based on the Twin Delayed Deep Deterministic Policy Gradient algorithm. This approach enables effective autoscaling decisions and decentralizes responsibilities from a central node to distributed nodes. Comparative results with state-of-the-art approaches, obtained from a realistic testbed and traces, indicate that our approach reduces the average response time by 15% and the number of failed requests by 24%, validating improved scalability as the number of requests increases.

Fog and Edge computing extend cloud services to the proximity of end users, allowing many Internet of Things (IoT) use cases, particularly latency-critical applications. Smart devices, such as traffic and surveillance cameras, often do not have sufficient resources to process computation-intensive and latency-critical services. Hence, the constituent parts of services can be offloaded to nearby Edge/Fog resources for processing and storage. However, making offloading decisions for complex services in highly stochastic and dynamic environments is an important, yet difficult task. Recently, Deep Reinforcement Learning (DRL) has been used in many complex service offloading problems; however, existing techniques are most suitable for centralized environments, and their convergence to the best-suitable solutions is slow. In addition, constituent parts of services often have predefined data dependencies and quality of service constraints, which further intensify the complexity of service offloading. To solve these issues, we propose a distributed DRL technique following the actor-critic architecture based on Asynchronous Proximal Policy Optimization (APPO) to achieve efficient and diverse distributed experience trajectory generation. Also, we employ PPO clipping and V-trace techniques for off-policy correction for faster convergence to the most suitable service offloading solutions. The results obtained demonstrate that our technique converges quickly, offers high scalability and adaptability, and outperforms its counterparts by improving the execution time of heterogeneous services.

Elastic scaling of parallel operators has emerged as a powerful approach to reduce response time in stream applications with fluctuating inputs. Many state-of-the-art works focus on stateless operators and change the operator parallelism from one aspect. They often lack efficient management of operator states and overlook the costs associated with resource over-provisioning. To overcome these limitations, we introduce Es-Stream for elastic scaling of stateful operators over fluctuating data streams, which includes: 1) We observe that under-provisioning of operator parallelism leads to data pile-up, resulting in longer system latency, while over-provisioning of operator parallelism causes idle instances and additional resource consumption. 2) The Es-Stream system scales in two dimensions: the parallelism of operators and the number of resources. It dynamically adjusts operators to an optimal parallelism while scaling the resources used by the stream application. 3) When the parallelism of stateful operators changes, upstream operators backup downstream operators’ state and cache the emitted data tuples at dynamic time intervals, ensuring the operator parallelism is adjusted in a low-overhead way. 4) Experimental results demonstrate that Es-Stream provides promising performance improvements, reducing the maximum system latency by 3x and saving the maximum state recovery time by 2x, compared to existing state-of-the-art works.

Cloud computing has become an important driving force in the economy and the fundamental facility for digitization transformation. Due to its rapid development and increasing demands, accurate resource usage prediction for the cloud has been a long-term challenge. To address this challenge, this paper introduces RCorrPolicy for resource metrics selection and proposes EN-Beats, an efficient ensemble learning-based approach, for predicting multiple resource usages in the cloud. The paper presents trace-driven experiments conducted on a real-world dataset, demonstrating notable improvements in predicting multiple resource metrics. Ablation experiments conducted on existing methods for RCorrPolicy indicate that the proposed policy enhances the performance of these methods across different evaluated metrics. Furthermore, EN-Beats outperforms existing methods by achieving the lowest NRMSE (lower values indicate better performance) for CPU util rate (up to 8% lower), memory usage (up to 6% lower), and network incoming traffic (up to 3% lower). Additionally, EN-Beats attains the highest R2 score (higher values indicate better performance) for predicting CPU util rate (up to 1.39 higher), memory usage (up to 0.57 higher), and network incoming traffic (up to 0.62 higher).

The Internet of Things (IoT) driven latency-critical applications are deployed on lightweight Micro-Clouds at the network's edge. Renting physical space from geographically distributed colocation datacenters connected via a Wide Area Network (WAN) is a cost-effective way of deploying Micro-Clouds, despite WANs' dynamic communication latency from traffic congestion. However, this deployment approach can limit Micro-Clouds to operate within a soft power budget, as colocation datacenter providers utilize it to add more servers and lower capital costs through oversubscribing power infrastructure. As a result, Micro-Clouds use extreme energy reduction measures like power capping and task throttling to address power overdraw events, where power consumption exceeds soft power budget limits, which reduces the performance of latency-critical applications. We propose a solution where a dynamic power budget can be achieved by adding renewable energy sources to the existing soft power budget without upgrading power delivery systems. To take advantage of this, we propose a dynamic, decentralized task-scheduling algorithm called DEMOTS. DEMOTS effectively utilizes the available dynamic power budget in a WAN with varying degrees of network traffic congestion, thereby avoiding the need for extreme energy reduction measures. We implement DEMOTS on a simulation test-bed. Compared to state-of-the-art baseline using MCOP for decentralized task-scheduling in Micro-Clouds, DEMOTS reduces Power Overdraw Impact up to 19%, Task Latency Increase Impact up to 47 %, and Task Schedule Time Impact up to 49%.

Efficiently scheduling jobs in clouds is critical for job performance, system throughput and resource utilization. The growing importance of parallel applications in clouds introduces challenges in scheduling data-parallel jobs. Production data-parallel jobs increasingly have complex dependency structure, i.e., complex task dependencies expressed as directed acyclic graphs (DAGs), and heterogeneous resource demands. NP-hard problems are introduced by relaxing either of these challenges (i.e., scheduling of homogeneous tasks with dependency constraints or independent and heterogeneous tasks) for scheduler design. It is challenging to design a scheduler for simultaneously achieving low latency and high resource utilization due to the complex dependency structure and job heterogeneity. In this paper, we propose Sailfish, a dependency-aware and resource efficient scheduling for low latency in clouds. Sailfish first uses the machine learning algorithm to classify jobs into two categories (high priority jobs and low priority jobs) based on the extracted features. Next, Sailfish splits the jobs into tasks and distributes the tasks to the master nodes based on the dependency of tasks and the load of master nodes. Then, Sailfish utilizes the dependency information of tasks to determine tasks’ priority, and packs tasks by leveraging the complementary of tasks’ requirements on different resource types and task dependency. Finally, the master nodes leverage the proposed mutual reinforcement algorithm to distribute tasks to workers in the system based on the resource demands of tasks, the available resources of workers and task dependency. Extensive experimental results based on a real cluster and experiments using real-world Amazon EC2 cloud service show that Sailfish can improve the average resource utilization (by up to 40%) and reduce the latency (the average job completion time) significantly (by up to 91%) compared to the existing schedulers.

Emerging applications in healthcare, autonomous vehicles, and wearable assistance require interactive and low-latency data analysis services. Unfortunately, cloud-centric architectures cannot fulfill the low-latency demands of these applications, as user devices are often distant from cloud data centers. Edge computing aims to reduce the latency by enabling processing tasks to be offloaded to resources located at the network’s edge. However, determining which tasks must be offloaded to edge servers to reduce the latency of application requests is not trivial, especially if the tasks present dependencies. This paper proposes a Deep Reinforcement Learning (DRL) approach called TPTO, which leverages Transformer Networks and Proximal Policy Optimization (PPO) to offload dependent tasks of IoT applications in edge computing. We consider users with various preferences, where devices can offload computation to an edge server via wireless channels. Performance evaluation results demonstrate that under fat application graphs, TPTO is more effective than state-of-the-art methods, such as Greedy, HEFT, and MRLCO, by reducing latency by 30.24%, 29.61%, and 12.41%, respectively. In addition, TPTO presents a training time approximately 2.5 times faster than an existing DRL approach.

In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The frontiers of these computing technologies have been boosted by shift from manually encoded algorithms to Artificial Intelligence (AI)-driven autonomous systems for optimum and reliable management of distributed computing resources. Prior work focuses on improving existing systems using AI across a wide range of domains, such as efficient resource provisioning, application deployment, task placement, and service management. This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality of Service (QoS) in the presence of a continuum of resources. We present the latest trends and impact areas such as optimizing AI models that are deployed on or for computing systems. We layout a roadmap for future research directions in areas such as resource management for QoS optimization and service reliability. Finally, we discuss blue-sky ideas and envision this work as an anchor point for future research on AI-driven computing systems.

The efficient cloud resource provisioning for the execution of complex workflow applications has always been one of the important research issues. Most of the existing approaches focus on the resource provisioning of single-type virtual machine (VM) instances for the single or multiple workflows, while few consider the situation of provisioning multi-type VM instances simultaneously. As a result, the executing performance of complex workflows degrades. Different from the existing work, this paper proposes an adaptive cloud bundle provisioning and multi-workflow scheduling model to dynamically perform both the horizontal and vertical cloud resource scaling on multi-type VM instances for the execution of complex workflows. Among the model, a depth-first-search coalition reinforcement learning (DFSCRL) provisioning policy is presented to realize the resource scaling, which integrates the physical machine (PM) coalition formation with the Q-learning algorithm, then dynamically generates an optimal multi-type VM instance bundle from the PM coalition, and finally provisions these instances to the concurrent execution of multiple workflows. The theoretical proofs and various experiments with the multifaceted metrics demonstrate that the performance of the proposed algorithms is superior to that of the state-of-the-art relevant policies.

BCube is a well-known recursively defined network structure. It provides multiple low-diameter paths and good fault-tolerance for data center networks (DCNs). Its distributed routing algorithm, BCube Source Routing (BSR), can be deployed rapidly and conveniently to build multiple parallel path sets. But in the worst case, BSR may suffer from flow collisions and waste$50\%$of the capacity of each BCube link. In this paper, to decrease collisions and improve bandwidth utilization, we supplement the BCube topology with a central master computer and design two centralized dynamic parallel flow scheduling algorithms: CDPFS and CDPFSMP, for single-path and multi-path respectively. We focus on finding the least congested path for each flow by analyzing the information about the state of the global network. Furthermore, we allocate those paths to each flow in parallel. The simulation result shows that our proposed algorithms take advantage of BCube structure and deliver high-performance solutions for load balancing problems, which have improved 44.1% of the throughput in random bijective traffic pattern and 36.2% of throughput in data shuffle compared with BSR algorithm.

Fog/Edge computing is a novel computing paradigm supporting resource-constrained Internet of Things (IoT) devices by placement of their tasks on edge and/or cloud servers. Recently, several Deep Reinforcement Learning (DRL)-based placement techniques have been proposed in fog/edge computing environments, which are only suitable for centralized setups. The training of well-performed DRL agents requires manifold training data while obtaining training data is costly. Hence, these centralized DRL-based techniques lack generalizability and quick adaptability, thus failing to efficiently tackle application placement problems. Moreover, many IoT applications are modeled as Directed Acyclic Graphs (DAGs) with diverse topologies. Satisfying dependencies of DAG-based IoT applications incur additional constraints and increase the complexity of placement problem. To overcome these challenges, we propose an actor-critic-based distributed application placement technique, working based on the IMPortance weighted Actor-Learner Architectures (IMPALA). IMPALA is known for efficient distributed experience trajectory generation that significantly reduces exploration costs of agents. Besides, it uses an adaptive off-policy correction method for faster convergence to optimal solutions. Our technique uses recurrent layers to capture temporal behaviors of input data and a replay buffer to improve the sample efficiency. The performance results, obtained from simulation and testbed experiments, demonstrate that our technique significantly improves execution cost of IoT applications up to 30% compared to its counterparts.

Renewable energy supply is a promising solution for datacenters' increasing electricity monetary cost, energy consumption and harmful gas emissions. However, due to the instability of renewable energy, insufficient renewable energy supply may lead to the use of stored energy or brown energy. To handle this problem, in this paper, we propose an instability-resilient renewable energy allocation system. We define a job's service-level-objective (SLO) as the successful running probability by only using supplied renewable energy. The system allocates jobs with the same SLO level to the same physical machine (PM) group, and powers each PM group with renewable energy generators that have probability no less than its SLO to produce the amount no less than its energy demand. We use a deep learning technique to predict the probability of producing the amount no less than each value of each renewable energy source, and predict the energy demands of each PM area. We formulate an optimization problem to match renewable energy resources with different instabilities to different PM groups for supply, and use reinforcement learning method and linear programming method to solve it. We further propose an energy-driven computing resource assignment method, which adjusts the amount of computing resource of each job based on job deadline and failure probability in each PM group, and a failure prediction based energy saving method. Real trace driven experiments show that our methods achieve much lower SLO violations, total energy monetary cost and total carbon emission compared to other methods and the effectiveness of individual methods.

Given the changing workloads from the tenants, it is not uncommon for a service composition running in the multi-tenant SaaS cloud to encounter under-utilization and over-utilization on the component services. Both cases are undesirable and it is therefore nature to mitigate them by recomposing the services to a newly optimized composition plan once they have been detected. However, this ignores the fact that under-/over-utilization can be merely caused by temporary effects, and thus the advantages may be short-term, which hinders the long-term benefits that could have been created by the original composition plan, while generating unnecessary overhead and disturbance via recomposition. In this article, we proposeDebtCom, a framework that determines whether to trigger recomposition based on the technical debt metaphor and time-series prediction of workload. In particular, we propose a service debt model, which has been explicitly designed for the context of service composition, to quantify the debt. Our core idea is that recomposition can be unnecessary if the under-/over-utilization only cause temporarily negative effects, and the current composition plan, although carries debt, can generate greater benefit in the long-term. We evaluateDebtComon a large scale service system with up to 10 abstract services, each of which has 100 component services, under real-world dataset and workload traces. The results confirm that, in contrast to the state-of-the-art,DebtComachieves better utility while having lower cost and number of recompositions, rendering each composition plan more sustainable.

A common performance problem in large-scale cloud systems is dealing with straggler tasks that are slow running instances which increase the overall response time. Such tasks impact the system's QoS and the SLA. There is a need for automatic straggler detection and mitigation mechanisms that execute jobs without violating the SLA. Prior work typically builds reactive models that focus first on detection and then mitigation of straggler tasks, which leads to delays. Other works use prediction based proactive mechanisms, but ignore volatile task characteristics. We propose a Straggler Prediction and Mitigation Technique (START) that is able to predict which tasks might be stragglers and dynamically adapt scheduling to achieve lower response times. START analyzes all tasks and hosts based on compute and network resource consumption using an Encoder LSTM network to predict and mitigate expected straggler tasks. This reduces the SLA violation rate and execution time without compromising QoS. Specifically, we use the CloudSim toolkit to simulate START and compare it with IGRU-SD, SGC, Dolly, GRASS, NearestFit and Wrangler in terms of QoS parameters. Experiments show that START reduces execution time, resource contention, energy and SLA violations by 13%, 11%, 16%, 19%, compared to the state-of-the-art.

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users’ costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this article, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users’ costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is$\sim$9.61% more energy and$\sim$20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users’ costs.

On behalf of the Technical Program Committee, we welcome you to the International Conference on Computer Communications and Networks (ICCCN), 2022. This year we celebrate the 31st anniversary of the conference, and continuing its tradition, we aspired in delivering an exciting and of high quality technical program, while aiming to bring together researchers, designers, and implementers of all aspects of computer communications and networks.

Vehicular Ad Hoc Networks (VANET) is an emerging technology that enables a comfortable, safe, and efficient travel experience by providing mechanisms to execute applications related to traffic congestions, road accidents, autonomous driving, and entertainment. The mobile vehicles in VANET are characterized by low computational and storage capabilities. In such scenarios, to meet applications’ performance requirements, requests from vehicles are offloaded to edge and cloud servers. The high energy consumption of these servers increases operating costs and threatens the environment. Energy-aware offloading strategies have been introduced to tackle this problem. Existing works on computation offloading focus on optimizing the energy consumption of either the IoT devices/mobile/vehicles and/or the edge servers. This paper proposes a novel offloading algorithm that optimizes the energy of edge–cloud integrated computing platforms based on Evolutionary Genetic Algorithm (EGA) while maintaining applications’ Service Level Agreement (SLA). The proposed algorithm employs an adaptive penalty function to incorporate the optimization constraints within EGA. Comparative analysis and numerical experiments are carried out between the proposed algorithm, random and genetic algorithm-based offloading, and no offloading baseline approaches. On average, the results show that the proposed algorithm saves 2.97 times and 1.37 times more energy than the random and no offloading algorithms respectively. Our algorithm has 0.3% of violations versus 52.8% and 62.8% by the random and no offloading approaches respectively. While the energy-non-SLA-aware genetic algorithm saves, on average, 1.22 times more energy than our approach, however, it violates SLAs by 159 times more than our proposed approach.

The rapid development and deployment of Internet of Things (IoT) devices in modern networks and Industry 4.0 have attracted substantial interest from cybersecurity researchers. In this study, we propose a software-defined framework that improves network intrusion detection systems by using manufacturer usage description (MUD) to enhance the behavioral monitoring in IoT networks. We aim to explore whether Industrial IoT (IIoT) devices typically serve a common role in cyber–physical systems, and their communications exhibit predictable patterns that can be defined in MUD profile(s) formally and succinctly. We design a framework that utilizes the concept of digital twins and software-defined networking to improve the security of IIoT environments. The MUD data are profiled, and the actions are evaluated on the network digital twin before they are used in the physical network. The behavioral profiling system is updated in real time, thereby improving the overall system security and compliance to policies in the IoT deployment. Evaluation results show that our solution outperforms existing approaches substantially in terms of attack detection accuracy, predicting security incidents, response time, and resource usage.

Internet of Things (IoT) devices have become an integral part of our lives and are increasingly used in almost every field. Subsequently, there are a large number of latency-sensitive IoT applications (e.g., face recognition and autonomous driving) targeted for mobile edge computing environments. These IoT applications are often split into multiple collaborative tasks and offloaded onto containers or virtual machines (VMs) with certain failure rates and recovery rates. If these containers or VMs are not deployed in the same edge servers, the bandwidth resources of edge clouds must be consumed to transfer data. These factors increase the completion time of IoT applications to different degrees, and then affect their reliability level. Therefore, there exists equilibrium between the reliability level and bandwidth consumption. In this article, we investigate the equilibrium of minimizing the bandwidth consumption of IoT applications while maximizing the reliability level of these IoT applications during task offloading. We propose a multiobjective optimization problem, and transform it to a single-objective optimization problem. Furthermore, we introduce two efficient approaches to acquire two near-optimal solutions. The results of simulation experiments demonstrate that our proposed approaches can observably enhance the reliability level and reduce the bandwidth consumption of IoT applications compared with other related approaches. Meanwhile, we also make a comparative analysis of our proposed approaches.

Abstract Internet of Things (IoT) has a pivotal role in developing intelligent and computational solutions to facilitate varied real‐life applications. To execute high‐end computations and data analytics, IoT and cloud‐based solutions play the most significant role. However, frequent communication with long distant cloud servers is not a delay‐aware and energy‐efficient solution while providing time‐critical applications such as healthcare. This article explores the possibilities and opportunities of integrating cloud technology with fog and edge‐based computing to provide healthcare services to users in exigency. Here, we propose an end‐to‐end framework namedRESCUE(enabling green healthcare services using integrated iot‐edge‐fog‐cloud computing environments), consisting efficient spatio‐temporal data analytics module for efficient information sharing, spatio‐temporal data analysis to predict the path for users to reach the destination (healthcare center or relief camps) with minimum delay in the time of exigency (say, natural disaster). This module analyzes the collected information through crowd‐sourcing and assists the user by extracting optimal path postdisaster when many regions are nonreachable. Our work is different from the existing literature in varied aspects: it analyses the context and semantics by augmenting real‐time volunteered geographical information (VGI) and refines it. Furthermore, the novel path prediction module incorporates such VGI instances and predicts routes in emergencies avoiding all possible risks. Also, the design of development of a latency‐aware, power‐aware data‐driven analytics system helps to resolve any spatio‐temporal query more efficiently compared to the existing works for any time‐critical application. The experimental and simulation results outperform the baselines in terms of accuracy, delay, and power consumption.

Abstract Several global health incidents and evidences show the increasing likelihood of pandemics (large‐scale outbreaks of infectious disease), which has adversely affected all aspects of human lives. It is essential to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data‐sources to deliver insights for enhancing preparedness to combat the pandemic. Specifically, human mobility, travel history, and other transport statistics have significantly impact on the spread of any infectious disease. This article proposes a spatio‐temporal knowledge mining framework, named STOPPAGE, to model the impact of human mobility and other contextual information over the large geographic areas in different temporal scales. The framework has two key modules: (i) spatio‐temporal data and computing infrastructure using fog/edge based architecture; and (ii) spatio‐temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. We created a pandemic‐knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hotspot zones. Further, we provide necessary support in home‐health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real‐life datasets related to COVID‐19 in India illustrate the efficacy of the proposed methods. STOPPAGE outperforms the existing works and baseline methods in terms of accuracy by (18–21)% in predicting hotspots and reduces the power consumption of the smartphone significantly. The scalability study yields that the STOPPAGE framework is flexible enough to analyze a huge amount of spatio‐temporal datasets and reduces the delay in predicting health status compared to the existing studies.

Abstract Quantum computing (QC) is an emerging paradigm with the potential to offer significant computational advantage over conventional classical computing by exploiting quantum‐mechanical principles such as entanglement and superposition. It is anticipated that this computational advantage of QC will help to solve many complex and computationally intractable problems in several application domains such as drug design, data science, clean energy, finance, industrial chemical development, secure communications, and quantum chemistry. In recent years, tremendous progress in both quantum hardware development and quantum software/algorithm has brought QC much closer to reality. Indeed, the demonstration of quantum supremacy marks a significant milestone in the Noisy Intermediate Scale Quantum (NISQ) era—the next logical step being the quantum advantage whereby quantum computers solve a real‐world problem much more efficiently than classical computing. As the quantum devices are expected to steadily scale up in the next few years, quantum decoherence and qubit interconnectivity are two of the major challenges to achieve quantum advantage in the NISQ era. QC is a highly topical and fast‐moving field of research with significant ongoing progress in all facets. A systematic review of the existing literature on QC will be invaluable to understand the state‐of‐the‐art of this emerging field and identify open challenges for the QC community to address in the coming years. This article presents a comprehensive review of QC literature and proposes taxonomy of QC. The proposed taxonomy is used to map various related studies to identify the research gaps. A detailed overview of quantum software tools and technologies, post‐quantum cryptography, and quantum computer hardware development captures the current state‐of‐the‐art in the respective areas. The article identifies and highlights various open challenges and promising future directions for research and innovation in QC.

Abstract Microservices have gained wide recognition and acceptance in software industries as an emerging architectural style for autonomous, scalable and more reliable computing. A critical problem related to microservices is reasoning about the suitable granularity level of a microservice (i.e., when and how to merge or decompose microservices). Although scalability is pronounced as one of the major factors for adoption of microservices, there is a general gap of approaches that systematically analyse the dimensions and metrics, which are important for scalability‐aware granularity adaptation decisions. To the best of our knowledge, the state‐of‐art in reasoning about microservice granularity adaptation is neither: (1) driven by microservice‐specific scalability dimensions and metrics nor (2) follow systematic scalability analysis to make scalability‐aware adaptation decisions. In this article, we address the aforementioned problems using a two‐fold contribution. Firstly, we contribute to a working catalogue of microservice‐specific scalability dimensions and metrics. Secondly, we describe a novel application of scalability goal‐obstacle analysis for the context of reasoning about microservice granularity adaptation. We analyse both contributions by comparing their usage on a hypothetical microservice architecture against ad‐hoc scalability assessment for the same architecture. This analysis shows how both contributions can aid making scalability‐aware granularity adaptation decisions.

Abstract Self‐adaptive security methods have been extensively leveraged for securing software systems and users from runtime threats in online and elastic environments, such as the cloud. The existing solutions treat security as an aggregated quality by enforcing “one service for all” without considering the explicit security requirements of each asset or the costs associated with security. Dealing with the security of assets in ultra‐large environments calls for rethinking the way we select and compose services—considering not only the services but the underlying supporting computational resources in the process. We motivate the need for an asset‐centric, self‐adaptive security framework that selects and allocates services and underlying resources in the cloud. The solution leverages learning algorithms and market‐inspired approaches to dynamically manage changes in the runtime security goals/requirements of assets with the provision of suitable services and resources, while catering for monetary and computational constraints. The proposed framework aims to inform the self‐adaptive security efforts of security researchers and practitioners operating in dynamic large‐scale environments, such as the Cloud. To illustrate the utility of the proposed framework it is evaluated using simulation on an application based scenario, involving cloud‐based storage and security services.

Container orchestration platforms such as Kubernetes and Kubernetes-derived KubeEdge (called Kubernetes-based systems collectively) have been gradually used to conduct unified management of Cloud, Fog, and Edge resources. Container provisioning algorithms are crucial to guaranteeing quality of services (QoS) of such Kubernetes-based systems. However, most existing algorithms focus on placement and migration of fixed number of containers without considering elastic provisioning of containers. Meanwhile, widely used linear-performance-model-based feedback control or fixed-processing-rate-based queuing model on diverse platforms cannot describe the performance of containerized Web systems accurately. Furthermore, a fixed reference point used by existing methods is likely to generate inaccurate output errors incurring great fluctuations encountered with large arrival-rate changes. In this article, a feedback control method is designed based on a combination of varying-processing-rate queuing model and linear-model to provision containers elastically which improves the accuracy of output errors by learning reference models for different arrival rates automatically and mapping output errors from reference models to the queuing model. Our approach is compared with several state-of-art algorithms on a real Kubernetes cluster. Experimental results illustrate that our approach obtains the lowest percentage of service level agreement (SLA) violation (decreasing no less than 8.44 percent) and the second lowest cost.

Big data frameworks such as Apache Spark is becoming prominent to perform large-scale data analytics jobs in various domains. However, due to limited resource availability, the local or on-premise computing resources are often not sufficient to run these jobs. Therefore, public cloud resources can be hired on a pay-per-use basis from the cloud service providers to deploy a Spark cluster entirely on the cloud. Nevertheless, using only cloud resources can be costly. Hence, both local and cloud resources nowadays are used together to deploy a hybrid cloud computing cluster. However, scheduling jobs in a cluster deployed on hybrid clouds is challenging in the presence of various Service-Level Agreement (SLA) demands such as cost minimization and job deadline guarantee. Most of the existing works either consider a public or a locally deployed cluster and mainly focus on improving job performance in the cluster. In this article, we propose efficient scheduling algorithms that leverage from different VM instance pricing in a hybrid cloud deployed cluster to optimize the Virtual Machine (VM) usage cost for both local and cloud resources and maximize the job deadline met percentage. We have conducted extensive simulation-based experiments to compare our proposed algorithms with the baseline approaches. In addition, we have developed a prototype system on top of Apache Mesos cluster manager and performed real experiments to evaluate the applicability of our proposed approaches in a real platform with benchmark applications. The results show that our proposed algorithms are highly scalable and reduce the cost of VM usage of a hybrid cluster for up to 20 percent.

The elasticity of cloud resources allows cloud clients to expand and shrink their demand for resources dynamically over time. However, fluctuations in the resource demands and pre-defined size of virtual machines (VMs) lead to lack of resource utilization, load imbalance, and excessive power consumption. To address these issues and to improve the performance of data center, an efficient resource management framework is proposed, which anticipates resource utilization of the servers and balances the load accordingly. It facilitates power saving, by minimizing the number of active servers, VM migrations, and maximizing the resource utilization. An online resource prediction system, is developed and deployed at each VM to minimize the risk of Service Level Agreement (SLA) violations and performance degradation due to under/overloaded servers. In addition, multi-objective VM placement and migration algorithms are proposed to reduce the network traffic and power consumption within data center. The proposed framework is evaluated by executing experiments on three real world workload datasets namely, Google Cluster dataset, Planet Lab, and Bitbrains VM traces. The comparison of proposed framework with the state-of-the-art approaches reveals its superiority in terms of different performance metrics. The improvement in power saving achieved by OP-MLB framework is upto 85.3 percent over the Best-Fit approach.

As data of COVID-19 patients is increasing, the new framework is required to secure the data collected from various Internet of Things (IoT) devices and predict the trend of disease to reduce its spreading. This article proposes security- and privacy-based lightweight framework called iFaaSBus, which uses the concept of IoT, machine learning (ML), and function as a service (FaaS) or serverless computing to diagnose the COVID-19 disease and manages resources automatically to enable dynamic scalability. iFaaSBus offers OAuth-2.0 Authorization protocol-based privacy and JSON Web Token & Transport Layer Socket protocol-based security to secure the patient's health data. iFaaSBus outperforms response time compared to nonserverless computing while responding to up to 1100 concurrent requests. Further, the performance of various ML models is evaluated based on accuracy, precision, recall, F-score, and area under the curve (AUC) values, and the K-nearest neighbor model gives the highest accuracy rate of 97.51%.

To ensure the proper functioning and performance of Industrial grade Internet of Things devices (IIoT) in Industry 4.0 networks, it is critical to identify the capabilities and malfunctions of their component devices (e.g., sensors, actuators, and controllers) and detect potential misbehavior arising due to cyber-attacks, and misconfiguration. We envision future IoT devices embed behavioral profiles throughSecurity-by-Contract(S×C) that are easy to validate and verify against network security policies; manufacturers to provide manufacturer usage description (MUD) profiles as amanifestfor the devices to signal to the network what sort of access and network functionality they require to properly function. We design authentication in the IoT onboarding process, employ blockchains to a verifiable and immutable repository to store this network manifests, that is signed and verifiable with S×C basedsmart contractsby the device manufacturer, or industry authority. The integrated framework combines blockchains and S×C security contracts, MUD-based behavioral fingerprinting, and software-defined-networking for managing the security of IIoT ecosystems. Finally, the proposed scheme is validated in a simulated IoT environment on various performance parameters.

The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources. Efficient scheduling of application tasks in such environments is challenging due to constrained resource capabilities, mobility factors in IoT, resource heterogeneity, network hierarchy, and stochastic behaviors. Existing heuristics and Reinforcement Learning based approaches lack generalizability and quick adaptability, thus failing to tackle this problem optimally. They are also unable to utilize the temporal workload patterns and are suitable only for centralized setups. However, asynchronous-advantage-actor-critic (A3C) learning is known to quickly adapt to dynamic scenarios with less data and residual recurrent neural network (R2N2) to quickly update model parameters. Thus, we propose an A3C based real-time scheduler for stochastic Edge-Cloud environments allowing decentralized learning, concurrently across multiple agents. We use the R2N2 architecture to capture a large number of host and task parameters together with temporal patterns to provide efficient scheduling decisions. The proposed model is adaptive and able to tune different hyper-parameters based on the application requirements. We explicate our choice of hyper-parameters through sensitivity analysis. The experiments conducted on real-world data set show a significant improvement in terms of energy consumption, response time, Service-Level-Agreement and running cost by 14.4, 7.74, 31.9, and 4.64 percent, respectively when compared to the state-of-the-art algorithms.

By integrating Software-Defined Networking and cloud computing, virtualized networking and computing resources can be dynamically reallocated through live migration of Virtual Machines (VMs). Dynamic resource management such as load balancing and energy-saving policies can request multiple migrations when the algorithms are triggered periodically. There exist notable research efforts in dynamic resource management that alleviate single migration overheads, such as single migration time and co-location interference while selecting the potential VMs and migration destinations. However, by neglecting the resource dependency among potential migration requests, the existing solutions of dynamic resource management can result in the Quality of Service (QoS) degradation and Service Level Agreement (SLA) violations during the migration schedule. Therefore, it is essential to integrate both single and multiple migration overheads into VM reallocation planning. In this paper, we propose a concurrency-aware multiple migration selector that operates based on the maximal cliques and independent sets of the resource dependency graph of multiple migration requests. Our proposed method can be integrated with existing dynamic resource management policies. The experimental results demonstrate that our solution efficiently minimizes migration interference and shortens the convergence time of reallocation by maximizing the multiple migration performance while achieving the objective of dynamic resource management.

Big data frameworks such as Spark and Hadoop are widely adopted to run analytics jobs in both research and industry. Cloud offers affordable compute resources which are easier to manage. Hence, many organizations are shifting towards a cloud deployment of their big data computing clusters. However, job scheduling is a complex problem in the presence of various Service Level Agreement (SLA) objectives such as monetary cost reduction, and job performance improvement. Most of the existing research does not address multiple objectives together and fail to capture the inherent cluster and workload characteristics. In this article, we formulate the job scheduling problem of a cloud-deployed Spark cluster and propose a novel Reinforcement Learning (RL) model to accommodate the SLA objectives. We develop the RL cluster environment and implement two Deep Reinforce Learning (DRL) based schedulers in TF-Agents framework. The proposed DRL-based scheduling agents work at a fine-grained level to place the executors of jobs while leveraging the pricing model of cloud VM instances. In addition, the DRL-based agents can also learn the inherent characteristics of different types of jobs to find a proper placement to reduce both the total cluster VM usage cost and the average job duration. The results show that the proposed DRL-based algorithms can reduce the VM usage cost up to 30%.

Functions provided by Web applications are increasingly diverse which make their structures complicated and meshed. Cloud computing platforms provide elastic computing capacities for these meshed Web systems to guarantee Service Level Agreement (SLA). Though workloads of meshed Web systems usually change steadily and periodically in total, sometimes there are sudden fluctuations. In this paper, a hybrid State-space-model-and-Queuing-network based Feedback control method (SQF) is developed for auto-scaling Virtual Machines (VMs) allocated to each tier of meshed Web systems. For the case with workloads changing steadily, a State-space-model based static Feedback Control method (SFC) is proposed in SQF to stabilize request response times near the reference time. For unsteadily changing workloads, a Queuing-network based multi-tier collaborative Feedback Control method (QFC) is proposed for effectively eliminating bottlenecks. QFC builds a control system for each tier individually and uses the queuing network to measure the interaction relationships among different tiers. Experimental results show that QFC is able to improve the efficiency of eliminating bottlenecks (decreasing upper-limit SLA violation ratios by 31.99%$\sim$56.52%) with similar or a little bit high VM rental costs compared to existing methods while SFC obtains more stable response times for requests with reasonable additional costs.

With the enormous growth of wireless technology, and location acquisition techniques, a huge amount of spatio-temporal traces are being accumulated. This dataset facilitates varied location-aware services and helps to take real-life decisions. Efficiently handling and processing spatio-temporal queries are necessary to respond in real-time. Processing the vast spatio-temporal data requires scalable computing infrastructure. In this regard, an efficient query resolution system can be deployed if we predict the infrastructure requirement of the user query apriori along with the identification of the geospatial service chain. In this work, we propose a framework, namelyLYRIC(deadLine and budget aware spatio-temporal querYpRocessingInCloud), where the spatio-temporal queries are resolved efficiently considering user-defined deadline and budget constraint. Our framework shows high deadline completion accuracy in the range of 1.0 - 0.937, which is more accurate than SparkGIS, GeoSpark, GeoMesa and JUST. This also reduces the resource prediction error by 11 percent, considering the geospatial service chain than without it. The cost of the spatio-temporal query is reduced by$\approx$23% in LYRIC, further, the simulation study (using CloudSim) illustrates the efficacy and scalability of LYRIC in terms of optimal budget usage and execution time compared to four baseline approaches.

Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud, due to long distances. Fog/edge computing is a new model for analysing and acting on time-sensitive data, adjacent to where it is produced. Further, cloud services provided by large companies such as Google, can also be localised to improve response time and service agility. This is accomplished through deploying small-scale datacentres in various locations, where needed in proximity of users; and connected to a centralised cloud that establish a multi-access edge computing (MEC). The MEC setup involves three parties, i.e., service providers (IaaS), application providers (SaaS), network providers (NaaS); which might have different goals, therefore, making resource management difficult. Unlike existing literature, we consider resource management with respect to all parties; and suggest game-theoretic resource management techniques to minimise infrastructure energy consumption and costs while ensuring applications’ performance. Our empirical evaluation, using Google’s workload traces, suggests that our approach could reduce up to 11.95 percent energy consumption, and$\sim$17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up to 20.27 percent energy bills and NaaS can increase their costs-savings up to 18.52 percent as compared to other methods.

The use of cloud computing for delivering application services over the Internet has gained rapid traction. Since the beginning of the COVID-19 global pandemic, the work from home scheme and increased business presence online have created more demand for computing resources. Many enterprises and organizations are expanding their private data centres and utilizing hybrid or multi-cloud environments for their IT infrastructure. Because of the ever-increasing demand for computing resources, energy consumption and carbon emission have become a pressing issue. Renewable energy sources have been recognized as clean and sustainable alternatives to fossil-fuel based brown energy. However, due to the intermittent nature of availability of renewable energy sources, it brings many challenges to automatically and efficiently schedule tasks under renewable energy constraints and deadlines. Task scheduling with traditional heuristic algorithms are not able to adapt quickly with changing energy availability and stochastic task arrival. In this regard, this work aims at building a novel scheduling policy with deep reinforcement learning, which automatically applies scheduling techniques like workload shifting and cloud -bursting in a geographically distributed hybrid multi-cloud environment consists of multiple private and public clouds. Our primary goals are maximizing renewable energy utilization and avoiding deadline constraint violations. We also introduce user configurable hyper-parameters to enable multi-objective scheduling on cloud cost, makespan and utilization. Our experiment results show that the proposed scheduling approach can achieve the aforementioned objectives dynamically to varying renewable energy availability.

Serverless computing is an event-driven cloud computing architecture for processing requests on-demand, using light weight function containers and a micro-services model. A variety of applications like Internet of Things (IoT) services, edge computing, and stream processing have been introduced to the serverless paradigm. These applications are characterized by their stringent response time requirements, therefore expecting a quick and fault tolerant feedback from the application. The serverless, or Function-as-a-Service (FaaS), paradigm suffers from function `cold start' challenges, where the serverless platform takes time to set up the dependencies, prepare the runtime environment and code for execution before serving the incoming workload. Most of the current works address the problem of cold start by (1) reducing the start-up or preparation time of function containers, or (2) reducing the frequency of function cold starts on the platform. Recent industrial research has identified that factors such as runtime environment, CPU and memory settings, invocation concurrency, and networking requirements, affect the cold start of a function. Therefore, we propose a Reinforcement Learning (Q-Learning) agent setting, to analyze the identified factors such as function CPU utilization, to ascertain the function-invocation patterns and reduce the function cold start frequency by preparing the function instances in advance. The proposed Q-Learning agent interacts with the Kubeless serverless platform by discretizing the environment states, actions and rewards with the use of per-instance CPU utilization, available function instances and success or failure rate of response, respectively. The workload is replicated using the Apache JMeter non-GUI toolkit and our agent is evaluated against the baseline default auto-scale feature of Kubeless. The agent demonstrates the capability of learning the invocation pattern, make informed decisions by preparing the optimal number of function instances over the period of learning, under controlled environment settings.

Serverless computing enables rapid application development and deployment by composing loosely coupled microservices at a scale. This emerging paradigm greatly unburdens the users of cloud environments, from the need to provision and manage the underlying cloud resources. With this shift in responsibility, the cloud provider faces the challenge of providing acceptable performance to the user without compromising on reliability, while having minimal knowledge of the application requirements. Sub-optimal resource allocations, specifically the CPU resources, could result in the violation of performance requirements of applications. Further, the fine-grained serverless billing model only charges for resource usage in terms of function execution time. At the same time, the provider has to maintain the underlying infrastructure in always-on mode to facilitate asynchronous function calls. Thus, achieving optimum utilization of cloud resources without compromising on application requirements is of high importance to the provider. Most of the current works only focus on minimizing function execution times caused by delays in infrastructure set up and reducing resource costs for the end-user. However, in this paper, we focus on both the provider and user's perspective and propose a function placement policy and a dynamic resource management policy for applications deployed in serverless computing environments. The policies minimize the resource consumption cost for the service provider while meeting the user's application requirement, i.e., deadline. The proposed solutions are sensitive to deadline and efficiently increase the resource utilization for the provider, while dynamically managing resources to improve function response times. We implement and evaluate our approach through simulation using ContainerCloudSim toolkit. The proposed function placement policy when compared with baseline scheduling techniques can reduce resource consumption by up to three times. The dynamic resource allocation policy when evaluated with a fixed resource allocation policy and a proportional CPU-shares policy shows improvements of up to 25% in meeting the required function deadlines.

Cloud-based enterprise search services (e.g., AWS Kendra) have been entrancing big data owners by offering convenient and real-time search solutions to them. However, the problem is that individuals and organizations possessing confidential big data are hesitant to embrace such services due to valid data privacy concerns. In addition, to offer an intelligent search, these services access the user's search history that further jeopardizes his/her privacy. To overcome the privacy problem, the main idea of this research is to separate the intelligence aspect of the search from its pattern matching aspect. According to this idea, the search intelligence is provided by an on-premises edge tier and the shared cloud tier only serves as an exhaustive pattern matching search utility. We propose Smartness at Edge (SAED mechanism that offers intelligence in the form of semantic and personalized search at the edge tier while maintaining privacy of the search on the cloud tier. At the edge tier, SAED uses a knowledge-based lexical database to expand the query and cover its semantics. SAED personalizes the search via an RNN model that can learn the user's interest. A word embedding model is used to retrieve documents based on their semantic relevance to the search query. SAED is generic and can be plugged into existing enterprise search systems and enable them to offer intelligent and privacy-preserving search without enforcing any change on them. Evaluation results on two enterprise search systems under real settings and verified by human users demonstrate that SAED can improve the relevancy of the retrieved results by on average ≈24% for plain-text and ≈75% for encrypted generic datasets.

Fog/Edge computing model allows harnessing of resources in the proximity of the Internet of Things (IoT) devices to support various types of latency-sensitive IoT applications.However, due to the mobility of users and a wide range of IoT applications with different resource requirements, it is a challenging issue to satisfy these applications' requirements.The execution of IoT applications exclusively on one fog/edge server may not be always feasible due to limited resources, while the execution of IoT applications on different servers requires further collaboration and management among servers.Moreover, considering user mobility, some modules of each IoT application may require migration to other servers for execution, leading to service interruption and extra execution costs.In this article, we propose a new weighted cost model for hierarchical fog computing environments, in terms of the response time of IoT applications and energy consumption of IoT devices, to minimize the cost of running IoT applications and potential migrations.Besides, a distributed clustering technique is proposed to enable the collaborative execution of tasks, emitted from application modules, among servers.Also, we propose an application placement technique to minimize the overall cost of executing IoT applications on multiple servers in a distributed manner.Furthermore, a distributed migration management technique is proposed for the potential migration of applications' modules to other remote servers as the users move along their path.Besides, failure recovery methods are embedded in the clustering, application placement, and migration management techniques to recover from unpredicted failures.The performance results demonstrate that our technique significantly improves its counterparts in terms of placement deployment time, average execution cost of tasks, the total number of migrations, the total number of interrupted tasks, and cumulative migration cost.

In recent years, several cluster ensemble methods have been developed, but they still have some limitations.They commonly use different clustering algorithms in both stages of the clustering ensemble method, such as the ensemble generation step and the consensus function, resulting in a compatibility issue in terms of working functionality between different clustering algorithms.In addition, in a clustering ensemble method, the accuracy of the final results is a major concern.To deal with it, we propose a novel cluster ensemble method based on a single clustering algorithm (CES).In this method, we iterate a clustering algorithm affinity propagation (AP) ten times in the ensemble generation step to obtain multiple base partitions with a high level of diversity in each iteration due to its nature of producing a random number of clusters.Furthermore, with a few modifications, the same algorithm AP is used to propose a novel consensus function for combining these base partitions into a single partition.The proposed consensus function takes advantage of little side-information in the form of partial labels by using pairwise constraints with AP and number of clusters in a dataset.By employing this information, AP is limited to produce an actual number of cluster centres in a dataset rather than a random number of clusters, which considerably enhanced the accuracy of final outcomes.As a result, CES uses the same clustering functionality in both stages of proposed cluster ensemble method and produces the desired number of clusters in the final partition of a dataset which is significantly improving accuracy when compared to state-of-the-art cluster ensemble methods.Furthermore, as a result of these modifications, the CES outperforms AP in terms of accuracy and execution time.Experiments on real-world datasets from various sources show that CES improves accuracy by 5% on average compared to state-of-the-art cluster ensemble methods and by 55.54% compared to AP while consuming 44.60% less execution time.

Summary Containers are gaining popularity over virtual machines as they provide the advantages of virtualization with the performance of near bare metal. The uniformity of support provided by Docker containers across different cloud providers makes them a popular choice for developers. Evolution of microservice architecture allows complex applications to be structured into independent modular components making them easier to manage. High‐performance computing (HPC) applications are one such application to be deployed as microservices, placing significant resource requirements on the container framework. However, there is a possibility of interference between different microservices hosted within the same container (intracontainer) and different containers (intercontainer) on the same physical host. In this paper, we describe an extensive experimental investigation to determine the performance evaluation of Docker containers executing heterogeneous HPC microservices. We are particularly concerned with how intracontainer and intercontainer interference influences the performance. Moreover, we investigate the performance variations in Docker containers when control groups (cgroups) are used for resource limitation. For ease of presentation and reproducibility, we use Cloud Evaluation Experiment Methodology (CEEM) to conduct our comprehensive set of experiments. We expect that the results of evaluation can be used in understanding the behavior of HPC microservices in the interfering containerized environment.

Abstract Rapid increase in energy consumption is a serious problem in cloud storage systems. Data accessed in large‐scale storage systems usually exhibit temporal and spatial characteristics, which make it possible to reduce energy consumption by clustering data with similar access characteristics for storage in the same zone of cloud storage systems. Existing works usually only focus on the frequency of data access. However, widely existing phenomena show data access with seasonal and tidal characteristics in cloud storage systems. The seasonal and tidal characteristics of data access are extracted thoroughly in this paper. According to the extracted data access characteristics, energy‐aware data clustering through a machine learning algorithm (K‐ear) is proposed. K‐ear classifies data into five seasonal categories according to their seasonal access characteristics and then classifies every seasonal category into three tidal categories according to its tidal access characteristics. The 15 classified categories are stored in different storage zones with different energy and performance modes. Simulation experiments using CloudSimDisk with the constructed mathematic models demonstrate that the proposed K‐ear algorithm is more energy‐efficient than the default data clustering algorithms in Hadoop and the classical data clustering storage strategy according to the data access frequency (Striping‐Based Energy‐Aware Strategy).

Fog computing (FC) has emerged as a complementary solution to the centralized cloud infrastructure. An FC node is available in closer proximity to users and extends cloud services to the edge of the network in a highly distributed manner. However, with an increase in streaming and delay-sensitive Internet-of-Things (IoT) applications, FC also needs to address the issue of higher latency while forwarding compute-intensive jobs to remote cloud data centers. Hence, there is a need to investigate the use of computational resources at the edge of the network. Volunteer computing (VC) offers a reduction in the cost of maintaining high-performance computing by making use of user-owned underutilized or idle resources, e.g., laptops and desktop computers closer to fog devices. We propose volunteer-supported FC (VSFC), as a computing paradigm, that explores the interplay of these two distributed computing domains to help minimize inherent communication delays of cloud computing, energy consumption, and network usage. To this effect, we have extended the iFogSim toolkit to support VSFC. Extensive simulations show that VSFC outperforms traditional FC-cloud computing by reducing delay by 47.5%, energy by 93%, and network usage by 92% under normal to heavy load conditions.

The Internet of Things (IoT) enables the interconnection of new cyber–physical devices that generate significant traffic of distributed, heterogeneous, and dynamic data at the network edge. Since several IoT applications demand for short response times (e.g., industrial applications, emergency management, real-time systems, and healthcare systems) and, at the same time, rely on resource-constrained devices, the adoption of traditional data mining techniques is neither effective nor efficient. Therefore, conventional data mining techniques need to be adjusted for optimizing response times, energy consumption, and data traffic while still providing adequate accuracy as required by the IoT applications.

Among the novel IT paradigms, cloud computing and the Internet of Things (CloudIoT) are two complementary areas designed to support the creation of smart cities and application services. The CloudIoT not only presents ubiquitous services through IoT nodes but it also provides virtually unlimited resources through services composition. The services composition problem aims to find a set of services among functionally equivalent services with different Quality of Service (QoS) concerning users' constraints. To this aim, previous studies calculate QoS values through service logs without considering the presence of anomalies in the existing QoS values; however, the dynamicity of distributed service environments and communication networks in CloudIoT environments causes anomalies in the QoS values. Therefore, existing approaches fail to model QoS values accurately that leads to service-level agreement (SLA) violation and penalties for service broker. To address this challenge, we propose a scalable anomaly-aware approach (SAIoT) including two main components: the first component models QoS values based on a machine learning anomaly detection technique, to remove the existing abnormal QoS records, and the second component finds a near-optimal composition by using an effective and efficient metaheuristic algorithm. The experimental results based on real-world data sets show that our approach achieves 30.64% of the average improvement in the QoS value of a composite plan with equal or even less price compared to the previous works, such as information theory-based and advertised QoS-based methods.

The cloud-fog-edge hybrid system is the evolution of the traditional centralized cloud computing model. Through the combination of different levels of resources, it is able to handle service requests from terminal users with a lower latency. However, it is accompanied by greater uncertainty, unreliability, and instability due to the decentralization and regionalization of service processing, as well as the unreasonable and unfairness in resource allocation, task scheduling, and coordination, caused by the autonomy of node distribution. Therefore, this paper introduces blockchain technology to construct a trust-enabled interaction framework in a cloud-fog-edge environment, and through a double-chain structure, it improves the reliability and verifiability of task processing without a big management overhead. Furthermore, in order to fully consider the reasonability and load balance in service coordination and task scheduling, Berger’s model and the conception of service justice are introduced to perform reasonable matching of tasks and resources. We have developed a trust-based cloud-fog-edge service simulation system based on iFogsim, and through a large number of experiments, the performance of the proposed model is verified in terms of makespan, scheduling success rate, latency, and user satisfaction with some classical scheduling models.

Abstract The integration and crosscoordination of big data processing and software‐defined networking (SDN) are vital for improving the performance of big data applications. Various approaches for combining big data and SDN have been investigated by both industry and academia. However, empirical evaluations of solutions that combine big data processing and SDN are extremely costly and complicated. To address the problem of effective evaluation of solutions that combine big data processing with SDN, we present a new, self‐contained simulation tool named BigDataSDNSim that enables the modeling and simulation of the big data management system YARN, its related programming models MapReduce, and SDN‐enabled networks in a cloud computing environment. BigDataSDNSim supports cost‐effective and easy to conduct experimentation in a controllable, repeatable, and configurable manner. The article illustrates the simulation accuracy and correctness of BigDataSDNSim by comparing the behavior and results of a real environment that combines big data processing and SDN with an equivalent simulated environment. Finally, the article presents two uses cases of BigDataSDNSim, which exhibit its practicality and features, illustrate the impact of data replication mechanisms of MapReduce in Hadoop YARN, and show the superiority of SDN over traditional networks to improve the performance of MapReduce applications.

I am delighted that our favorite Journal of Software: Practice and Experience (SPE) turns 50 and the year 2021 is its Golden Jubilee year! The founding fathers of SPE Journal, Charles Lang and David Barron, in their editorial for the first issue published in January 1971, noted “the continuing development of the application of computers in all walks of life is held back by the difficulty of producing software”. They also wrote “As applications become more sophisticated and systems get larger, the effects of bad software will be less easy to ignore since the possibility of disastrous errors will increase.” During the last 50 years there are many instances of software errors causing failures of big missions, critical business, and medical services. A Wikipedia list of software bugs (https://en.wikipedia.org/wiki/List_of_software_bugs) shows their serious consequences—either financially or as a threat to human well-being from many application domains including medical, space, telecom, finance, military, transportation, and government administration. Just to share a recent example: A day-long outage in trading at the Australian Securities Exchange (ASX) on Monday November 16, 2020 was traced to “software bug” in their new equity trading platform. Lang and Barron identified seven major factors that have led to this situation, which continue to hold true even after 50 years. I would like to state the last two verbatim: Since its foundation, SPE primarily aimed at addressing these two problems by serving as a vehicle for dissemination of practice and experience of software researchers, developers, engineers, and practitioners to write up and present their work in a form that will be useful to others. Although software practice has greatly improved from then with the help of these continued efforts from SPE and community at large, many challenges yet to be addressed. This unique aim of SPE has drawn me to it and publish my work 25 years ago when I was working on a Microkernel based Operating System for India's PARAM Supercomputers at the Centre for Development of Advanced Computing (C-DAC). When I was invited to join SPE team of Editors in 2014, I was delighted and continue to consider it as my privilege to be part of this visionary and pioneering journal, whose founding year happens to be the same year in which I was born! Right from its first issue, SPE has attracted articles that covered early breakthrough works from prominent researchers and companies on their work on operating systems software, programming languages, data structures, and applications. Let me share four ground breaking works published in the first volume of SPE in 1971: I am pleased to learn that some of the authors who published their early pioneering work in SPE went on to win ACM Turing Award, which is recognized as the highest distinction in Computing Science and Engineering, or the “Nobel Prize of Computing”. During the last 50 years, SPE has attracted and disseminated many innovative software systems and applications revolutionizing the software discipline. I look forward for its significant contribution to software industry and society during the next 50 years. This glories and revolutionary contributions and impact of SPE is made possible by the vision and dedicated efforts of editors, guest editors, authors, reviewers, advisory board members, and journal staff members. It is my humble duty to thank you all for your contributions. I glanced through the past issues of SPE and identified editors, years of their service, and their affiliation at the start of their service, which appears as follows: SPE has inducted the following two colleagues as its editors starting from 2021, I warmly welcome them: I request your continued support in sustaining SPE as the premier forum for publishing new software research and innovation along with practice and experience focus. Your feedback and comments on further improving SPE are highly appreciated.

We are pleased to present a special issue that focuses on state-of-the-art research on Elastic Computing from Edge to the Cloud Environments. Today, a huge amount of data is being generated by the Internet of Things (IoT) devices such as smartphones, sensors, cameras, cars, and robots.1 In order to process the generated data, there exist Big Data platforms (such as Hadoop and Spark). Conventionally, they are deployed in centralized Data Centers, which, however, fall short of addressing time-critical requirements of the applications due to high latency between the Edge, where the data are generated and the Data Centers where they are processed.2 The emerging Edge and Fog computing paradigms promise to solve this problem by seamlessly integrating hardware and software resources across multiple computing tiers, from the Edge to the Data Center/Cloud. Since computing resources at the Edge may be power and capacity constrained, it is necessary to invent new lightweight platforms and techniques that seamlessly interact, sense, execute and produce results with very low latency, while at the same time address other high-level requirements of applications, such as security and privacy. Regarding these problems, there are many challenges that must be addressed with the invention of new architectures, methods, algorithms, and solutions. This special issue features six papers covering a range of topics including IoT application deployment frameworks in edge-cloud environments, cost optimization models, and lightweight virtualization model. The first paper in this special issue titled “Edge-adaptable serverless acceleration for machine learning Internet of Things applications”3 presents STOIC (serverless teleoperable hybrid cloud), an IoT application deployment and offloading system that extends the serverless model. The authors have developed a dynamic feedback control mechanism to precisely predict latency and dispatch workloads uniformly across edge and cloud systems using a distributed serverless framework. STOIC leverages hardware acceleration (e.g., GPU resources) for serverless function execution when available from the underlying cloud system. Finally, it configures the system to overcome deployment variability associated with public clouds. The system is evaluated using real-world machine learning applications and multitier IoT deployments (edge and cloud) and shown that it reduces overall execution time and achieves placement accuracy in the range of 92%–97%. The second paper in this special issue titled “Server Configuration Optimisation in Mobile Edge Computing: A Cost-Performance Tradeoff Perspective”4 studies the problem of server configuration optimization in Mobile edge computing environments. The authors use M/M/m queuing models and establish the performance and cost models for the system. The article considers cost-constrained performance optimization, and performance constrained cost optimization based on multiple numerical algorithms. The numerical simulation-based experiments have shown this approach is able to balance the trade-off between investment cost and service quality. The third paper in this special issue titled “EFFORT: Energy-efficient framework for offload. Communication in mobile cloud computing”5 proposes a task offloading mechanism from mobile to the remote cloud. The author's solution aims to solve the energy consumption of communication-intensive applications from mobile devices such as smartphones. The experimental evaluation is done by implementing a demonstration application in Android mobile OS. The results have shown that the proposed solution reduces the energy consumption of smartphones while executing applications and simultaneously reducing the communication cost. The fourth paper in this special issue titled “Human Microservices: A framework for turning humans into service providers”6 presents a framework facilitating the deployment of Application Programming Interface on companion devices (smartphones and IoT devices). The author's framework proposes a new approach aiming to integrate humans in the IoT loop and facilitate computation units' deployment in the devices that are closer to users, instead of remote clouds. The solution leverages existing standards for the rapid development of applications and improves software quality. Through a detailed case study on monitoring people's activity, the authors developed a software application following framework specification and demonstrated the feasibility of their proposed solution. The fifth paper in this special issue titled “Function delivery network: Extending serverless computing for heterogeneous platforms”7 introduces the extension to Function-as-a-Service model. The authors consider heterogeneous clusters and support heterogeneous functions through a network of distributed heterogeneous target platforms called Function Delivery Network (FDN). The article proposes Function-Delivery-as-a-Service, a service model to deliver the function to the required to be targeted platform. It also ensures that Service Level Objective (SLO) energy efficiency requirements are met when scheduling functions. The new benchmarking system FDNInspecto is implemented which benchmarks the different distributed target platforms. The results have found that functions scheduling on edge reduce energy consumption by 17× without violating the SLO requirements when compared to a high-end target platform. Finally, the last paper of this special issue titled “A Lightweight Virtualisation Model to Enable Edge Computing in Deeply Embedded Systems”8 builds a virtualization model for resource-constrained embedded devices. The existing containers cannot be used in many deeply embedded systems (DES) due to an underlying operating system's resource requirements including storage, memory, and processing power. To mitigate this issue, the authors present the Hellfire hypervisor, a lightweight virtualization model that enables separation and improves the security of IoT applications on DES. The Hellfire simplifies essential components of virtualization including compute, storage, and I/O among others. The experimental results conducted with the coremark benchmark show Hellfire has a small footprint of 23 KB while keeping a low average virtualization overhead of 0.62% for multiple virtual machines execution. To summarize, the articles in this special issue provide new definitions, architectural principles, systems, and approaches to solve the challenges posed by emerging distributed systems such as edge and cloud computing, mainly driven by IoT workloads. They address important problems including lightweight platforms, energy efficiency, reliability and easy to integrate frameworks. We hope that you will find this special issue truly useful and enjoyable. We sincerely thank the editor-in-chief for helping us to organize this special issue. We thank the editorial office staffs for their continuous support. We are also thankful to all the authors for submitting their research works. More importantly, thanks to the reviewers for their valuable contributions in providing thoughtful comments and improving the quality of articles.

In Dynamic Service Composition (DSC), an application can be dynamically composed using web services to achieve its functional and Quality of Services (QoS) goals. DSC is a relatively mature area of research that crosscuts autonomous and services computing. Complex autonomous and self-adaptive computing paradigms (e.g., multi-tenant cloud services, mobile/smart services, services discovery and composition in intelligent environments such as smart cities) have been leveraging DSC to dynamically and adaptively maintain the desired QoS, cost and to stabilize long-lived software systems. While DSC is fundamentally known to be an NP-hard problem, systematic attempts to analyze its scalability have been limited, if not absent, though such analysis is of a paramount importance for their effective, efficient, and stable operations. This article reports on a new application of goal-modeling, providing a systematic technique that can support DSC designers and architects in identifying DSC-relevant characteristics and metrics that can potentially affect the scalability goals of a system. The article then applies the technique to two different approaches for QoS-aware dynamic services composition, where the article describes two detailed exemplars that exemplify its application. The exemplars hope to provide researchers and practitioners with guidance and transferable knowledge in situations where the scalability analysis may not be straightforward. The contributions provide architects and designers for QoS-aware dynamic service composition with the fundamentals for assessing the scalability of their own solutions, along with goal models and a list of application domain characteristics and metrics that might be relevant to other solutions. Our experience has shown that the technique was able to identify in both exemplars application domain characteristics and metrics that had been overlooked in previous scalability analyses of these DSC, some of which indeed limited their scalability. It has also shown that the experiences and knowledge can be transferable: The first exemplar was used as an example to inform and ease the work of applying the technique in the second one, reducing the time to create the model, even for a non-expert.

Cloud datacenters have become a backbone for today’s business and economy, which are the fastest-growing electricity consumers, globally. Numerous studies suggest that$\sim$30% of the US datacenters are comatose and the others are grossly less-utilized, which make it possible to save energy through resource consolidation techniques. However, consolidation comprises migrations that are expensive in terms of energy consumption and performance degradation, which is mostly not accounted for in many existing models, and, possibly, it could be more energy and performance efficient not to consolidate. In this paper, we investigate how migration decisions should be taken so that the migration cost is recovered, as only when migration cost has been recovered and performance is guaranteed, will energy start to be saved. We demonstrate through several experiments, using the Google workload data for 12,583 hosts and approximately one million tasks that belong to three different kinds of workload, how different allocation policies, combined with various migration approaches, will impact on datacenter’s energy and performance efficiencies. Using several plausible assumptions for containerised datacenter set-up, we suggest, that a combination of the proposed energy-performance-aware allocation (Epc-Fu) and migration (Cper) techniques, and migrating relatively long-running containers only, offers for ideal energy and performance efficiencies.

In this paper, we try to design a service mechanism for profit optimizations of both a cloud provider and its multiple users. We consider the problem from a game theoretic perspective and characterize the relationship between the cloud provider and its multiple users as a Stackelberg game, in which the strategies of all users are subject to that of the cloud provider. The cloud provider tries to select and provision appropriate servers and configure a proper request allocation strategy to reduce energy cost while satisfying its cloud users at the same time. We approximate its servers selection space by adding a controlling parameter and configure an optimal request allocation strategy. For each user, we design a utility function which combines the net profit with time efficiency and try to maximize its value under the strategy of the cloud provider. We formulate the competitions among all users as a generalized Nash equilibrium problem (GNEP). We solve the problem by employing variational inequality (VI) theory and prove that there exists a generalized Nash equilibrium solution set for the formulated GNEP. Finally, we propose an iterative algorithm (IA), which characterizes the whole process of our proposed service mechanism. We conduct some numerical calculations to verify our theoretical analyses. The experimental results show that our IA algorithm can benefit both of a cloud provider and its multiple users by configuring proper strategies.

Cloud service providers (CSP) and cloud consumers often need to forecast the cloud price to optimize their business strategy. However, pricing of cloud services is a challenging task due to its services complexity and dynamic nature of the ever-changing environment. Moreover, the cloud pricing based on consumers' willingness to pay (W2P) becomes even more challenging due to the subjectiveness of consumers' experiences and implicit values of some non-marketable features, such as burstable CPU, dedicated server, and cloud data center global footprints. Unfortunately, many existing pricing models often cannot support value-based pricing. In this paper, we propose a novel solution based on value-based pricing, which does not only consider how much does the service cost (or intrinsic values) to a CSP but also how much a customer is willing to pay (or extrinsic values) for the service. We demonstrate that the cloud extrinsic values would not only become one of the competitive advantages for CSPs to lead the cloud market but also increase the profit margin. Our approach is often referred to as a hedonic pricing model. We show that our model can capture the value of non-marketable features. This value is about 43.4 percent on average above the baseline, which is often ignored by many traditional cloud pricing models. We also show that Average Annual Growth Rate (AAGR) of Amazon Web Services' (AWS) is about -20.0 percent per annum between 2008 and 2017, ceteris paribus. In comparison with Moore's law (-50 percent per annum), it is at a far slower pace. We argue this value is Moore's law equivalent in the cloud. The primary goal of this research is to provide a less biased pricing model for cloud decision makers to develop their optimizing investment strategy.

Fog/Edge computing emerges as a novel computing paradigm that harnesses resources in the proximity of the Internet of Things (IoT) devices so that, alongside with the cloud servers, provide services in a timely manner. However, due to the ever-increasing growth of IoT devices with resource-hungry applications, fog/edge servers with limited resources cannot efficiently satisfy the requirements of the IoT applications. Therefore, the application placement in the fog/edge computing environment, in which several distributed fog/edge servers and centralized cloud servers are available, is a challenging issue. In this article, we propose a weighted cost model to minimize the execution time and energy consumption of IoT applications, in a computing environment with multiple IoT devices, multiple fog/edge servers and cloud servers. Besides, a new application placement technique based on the Memetic Algorithm is proposed to make batch application placement decision for concurrent IoT applications. Due to the heterogeneity of IoT applications, we also propose a lightweight pre-scheduling algorithm to maximize the number of parallel tasks for the concurrent execution. The performance results demonstrate that our technique significantly improves the weighted cost of IoT applications up to 65 percent in comparison to its counterparts.

The technologies for real-time multimedia transmission and immersive 3D gaming applications are rapidly emerging, posing challenges in terms of performance, security, authentication, data privacy, and encoding. The communication channel for these multimedia applications must be secure and reliable from network attack vectors and data-contents must employ strong encryption to preserve privacy and confidentiality. Towards delivering secure multimedia application environment for 5G networks, we propose an SDN/NFV (Software-Defined-Networking/Network-Function-Virtualization) framework called STREK , which attempts to deliver highly adaptable Quality-of-Experience (QoE), Security, and Authentication functions for multi-domain Cloud to Edge networks. The STREK architecture consists of a holistic SDNFV dataplane, NFV service-chaining and network slicing, a lightweight adaptable hybrid cipher scheme called TREK, and an open RESTful API for applications to deploy custom policies at runtime for multimedia services. For multi-domain/small-cell deployments, the key-generation scheme is dynamic at flow/session-level, and the handover authentication scheme uses a novel method to exchange security credentials with the Access Points (APs) of neighborhood cells. This scheme is designed to improve authentication function during handover with low overhead, delivering the 5G ultra-low latency requirements. We present the experiments with both software and hardware-based implementations and compare our solution with popular lightweight cryptographic solutions, standard open source software, and SDN-based research proposals for 5G multimedia. In the microbenchmarks, STREK achieves smaller hardware, low overhead, low computation, higher attack resistance, and offers better network performance for multimedia streaming applications. In real-time multimedia use-cases, STREK shows greater level of quality distortion for multimedia contents with minimal encryption bitrate overhead to deliver data confidentiality, immunity to common cryptanalysis, and significant resistance to communication channel attacks, in the context of low-latency 5G networks.

Thermal management in the hyper-scale cloud data centers is a critical problem. Increased host temperature creates hotspots which significantly increases cooling cost and affects reliability. Accurate prediction of host temperature is crucial for managing the resources effectively. Temperature estimation is a non-trivial problem due to thermal variations in the data center. Existing solutions for temperature estimation are inefficient due to their computational complexity and lack of accurate prediction. However, data-driven machine learning methods for temperature prediction is a promising approach. In this regard, we collect and study data from a private cloud and show the presence of thermal variations. We investigate several machine learning models to accurately predict the host temperature. Specifically, we propose a gradient boosting machine learning model for temperature prediction. The experiment results show that our model accurately predicts the temperature with the average RMSE value of 0.05 or an average prediction error of 2.38 °C, which is 6 °C less as compared to an existing theoretical model. In addition, we propose a dynamic scheduling algorithm to minimize the peak temperature of hosts. The results show that our algorithm reduces the peak temperature by 6.5 °C and consumes 34.5 percent less energy as compared to the baseline algorithm.

The virtualization concept and elasticity feature of cloud computing enable users to request resources on-demand and in the pay-as-you-go model. However, the high flexibility of the model makes the on-time resource scaling problem more complex. A variety of techniques such as threshold-based rules, time series analysis, or control theory are utilized to increase the efficiency of dynamic scaling of resources. However, the inherent dynamicity of cloud-hosted applications requires autonomic and adaptable systems that learn from the environment in real-time. Reinforcement Learning (RL) is a paradigm that requires some agents to monitor the surroundings and regularly perform an action based on the observed states. RL has a weakness to handle high dimensional state space problems. Deep-RL models are a recent breakthrough for modeling and learning in complex state space problems. In this article, we propose a Hybrid Anomaly-aware Deep Reinforcement Learning-based Resource Scaling (ADRL) for dynamic scaling of resources in the cloud. ADRL takes advantage of anomaly detection techniques to increase the stability of decision-makers by triggering actions in response to the identified anomalous states in the system. Two levels of global and local decision-makers are introduced to handle the required scaling actions. An extensive set of experiments for different types of anomaly problems shows that ADRL can significantly improve the quality of service with less number of actions and increased stability of the system.

The design principle of composability among Web services is one of the most crucial reasons for the success and popularity of Web services. However, achieving error-free automatic Web service composition is still a challenge. In this paper, we propose a recursive composition based modeling and verification technique for Web service interaction. The application of recursive composition over a Web service with respect to a given set of Web services yields a recursive composition interaction graph (RCIG). In order to capture the requirement specifications of a Web service interaction scenario, we propose recursive composition specification language (RCSL) as a requirement specification language. Further, we employ the proposed RCIG as an interpretation model to interpret the semantics of a RCSL formula. Our verification technique is based on the generation and analysis of all possible interaction patterns. Performance evaluation results, provided in this paper, show that our proposition is implementable for the real world applications. The key advantages of the proposed approach are: (i) it does not require explicit system modeling as in model checking based approaches, (ii) it captures primitive characteristics of Web service interaction patterns, such as recursive composition, sequential and parallel flow, etc, and (iii) it supports automatic composition of services.

Graph processing model is being adopted extensively in various domains such as online gaming, social media, scientific computing and Internet of Things (IoT). Since general purpose data processing tools such as MapReduce are shown to be inefficient for iterative graph processing, many frameworks have been developed in recent years to facilitate analytics and computing of large-scale graphs. However, regardless of distributed or single machine based architecture of such frameworks, dynamic scalability is always a major concern. It becomes even more important when there is a correlation between scalability and monetary cost - similar to what public clouds provide. The pay-as-you-go model that is used by public cloud providers enables users to pay only for the number of resources they utilize. Nevertheless, processing large-scale graphs in such environments has been less studied and most frameworks are implemented for commodity clusters where they will not be charged for the resources that they consume. In this paper, we have developed algorithms to take advantage of resource heterogeneity in cloud environments. Using these algorithms, the system can automatically adjust the number and types of virtual machines according to the computation requirements for convergent graph applications to improve the performance and reduce the monetary cost of the entire operation. Also, a smart profiling mechanism along with a novel dynamic repartitioning approach helps to distribute graph partitions expeditiously. It is shown that this method outperforms popular frameworks such as Giraph and decreases more than 50 percent of the dollar cost compared to Giraph.

Cloud services are often distributed across several data centers requiring new scalable approaches to efficiently perform searching to reduce the energy and price cost of fulfilling requests. Multiagent-based systems have arisen as a powerful technique for improving distributed processing on a wide scale, which can operate in environments where partial observability is the norm and the cost of prolonged search can be exponential. In this paper, we present a multiagent-based service composition approach, using agent-matchmakers and agent-representatives, for the efficient retrieval of distributed services and propagation of information within the agent network to reduce the amount of brute-force search. Our extensive simulation results indicate that by introducing localized agent-based memory searches, the amount of actions (with their associated energy costs) can be reduced by over 50 percent which results in a lower energy cost per composition request.

Rapid adoption of Cloud computing for hosting services and its success is primarily attributed to its attractive features such as elasticity, availability and pay-as-you-go pricing model. However, the huge amount of energy consumed by cloud data centers makes it to be one of the fastest growing sources of carbon emissions. Approaches for improving the energy efficiency include enhancing the resource utilization to reduce resource wastage and applying the renewable energy as the energy supply. This work aims to reduce the carbon footprint of the data centers by reducing the usage of brown energy and maximizing the usage of renewable energy. Taking advantage of microservices and renewable energy, we propose a self-adaptive approach for the resource management of interactive workloads and batch workloads. To ensure the quality of service of workloads, a brownout-based algorithm for interactive workloads and a deferring algorithm for batch workloads are proposed. We have implemented the proposed approach in a prototype system and evaluated it with web services under real traces. The results illustrate our approach can reduce the brown energy usage by 21 percent and improve the renewable energy usage by 10 percent.

Most of existing resource provisioning methods are designed for traditional Web applications with linear structures. However, Web systems with the meshed topology are becoming widespread. Meshed connections among different tiers make Virtual Machine (VM) provisioning and bottleneck elimination complex. In this paper, a Jackson network based Proactive and Reactive VM auto-scaling Method (JPRM) is proposed. In JPRM, request transition behaviors among tiers are modeled as a finite-state Markov stochastic process. A transition probability matrix is studied on-line to predict resource requirements based on M/M/N queuing models as proactive control. For reactive provisioning, the final increased request rate of each tier is determined based on stable state checking and Jackson equilibrium equation solving to eliminate bottleneck tiers and avoid bottleneck shifting. The JPRM is evaluated in a simulation environment established using CloudSim. Experimental results show that JPRM avoids bottleneck shifting with reasonable additional VM rental costs compared with existing methods.

With the major development of sensor technologies and advancements of communication network infrastructures, there is a growing interest to add more intelligence in the e-health monitoring for facilitating an effective healthcare system. While IoT devices are capable of continuous health-parameter sensing and providing notifications to the user, an effective business process management (BPM) facilitates effective system integration and data processing workflow. This paper proposes an efficient framework for managing emergency situations (specifically, health-related) through the analysis of heterogeneous data sources. The proposed framework, named CLAWER (CLoud-Fog bAsed Workflow for Emergency seRvice) aims to bridge the gap between process management and data analytics by providing an automated workflow for personalized health-monitoring and efficient recommendation system. Here, the IoT devices are used for collecting the movement and health data. The smart phone can act as an edge device to acquire data with user movement information. The accumulated data is initially processed inside the fog device, and finally the analysis and recommendations are generated by the cloud. In this paper the indoor health-status of the users are analysed in small cell cloud enhanced eNode B, which is used as fog device. The generated recommendations are stored in the fog device to provide the recommendations to the users with low latency and in timely manner. The experimental analysis of CLAWER yields better precision and recall values than the existing methods.

Modern computing paradigms, such as cloud computing, are increasingly adopting GPUs to boost their computing capabilities primarily due to the heterogeneous nature of AI/ML/deep learning workloads. However, the energy consumption of GPUs is a critical problem. Dynamic Voltage Frequency Scaling (DVFS) is a widely used technique to reduce the dynamic power of GPUs. Yet, configuring the optimal clock frequency for essential performance requirements is a non-trivial task due to the complex nonlinear relationship between the application's runtime performance characteristics, energy, and execution time. It becomes more challenging when different applications behave distinctively with similar clock settings. Simple analytical solutions and standard GPU frequency scaling heuristics fail to capture these intricacies and scale the frequencies appropriately. In this regard, we propose a data-driven frequency scaling technique by predicting the power and execution time of a given application over different clock settings. We collect the data from application profiling and train the models to predict the outcome accurately. The proposed solution is generic and can be easily extended to different kinds of workloads and GPU architectures. Furthermore, using this frequency scaling by prediction models, we present a deadline-aware application scheduling algorithm to reduce energy consumption while simultaneously meeting their deadlines. We conduct real extensive experiments on NVIDIA GPUs using several benchmark applications. The experiment results have shown that our prediction models have high accuracy with the average RMSE values of 0.38 and 0.05 for energy and time prediction, respectively. Also, the scheduling algorithm consumes 15.07% less energy as compared to the baseline policies.

Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidation-based energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. This makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. The experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided.

Although cloud computing became a mainstream industrial computing paradigm, low resource utilization remains a common problem that most warehouse-scale datacenters suffer from. This leads to a significant waste of hardware resources, infrastructure investment, and energy consumption. As the diversity in application workloads grows into an essential characteristic in modern datacenters, task co-location of different workloads to the same compute cluster has gained immense popularity as a heuristic solution for resource utilization optimization. Although the existing co-location methodologies manage to improve resource efficiency to a certain degree, application QoS is usually sacrificed as a trade-off when dealing with resource interference between different applications. This paper proposes a containerized task co-location (CTCL) scheduler to improve resource utilization and minimize task eviction rate. Our CTCL scheduler (1) applies an elastic task co-location strategy to improve resource utilization; and (2) supports a dynamic task rescheduling mechanism to prevent severe QoS degradation from frequent task evictions. We evaluate our approach in terms of resource efficiency and rescheduling cost through the ContainerCloudSim simulator. Our experiments with the Alibaba 2018 workload traces demonstrate that CTCL could improve overall resource efficiency and reduce rescheduling rate by 38% and 99% respectively.

Summary Most existing quality of service (QoS) control algorithms of Web applications take into account Web Server or database connections which can be released immediately. However, many applications are deployed on virtual machines (VMs) or even Spot VMs elastically rented from public Clouds. To save costs, interval‐priced VMs are not released until the ends of rented intervals. Such delays of control effects make existing methods rent or release excess VMs leading to overcontrol. Fluctuated prices make Spot VMs unreliable due to unexpected termination which makes fault‐tolerant strategies crucial. In this article, an unequal‐interval‐based loosely coupled control method is proposed to improve the quality of service (QoS) control ability of fault‐tolerant strategies. A queuing model with arrival‐rate‐adjustment coefficient is used to predict required capacity as a feedforward controller. Another two‐threshold and queuing‐model‐based method is applied to update the coefficient as a loosely coupled feedback controller. Meanwhile, unequal‐interval controller collaborating method is proposed to avoid overcontrol and react quickly to workload changes. Our approach is evaluated on both a simulation platform and a real Kubernetes Cluster. Experimental results illustrate that our approach decreases the percentage of waiting times larger than service level agreements with similar or lower rental costs compared with existing algorithms.

The use of Internet of Things (IoT) has introduced genuine concerns regarding data security and its privacy when data are in collection, exchange, and use. Meanwhile, blockchain offers a distributed and encrypted ledger designed to allow the creation of immutable and tamper-proof records of data at different locations. While blockchain may enhance IoT with innate security, data integrity, and autonomous governance, IoT data management and its allocation in blockchain still remain an architectural concern. In this article, we propose a novel context-aware mechanism for on-chain data allocation in IoT-blockchain systems. Specifically, we design a data controller based on fuzzy logic to calculate the Rating of Allocation (RoA) value of each data request considering multiple context parameters, i.e., data, network, and quality and decide its on-chain allocation. Furthermore, we illustrate how the design and realization of the mechanism lead to refinements of two commonly used IoT-blockchain architectural styles (i.e., blockchain-based cloud and fog). To demonstrate the effectiveness of our approach, we instantiate the data allocation mechanism in the blockchain-based cloud and fog architectures and evaluate their performance using FogBus. We also compare the efficacy of our approach to the existing decision-making mechanisms through the deployment of a real-world healthcare application. The experimental results suggest that the realization of the data allocation mechanism improves network usage, latency, and blockchain storage and reduces energy consumption.

Fog computing has emerged as a complementary solution to address the issues faced in cloud computing. While fog computing allows us to better handle time/delay-sensitive Internet of Everything (IoE) applications (e.g. smart grids and adversarial environment), there are a number of operational challenges. For example, the resource-constrained nature of fog-nodes and heterogeneity of IoE jobs complicate efforts to schedule tasks efficiently. Thus, to better streamline time/delay-sensitive varied IoE requests, the authors contributes by introducing a smart layer between IoE devices and fog nodes to incorporate an intelligent and adaptive learning based task scheduling technique. Specifically, our approach analyzes the various service type of IoE requests and presents an optimal strategy to allocate the most suitable available fog resource accordingly. We rigorously evaluate the performance of the proposed approach using simulation, as well as its correctness using formal verification. The evaluation findings are promising, both in terms of energy consumption and Quality of Service (QoS).

The Internet of Things (IoT) paradigm promises to make “things” such as physical objects with sensing capabilities and/or attached with tags, mobile objects such as smartphones and vehicles, consumer electronic devices, and home appliances such as fridge, television, health care devices, as part of the Internet environment. In cloud-centric IoT applications, the sensor data from these “things” is extracted, accumulated, and processed at the public/private clouds, leading to significant latencies. To satisfy the ever increasing demand for cloud computing resources from emerging applications such as IoT, academics and industry experts are now advocating for going from large-centralized cloud computing infrastructures to micro data centers located at the edge of the network. These micro data centers are often closer to a user (geographically and in access latency) compared to the centralized cloud data center. The aim of utilizing such edge resources is to off load computation that would have “traditionally” been carried out at the cloud data center to a resource that is closer to a user or edge devices. This vision also acknowledges the variation in network latency from an end-user to cloud data center. While the network around a data center is often high capacity and speed, that near the user device may have variable properties (in terms of resilience, bandwidth, latency, etc.). Referred to as “fog/edge computing,” this paradigm is expected to improve the agility of cloud service deployments in addition to bringing computing resources closer to end-users. The emergence of computing paradigms such as edge and fog computing supports the data analysis near the data sources for a wide range of applications. Edge computing is the middle layer between users and cloud data centers, and it plays an important role in the IoT use cases where applications required near real-time actions. The intermediate edge layer provides limited computing and storage resources, which consists of network gateways and micro data centers. Edge computing application orchestration is capable of big data processing and can be installed in heterogeneous hardware configurations. Due to the large-scale deployment and device heterogeneity, edge computing infrastructure designing and implementation are challenging, including model analysis, system integration, protocol designing, energy, and security modeling. In addition, since edge data centers are installed in network gateways with open network configuration, they are prone to several network threats, less trustworthy, and easy to compromise. On the one hand, the development of fog and edge clouds includes dedicated facilities, operating system, network, and middleware techniques to build and operate such micro data centers that host virtualized computing resources. On the other hand, the use of fog and edge clouds requires extension to current programming models and proposes new abstractions that will allow developers to design new applications that take benefit from such massively distributed systems. The use of this approach also opens up other challenges in security and privacy (as a user now needs to “trust” every micro data center they interact with), support for resource management for mobile users who transfer session from one micro data center to another, and support for “embedding” such micro data centers into devices (eg, cars, buildings, etc). The objective of this special issue is to disseminate original contributions and research findings concerning the challenges and changes (both evolutionary and disruptive) in edge and fog computing. It provides cutting-edge research from both academia and industry, with emphasis on current developments and future directions in security and privacy issues of emerging fog computing. The call for special issues received a number of submissions. Each paper was reviewed by at least three reviewers and went through at least two rounds of reviews. After a two-phase peer review process, we have accepted 14 high-quality papers related to the aforementioned areas of interest. The accepted papers focus on recent solutions by developing novel research ideas around edge and fog computing for several applications, such as health care, smart city, urban pollution monitoring, etc. The brief contributions of these papers are discussed in the following section. The first paper titled “Abnormal visual event detection based on multi-instance learning and autoregressive integrated moving average model in edge-based Smart City surveillance” by Xu et al proposes an abnormal event detection approach based on multi-instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places. It utilizes an unsupervised method for abnormal event detection by combining multi-instance visual feature selection and the autoregressive integrated moving average model. This approach has thoroughly experimented, and the experimental results demonstrate the efficiency of the proposed approach by achieving better abnormal event detection performance for a crowded scene of urban public places with an edge environment. The second paper titled “User allocation-aware edge cloud placement in mobile edge computing” by Guo et al studies the edge cloud placement problem, which is to place the edge clouds at the candidate locations and allocate the mobile users to the edge clouds. Further, it formulates as a multi-objective optimization problem with the objective to balance the workload between edge clouds and minimize the service communication delay of mobile users. The experiment results show the performance of the proposed approach in terms of workload balance and communication delay for validation. The third paper titled “A secure fog-based platform for SCADA-based IoT critical infrastructure” by Baker et al contributes a novel security “toolbox” to reinforce the integrity, security, and privacy of SCADA-based IoT critical infrastructure at the fog layer. The toolbox incorporates a key feature, that is, a cryptographic-based access approach to the cloud services using identity-based cryptography and signature schemes at the fog layer. This paper also presents the implementation details of a prototype for our proposed secure fog-based platform and provides performance evaluation results to demonstrate the appropriateness of the proposed platform in a real-world scenario. The results from the experiments demonstrate a superior performance of the secure fog-based platform, which is around 2.8 seconds when adding five virtual machines (VMs), 3.2 seconds when adding 10 VMs, and 112 seconds when adding 1000 VMs, compared to the multilevel user access control platform. The fourth paper titled “Developing applications in large scale, dynamic fog computing: A case study” by Giang et al presents a case study in building fog computing applications using an open-source platform distributed node-RED. It shows how applications can be decomposed and deployed to a geographically distributed infrastructure using distributed node-RED, and how existing software components can be adapted and reused to participate in fog applications. This case study is implemented in a lab-based fog infrastructure and simulated for large-scale evaluation. The fifth paper titled “An osmotic computing infrastructure for urban pollution monitoring” by Longo et al focuses on the design and development of a middleware that integrates data coming from mobile and IoT devices specifically deployed in urban contexts using the osmotic computing paradigm. Moreover, a component of the osmotic membrane has been developed in this paper for security management. The sixth paper titled “Characterizing application scheduling on edge, fog, and cloud computing resources” by Varshney and Simmhan offers a taxonomy of concepts essential for specifying and solving the problem of scheduling applications on edge, fog, and cloud computing resources. The proposed model is divided into multiple steps, initially characterized by the resource capabilities and limitations of these infrastructures and offers a taxonomy of application models, quality-of-service constraints and goals, and scheduling techniques based on a literature review, followed by tabulated key research prototypes and papers using this taxonomy. It also highlights gaps in the literature and open problems remain. The seventh paper titled “Cloud-aided online electroencephalography (EEG) classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN” by Ke et al presents the design of an online EEG classification system aided by cloud centering on a lightweight convolutional neural network (CNN). The system incrementally trains the CNN on cloud and enables hot deployment of the trained classifier without the need to restart the gateway to adapt to the users' needs. The classifier maintains a high convolutional layer to gain the ability of processing high-dimensional EEG segments. Finally, the model is experimented to validate the contribution. The eighth paper titled “SEWMS: An Edge-based Smart Wearable Maintenance System in Communication Network” by Rui et al proposes a dynamic context-aware information push algorithm (DCAIP) by focusing on the current low level in information, complicated scenes, and various information on on-site maintenance. It also presents a smart wearable maintenance system (SEWMS), an edge computing-assisted IoT platform for the real-time guidance of technical experts and systems for on-site maintenance personnel, aiming to improve the efficiency and quality of on-site maintenance. The ninth paper titled “A crosswalk pedestrian recognition system by using deep learning and zebra-crossing recognition techniques” by Dow et al investigates a real-time pedestrian recognition system that ensures high accuracy by using a deep learning classifier and zebra-crossing recognition techniques. The proposed system was designed to improve pedestrian safety and reduce accidents at intersections. Environmental feature vectors were first used to detect zebra crossings and to determine crossing areas. An adaptive mapping technique was then used to map the pedestrian waiting area based on the crossing area. A dual-camera mechanism was used to maintain detection accuracy and improve system fault tolerance. Finally, the you-only-look-once model was used to recognize pedestrians at intersections. The 10th paper titled “Intelligent sentiment analysis approach using edge computing-based deep learning technique” by Sankar et al studies machine learning algorithms to extract the best features from the training review dataset. Then, the selected features are fed into the CNN and other fully connected layers for further processing. This work has also employed a pretrained sentiment analysis model over an Android application framework to classify reviews on a smartphone without the need for any cloud or server-side application programming interface. The 11th paper titled “Pipeline provenance for cloud-based big data analytics” by Wang et al proposes a solution, named LogProv toward realizing the functionalities for big data provenance, which needs to renovate data pipelines or some of big data software infrastructure to generate structured logs for pipeline events, and then stores data and logs separately in cloud space. The LogProv is implemented and deployed in Nectar Cloud, associated with Apache Pig, Hadoop ecosystem, and adopted Elasticsearch to provide query service. The 12th paper titled “Socially aware microcloud service overlay optimization in community networks” by Apolónia et al presents a model, named Select in Community Networks (SELECTinCN), which enhances the overlay creation for pub/sub systems over peer-to-peer (P2P) networks. Moreover, SELECTinCN includes social information based on cooperation within CNs by exploiting the social aspects of the community of practice. The model organizes the peers in a ring topology and provides an adaptive P2P connection establishment algorithm, where each peer identifies the number of connections needed based on the social structure and user availability. The 13th paper titled “DewSim: A trace-driven toolkit for simulating mobile device clusters in Dew computing environments” by Hirsch et al models and develops a trace-based toolkit built on modular software artifacts to speed up research in resource management techniques in Dew environments. A trace-driven methodology is adopted to assure the practical value of simulated scenarios. The toolkit comprises a device profiler application for Android to capture generic battery and central processing unit traces from real devices, a profile mixer to create user interaction baseline traces through generic ones, and an extensible engine to simulate the execution of workloads configurable via text files. The 14th paper titled “How to Place Your Apps in the Fog-State of the Art and Open Challenges” Borgi et al review the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview of the currently employed algorithms, on the availability of open-source prototypes, and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog. The 15th paper titled ‘SELFNET 5G mobile edge computing infrastructure: Design and prototyping’ by Chirivella-Perez et al. presented the design and prototype implementation of the fifth-generation (5G) mobile edge infrastructure based on a mobile edge computing paradigm. This mobile edge infrastructure SELFNET is an amlganmation of cloud computing, software-defined networking, and network function virtualization to end up with a portable 5G infrastructure testbed which enabled the realistic execution and testing. Finally, in the last paper titled ‘SDN/NFV security framework for fog-to-things computing infrastructure’ by Krishnan et al. proposed DTARS which is a System for Distributed Threat Analytics and Response for an Edge/Fog and SDN integrated architecture. In this system, the detection scheme runs at the data plane wherein a coarse-grained behavioral, anti-spoofing, flow monitoring and fine-grained traffic multi-feature entropy-based algorithms are deployed. The proposed framework has been developed for defense applications on malware testbed. We hope that the research contributions and findings in this special issue would benefit the readers in terms of enhancing their knowledge and encouraging them to work on various aspects of edge and fog computing. We express our sincere thanks to the editor-in-chief for allowing us to organize this special issue. The editorial office staffs are excellent and thanks for their support. We are also thankful to all the authors who made this special issue possible, and to the reviewers for their thoughtful contributions.

We are pleased to present a special issue that focuses on the trends in the next generation of cloud computing. An obvious question that may arise in the mind of the reader is why another issue on clouds when there is plenty of discourse on the topic. Undoubtedly, the cloud computing landscape is rapidly changing to meet the challenges of emerging paradigms, such as Fog/Edge computing and the Internet‐of‐Things (IoT). These paradigms rely on services offered by the cloud but are demanding—need to scale for billions of heterogeneous devices and sensors while operating efficiently in real‐time.

MapReduce is a crucial framework in the cloud computing architecture, and is implemented by Apache Hadoop and other cloud computing platforms. The resources required for executing jobs in a large data center vary according to the job types. In general, there are two types of jobs, CPU-bound and I/O-bound, which require different resources but run simultaneously in the same cluster. The default job scheduling policy of Hadoop is first-come-first-served and therefore, may cause unbalanced resource utilization. Considering various job workloads, numerous job allocation schedulers were proposed in the literature. However, those schedulers encountered the data locality problem or unreasonable job execution performance. This study proposes a job scheduler based on a dynamic grouping integrated neighboring search strategy, which can balance the resource utilization and improve the performance and data locality in heterogeneous computing environments.

Cloud computing has recently emerged as an important service to manage applications efficiently over the Internet. Various cloud providers offer pay per use cloud services that requires Quality of Service (QoS) management to efficiently monitor and measure the delivered services through Internet of Things (IoT) and thus needs to follow Service Level Agreements (SLAs). However, providing dedicated cloud services that ensure user's dynamic QoS requirements by avoiding SLA violations is a big challenge in cloud computing. As dynamism, heterogeneity and complexity of cloud environment is increasing rapidly, it makes cloud systems insecure and unmanageable. To overcome these problems, cloud systems require self-management of services. Therefore, there is a need to develop a resource management technique that automatically manages QoS requirements of cloud users thus helping the cloud providers in achieving the SLAs and avoiding SLA violations. In this paper, we present SLA-aware autonomic resource management technique called STAR which mainly focuses on reducing SLA violation rate for the efficient delivery of cloud services. The performance of the proposed technique has been evaluated through cloud environment. The experimental results demonstrate that STAR is efficient in reducing SLA violation rate and in optimizing other QoS parameters which effect efficient cloud service delivery.

The fourth industrial revolution, widely known as Industry 4.0, is realizable through widespread deployment of Internet of Things (IoT) devices across the industrial ambiance. Due to communication latency and geographical distribution, Cloud-centric IoT models often fail to satisfy the Quality of Service requirements of different IoT applications assisting Industry 4.0 in real time. Therefore, Fog computing focuses on harnessing edge resources to place and execute these applications in the proximity of data sources. Since most of the Fog nodes are heterogeneous, distributed, and resource-constrained, it is challenging to place Industry 4.0-oriented applications (I4OAs) over them ensuring time-optimized service delivery. Diversified data sensing frequency of different industrial IoT devices and their data size further intensify the application placement problem. To address this issue, in this article we propose a context-aware application placement policy for Fog environments. Our policy coordinates the IoT device-level contexts with the capacity of Fog nodes and minimizes the service delivery time of various I4OAs such as image processing and robot navigation applications. It also ensures that the streams of input data flowing toward the placed applications neither congest the network nor increase the computing overhead of host Fog nodes significantly. Performance of the proposed policy is evaluated in both real-world and simulated Fog environments and compared with the existing placement policies. The experiment results show that our policy offers overall 16% improvement in service latency, network relaxation, and computing overhead management compared to other placement policies.

To slow down the spread of COVID-19, governments worldwide are trying to identify infected people, and contain the virus by enforcing isolation, and quarantine. However, it is difficult to trace people who came into contact with an infected person, which causes widespread community transmission, and mass infection. To address this problem, we develop an e-government Privacy-Preserving Mobile, and Fog computing framework entitled PPMF that can trace infected, and suspected cases nationwide. We use personal mobile devices with contact tracing app, and two types of stationary fog nodes, named Automatic Risk Checkers (ARC), and Suspected User Data Uploader Node (SUDUN), to trace community transmission alongside maintaining user data privacy. Each user's mobile device receives a Unique Encrypted Reference Code (UERC) when registering on the central application. The mobile device, and the central application both generate Rotational Unique Encrypted Reference Code (RUERC), which broadcasted using the Bluetooth Low Energy (BLE) technology. The ARCs are placed at the entry points of buildings, which can immediately detect if there are positive or suspected cases nearby. If any confirmed case is found, the ARCs broadcast pre-cautionary messages to nearby people without revealing the identity of the infected person. The SUDUNs are placed at the health centers that report test results to the central cloud application. The reported data is later used to map between infected, and suspected cases. Therefore, using our proposed PPMF framework, governments can let organizations continue their economic activities without complete lockdown.

Containers, as a lightweight application virtualization technology, have recently gained immense popularity in mainstream cluster management systems like Google Borg and Kubernetes. Prevalently adopted by these systems for task deployments of diverse workloads such as big data, web services, and IoT, they support agile application deployment, environmental consistency, OS distribution portability, application-centric management, and resource isolation. Although most of these systems are mature with advanced features, their optimization strategies are still tailored to the assumption of a static cluster. Elastic compute resources would enable heterogeneous resource management strategies in response to the dynamic business volume for various types of workloads. Hence, we propose a heterogeneous task allocation strategy for cost-efficient container orchestration through resource utilization optimization and elastic instance pricing with three main features. The first one is to support heterogeneous job configurations to optimize the initial placement of containers into existing resources by task packing. The second one is cluster size adjustment to meet the changing workload through autoscaling algorithms. The third one is a rescheduling mechanism to shut down underutilized VM instances for cost saving and reallocate the relevant jobs without losing task progress. We evaluate our approach in terms of cost and performance on the Australian National Cloud Infrastructure (Nectar). Our experiments demonstrate that the proposed strategy could reduce the overall cost by 23% to 32% for different types of cloud workload patterns when compared to the default Kubernetes framework.

In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time.

As greater numbers of data-intensive applications are required to process big data in bandwidth-constrained datacenters with heterogeneous physical machines (PMs) and virtual machines (VMs), network core traffic is experiencing rapid growth. The VMs of a virtual cluster (VC) must be allocated as compactly as possible to avoid bandwidth-related bottlenecks. Since each PM/switch has a certain failure probability, a VC may not be executed when it meets with any PM/switch fault. Although the VMs of a VC can be spread out across different fault domains to minimize the risk of violating the availability requirement of the VC, this increases the network core traffic. Therefore, avoiding the decrease in availability caused by the heterogeneous PM/switch failure probabilities and bandwidth-related bottlenecks has been a constant challenge. In this paper, we first introduce a joint optimization function to measure the overall risk cost and overall bandwidth usage in the network core to allocate the same set of data-intensive applications. We then introduce an approach to maximize the value of the joint optimization function. Finally, we performed a side-by-side comparison with prior algorithms, and the experimental results show that our approach outperforms the other existing algorithms.

Understanding human interests and intents from movement data are fundamental challenges for any location-based service. With the pervasiveness of sensor embedded smartphones and wireless networks and communication, the availability of spatio-temporal mobility trace (timestamped location information) is increasingly growing. Analysing these huge amount of mobility data is another major concern. This paper proposes a cloud-based framework named MovCloud to efficiently manage and analyse mobility data. Specifically, the framework presents a hierarchical indexing schema to store trajectory data in different spatio-temporal resolution, clusters the trajectories based on semantic movement behaviour instead of only raw latitude, longitude point and resolves mobility queries using MapReduce paradigm. MovCloud is implemented over Google Cloud Platform (GCP) and an extensive set of experiments on real-life data yield the effectiveness of the proposed framework. MovCloud has achieved ~ 28% better clustering accuracy and also executed three times faster than the baseline methods.

Recently Edge Computing paradigm has gained significant popularity both in industry and academia. With its increased usage in real-life scenarios, security, privacy and integrity of data in such environments have become critical. Malicious deletion of mission-critical data due to ransomware, trojans and viruses has been a huge menace and recovering such lost data is an active field of research. As most of Edge computing devices have compute and storage limitations, difficult constraints arise in providing an optimal scheme for data protection. These devices mostly use Linux/Unix based operating systems. Hence, this work focuses on extending the Ext4 file system to APEX (Adaptive Ext4): a file system based on novel on-the-fly learning model that provides an Adaptive Recover-ability Aware file allocation platform for efficient post-deletion data recovery and therefore maintaining data integrity. Our recovery model and its lightweight implementation allow significant improvement in recover-ability of lost data with lower compute, space, time, and cost overheads compared to other methods. We demonstrate the effectiveness of APEX through a case study of overwriting surveillance videos by CryPy malware on Raspberry-Pi based Edge deployment and show 678% and 32% higher recovery than Ext4 and current state-of-the-art File Systems. We also evaluate the overhead characteristics and experimentally show that they are lower than other related works.

A composite service in multi-tenant SaaS cloud would inevitably operate under dynamic changes on the workload from the tenants, and thus it is not uncommon for the composition to encounter under-utilization and over-utilization on the component services. However, both of those cases could be good or bad: the former implies that although there is under-utilization, the pay-off afterwards are more significant; the latter, in contrast, refers to the over-utilization that leads to trivial pay-off, or nothing at all. Such a notion perfectly matches with the Technical Debt (TD) metaphor in Software Engineering. As a result, it is necessary to identify the root causes of the debts and where the debt can be manifested in the service composition, which, in turn, would offer great helps on the decision making process of service composition. In this paper, we propose a novel approach for identifying the technical debt in service composition under SaaS cloud. The approach combines time series forecasting and a newly proposed technical debt model to estimate the future debt and utility in the service composition. Through a real world case study, we demonstrate that our approach can successfully identify both the good and bad debts, while producing satisfactory accuracy on estimating the technical debt in the service composition under SaaS cloud.

Summary Cloud computing utilizes heterogeneous resources that are located in various datacenters to provide an efficient performance on a pay‐per‐use basis. However, existing mechanisms, frameworks, and techniques for management of resources are inadequate to manage these applications, environments, and the behavior of resources. There is a requirement of a Quality of Service (QoS) based autonomic resource management technique to execute workloads and deliver cost‐efficient and reliable cloud services automatically. In this paper, we present an intelligent and autonomic resource management technique named RADAR. RADAR focuses on two properties of self‐management: firstly, self‐healing that handles unexpected failures and, secondly, self‐configuration of resources and applications. The performance of RADAR is evaluated in the cloud simulation environment and the experimental results show that RADAR delivers better outcomes in terms of execution cost, resource contention, execution time, and SLA violation while it delivers reliable services.

Summary Data centers consume an enormous amount of energy to meet the ever‐increasing demand for cloud resources. Computing and Cooling are the two main subsystems that largely contribute to energy consumption in a data center. Dynamic Virtual Machine (VM) consolidation is a widely adopted technique to reduce the energy consumption of computing systems. However, aggressive consolidation leads to the creation of local hotspots that has adverse effects on energy consumption and reliability of the system. These issues can be addressed through efficient and thermal‐aware consolidation methods. We propose an Energy and Thermal‐Aware Scheduling (ETAS) algorithm that dynamically consolidates VMs to minimize the overall energy consumption while proactively preventing hotspots. ETAS is designed to address the trade‐off between time and the cost savings and it can be tuned based on the requirement. We perform extensive experiments by using the real‐world traces with precise power and thermal models. The experimental results and empirical studies demonstrate that ETAS outperforms other state‐of‐the‐art algorithms by reducing overall energy without any hotspot creation.

Summary Cloud computing is a model for on‐demand access to shared resources based on the pay‐per‐use policy. In order to efficiently manage the resources, a continuous analysis of the operational state of the system is required to be able to detect the performance degradations and malfunctioned resources as soon as possible. Every change in the workload, hardware condition, or software code can change the state of the system from normal to abnormal, which causes the performance and quality of service degradations. These changes or anomalies vary from a simple gradual increase in the load to flash crowds, hardware faults, software bugs, etc. In this paper, we propose Isolation‐Forest based anomaly detection (IFAD) framework based on the unsupervised Isolation technique for anomaly detection in a multi‐attribute space of performance indicators for web‐based applications. Unsupervised nature of the algorithm and its fast execution make this algorithm most suitable for the environments with dynamic nature where the patterns of data change frequently. The experiment results demonstrate that IFAD can achieve good detection accuracy especially in terms of precision for multiple types of the anomaly. Moreover, we show the importance of validating the accuracy of anomaly detection algorithms with regard to both Area Under the Curve (AUC) and Precision‐Recall AUC (PRAUC) in an extensive set of comparisons including multiple unsupervised algorithms. The demonstration of the effectiveness of each algorithm shown by PRAUC results indicates the importance of PRAUC in selecting suitable anomaly detection algorithm, which is largely ignored in the literature.

In recent years, we have witnessed tremendous advances in cloud data centres (CDCs) from the point of view of the communication layer.A recent report from Cisco Systems Inc. demonstrates that CDCs, which are distributed across many geographical locations, will dominate the global data centre traffic flow for the foreseeable future.Their importance is highlighted by a top-line projection from this forecast that by 2019, more than four-fifths of total data centre traffic will be Cloud traffic.The geographical diversity of the computing resources in CDCs provides several benefits, such as high availability, effective disaster recovery, uniform access to users in different regions, and access to different energy sources.Although Cloud technology is currently predominant, it is essential to leverage new agile software technologies, agile processes and agile applications near to both the edge and the users; hence, the concept of Fog has been developed.Fog computing (FC) has emerged as an alternative to traditional Cloud computing to support geographically distributed, latency-sensitive and QoS-aware IoT applications while reducing the burden on data centres used in traditional Cloud computing.In particular, FC with features that can support heterogeneity and real-time applications (e.g.low latency, location awareness, and the capacity to process a large number of nodes with wireless access) is an attractive solution for delay-and resource-constrained large-scale applications.The distinguishing feature of the FC paradigm is that a set of Fog nodes (FNs) spreads communication and computing resources over the wireless access network to provide resource augmentation to resource-and energy-limited wireless (possibly mobile) devices.The joint management of Fog and Internet of Technology (IoT) paradigms can reduce the energy consumption and operating costs of state-of-the-art Fog-based data centres (FDCs).An FDC is dedicated to supervising the transmission, distribution and communication of FC.As a vital component of the Internet of Everything (IoE) environment, an FDC is capable of filtering and processing a considerable amount of incoming data on edge devices, by making the data processing architecture distributed and thereby scalable.An FDC therefore provides a platform for filtering and analysing the data generated by sensors utilising the resources of FNs.Increasing interest is emerging in FDCs and CDCs that allow the delivery of various kinds of agile services and applications over telecommunication networks and the Internet, including resource provisioning, data streaming/transcoding, analysis of high-definition videos across the edge of the network, IoE application analysis etc. Motivated by these issues, this special section solicits original research and practical contributions that advance the use of CDCs/FDCs in new technologies such as IoT, edge networks and industries.Results obtained from simulations are validated in terms of their boundaries by experiments or analytical results.The main objectives of this special issue are to provide a discussion forum for people interested in Cloud and Fog networking, and to present new models, adaptive tools and applications specifically designed for distributed and parallel on-demand requests received from (mobile) users and Cloud applications.The papers presented in this special issue provide insights in fields related to Cloud and Fog/edge architecture, including parallel processing of Cloudlets/Foglets, the presentation of new emerging models, performance evaluation and improvements, and developments in Cloud/Fog applications.We hope that readers can benefit from the insights in these papers, and contribute to these rapidly growing areas.

As the Internet of Things (IoT) paradigm is maturing, innovative, and novel services are being envisioned. An upcoming trend is the depiction of services enacted through seamless integration of multiple vertical IoT services, termed as cross-vertical or unified IoT services in this paper. Traditional Cloud-based centralized network architectures cannot cater to real-time responses demanded by such unified IoT applications. Moreover, introducing Fog nodes within the network architecture, though a promising alternative, cannot sustain the burden of a huge number of applications that culminates in massive data handling. In this paper, we envision employing lessons learned from context-aware computing, specifically context sharing among interdependent vertical IoT applications to address this delay requirement of such unified IoT applications by enacting context sharing among Fog nodes for minimizing system delay. The detailed network model and context sharing mechanism have been presented and the service time minimization has been framed as an optimization problem. Algorithms for context sharing and delay tolerant load balancing have been presented and simulation results carried out demonstrate the efficacy of the proposed methodology.

Summary Energy‐efficient smart retail system design is a challenging research area. In this paper, we propose an automated retail system using a femtolet‐based fog network. A femtolet is an indoor base station providing computation and storage. Femtolets in our system work as indoor base stations and maintain databases of the products located in their respective coverage areas. The femtolets switch to active or idle mode according to the user's presence in its coverage. A smart trolley is proposed for our retailing system, which guides the user to the particular product type selected by the user. The user, after entering the shopping mall, carries the smart trolley. The customer selects and purchases products using this trolley. On the basis of product purchasing, the respective databases maintained inside the femtolets are updated. An Android application for the proposed retailing is developed. We compare the power consumption and delay of the proposed retail system with the existing retail system. Simulation analyses illustrate that the proposed approach reduces power by approximately 89% and 94%, respectively, in comparison to the local cloud server–based and remote cloud server–based retail systems. Thus, we refer to the proposed system as a green retail system. The performance of the proposed system through experimental analysis is also evaluated.

Summary Containers, enabling lightweight environment and performance isolation, fast and flexible deployment, and fine‐grained resource sharing, have gained popularity in better application management and deployment in addition to hardware virtualization. They are being widely used by organizations to deploy their increasingly diverse workloads derived from modern‐day applications such as web services, big data, and internet of things in either proprietary clusters or private and public cloud data centers. This has led to the emergence of container orchestration platforms, which are designed to manage the deployment of containerized applications in large‐scale clusters. These systems are capable of running hundreds of thousands of jobs across thousands of machines. To do so efficiently, they must address several important challenges including scalability, fault tolerance and availability, efficient resource utilization, and request throughput maximization among others. This paper studies these management systems and proposes a taxonomy that identifies different mechanisms that can be used to meet the aforementioned challenges. The proposed classification is then applied to various state‐of‐the‐art systems leading to the identification of open research challenges and gaps in the literature intended as future directions for researchers.

Cloud computing, Internet of Things (IoT), and big data are three important technology trends affecting all the major enterprises across the world. All these three areas are successors of classical areas of data centers, sensor networks, and data processing and prediction solutions. However, the way industries, governments, and individuals are changing across the globe, cloud, IoT, and big data analytics are going to contribute a lot in future technology transformations. We see that a number of past forecasts and anticipations hold true for the growing cloud computing adoption. A report such as Gartner1 forecasts a heavy growth of around 17% in the overall revenues from public cloud computing infrastructure and related services in year 2019. Various cloud services contributing to this revenue include services such as cloud business process services (BPaaS), cloud application infrastructure services (PaaS), cloud application Services (SaaS), cloud management and security services, and cloud system infrastructure services (IaaS). Out of all these services, the major stakeholder with the highest revenue share is cloud application services, which are mostly SaaS services. These services provide variety of solutions to a number of domains including user computing, content management, hosting, analytics, and many more. The domain of cloud computing and its services are also seeing notable changes due to a sizable adoption of IoT environments and their increasing applications. A report in the work of Puranik2 envisaged that IoT will play a major stake in extensions of cloud applications. This report also forecasts that the recent time will see a heavy usage of data analytics driven IoT services running is the cloud. The integration of cloud, IoT, and data analytics is quickly becoming a center of the technology support for a variety of applications starting from improved customer experience, accurate predictions to better supply chain management. Coming to emerging IoT adoption, a major survey revealed that IoT will be a center of the upcoming technologies and will have the most impactful “machine-aided commerce” applications in coming five years from now.3 The impact of IoT is envisaged in this report ahead of cloud computing and artificial intelligence. Gartner in a report4 forecasted a number of trends related to IoT technologies and their role in shaping the current and upcoming businesses. They expect that there will be more than 20 billion IoT devices within two years from now. The report also anticipates a lack of trained data science specialists who can fully utilize the potential of these IoT devices. In particular, this report anticipates a 4:1 ratio between the devices and human beings to highlight the role of IoT devices. We are seeing a growing role of data analytics technologies in all technology sectors including advertising, finance, market research, and many other areas. A detailed report by Gartner in the work of Laney and Jain5 shows multiple faces of data analytics with a focus on its effects on various technical and nontechnical stakeholders of the industry. This report shows a number of important statistics and predictions including a major role of data analytics-based decisions and its dependency of IoT and cloud computing as enabler technologies. There is a growing interest among the research communities and academic groups to pursue and solve various related research problems at the intersection point of three different areas of cloud, IoT, and data analytics. Buyya et al6 showcase a detailed analysis of future directions and research problems of cloud computing. The authors list a number of challenges yet to be addressed, which include challenges related to scalability, security, heterogeneity, and economics. The authors also envisage a growing role of IoT and data analytics applications running in the cloud. A discussion in the work of CACM Staff7 highlights the role of IoT, data security, machine learning, and cloud computing. Eugster et al8 showcase that the growing computational requirements by big data analytics may even replace a single cloud with a cloud of clouds. A number of recent contributions address the growing security issues in amalgamation of cloud, IoT, and Big data. Kumarage et al9 show applications of homomorphic encryption scheme to provide secure cloud-based data analytics for IoT applications. Newer intermediate node-based paradigms such as fog computing have also evolved to provide quick and scalable solutions to support IoT applications.10 Siow et al11 provide a detailed treatment to the data analytics methods for IoT applications in the areas of health, transport, living, environment, and other industry related problems. In addition, authors provide a detailed taxonomy of predictive data analytics solutions with their objectives. On the other hand, Botta et al12 provide a detailed perspective on integration of cloud and IoT technologies and the new form of “CloudIoT” applications. In addition, the authors detail various complementary aspects such as displacement, reachability, and role of big data while seeing this integration. In the coming times, it is inevitable to see the success of any one of the three technology paradigms to deliver without the help of the other two. The role of these three paradigms is also very well suited where the cloud provides infrastructure, IoT devices work as real-time data and knowledge generators, and big data analytics to provide meaningful predictions. This Special Issue on “Integration of Cloud, IoT and Big Data Analytics” has five research contributions. These contributions focus on various important aspects of the intersection of these three paradigms. The first article of this special issue is titled Cloud-based video analytics using convolutional neural networks.13 The authors in this contribution provide a video analytics approach using convolutions neural networks, which uses an “in-memory” distributed computing scheme on cloud infrastructure. The contribution highlights an object classification approach that performs a threshold-based comparison among the stored objects and the input objects in videos streams. The authors provide a detailed mathematical analysis of video analytics process with a focus on their “in-memory” distributed computing approach. The authors also provide a detailed description of experimentation performed on a spark-based private cloud platform. The authors also provide role of data size and computing node in the overall processing of the video data. The authors in this paper show a matching accuracy of 97%. A number of IoT applications are based on video and this contribution demonstrates the role of cloud infrastructure scalability in video data analytics. The second article in this special issue is titled A middleware solution for integrating and exploring IoT and HPC capabilities.14 The authors in this paper provide a new middleware solution, “JCL”, for collaboration of IoT applications and high performance computing (HPC) facilities. The authors argue that there is a strong need of having middleware solutions for the emerging IoT devices and the computations on HPC resources. To address these issues, the authors showcase JCL middleware API that supports one API to program different device categories, supporting various programming models, interoperability among various IoT services, and security issues. The authors consider IoT tasks as HPC tasks and perform the processing in JCL. The authors state that the heterogeneity issues of IoT devices are addressed in JCL using Java-enabled Android and Arduino devices. To show the simplicity, authors demonstrate a small prototype IoT-HPC application in JCL with a focus on battery consumption studies. The middleware and related APIs are need of the hour for the IoT applications. The third article in this special issue is on A multi-time steps ahead prediction approach for scheduling live migration in cloud data centers.15 This contribution focuses on a prediction approach to anticipate the live virtual machine (VM) migration in cloud computing infrastructure. The authors in this contribution state that the short duration prediction decisions in the cloud infrastructure lack accuracy due to the dynamic nature of cloud resource management. The authors focus primarily on live VM migration problem as a prediction problem and assess linear and nonlinear methods for this purpose. The authors propose a multitime step-based recurrent neural network-based prediction approach to forecast CPU utilization and bandwidth data during a live migration. Authors evaluate single time step ahead and multitime step-ahead prediction algorithms for prediction of bandwidth and CPU data using recurrent neural network. The authors show simulation results for cloud infrastructure and reveal that the recurrent neural network-based approach outperforms various traditional prediction approaches. The fourth article in this special issue is titled Evolutionary mutation testing for IoT with recorded and generated events.16 The authors in this contribution testify the event processing language (EPL) in the context of IoT events using evolutionary mutation testing (EMT) approach. The authors argue that EPL is a suitable programming language of event-based IoT applications and its testing using EMT. The authors in this contribution focus on two research questions. The first question aims at the possibility of reducing the complexity of EMT beyond the random selections. The second question aims to test the suitability of IoT-TEG (test event generator). The authors address the first question by performing experiments and concluded that guided EMT helps in finding more strong mutants. On the other hand, the second question helps in evaluating IoT-TEG and the authors show that IoT-TEG can serve as a suitable automated alternative of handwritten generation in mutation testing and EMT. Finally, the fifth article in this special issue is titled Reducing the network overhead of user mobility-induced virtual machine migration in mobile edge computing.17 The authors in this work focus on mobile edge computing where the cloud resources are placed at the network edge. This not only helps the smartphones to extend their computation and storage capacity but also helps in achieving an improved latency. The authors in this work consider the cases of Cloud VM migrations from one edge cloud to another edge cloud owing to the user mobility and suggest improved VM migration algorithms to address the network overhead issues. The paper first details the network overheads involved in the VM migrations forced by mobility of smartphone users. The authors present a classification of user movement trajectory in the form of certain and uncertain moving trajectories. Based on these movement guidelines, the authors proposed two migration algorithms. M-Weight algorithm shows reduction in the network overhead for the VMs by assigning weights to different cloud data centers based on the latency requirements of the user. For uncertain trajectories, the authors propose an M-Predict algorithm to predict mobility. We see that the set of articles compiled in this special issue focus on various facets of intersection of cloud computing, IoT, and data analytics. We are thankful to the authors for presenting their latest contributions in the form of these high-quality articles. We also show our gratitude toward the reviewers contributing to this special issue in the form of their time to ascertain quality peer reviews. At the end, we hope that the articles presented in this special issue will add a great value to the current research directions in the target area, open up more research problems, and benefit the readers.

Summary Software‐defined networking (SDN) has evolved and brought an innovative paradigm shift in computer networks by utilizing a programmable software controller with open protocols. Network functions, previously served on dedicated hardware, have shifted to network function virtualization (NFV) that enabled functions to be virtualized and provisioned dynamically upon generic hardware. In addition to NFV, edge computing utilizes the edge resources close to end‐users, which can reduce the end‐to‐end service delay and the network traffic volume. Although these innovative technologies gained significant attention from both academia and industry, there are limited tools and simulation frameworks for the effectiveness evaluation in a repeatable and controllable manner. Furthermore, large‐scale experimental infrastructures are expensive to setup and difficult to maintain. Even if they are created, they are not available or accessible for the majority of researchers throughout the world. In this paper, we propose a framework for simulating NFV functionalities in both edge and cloud computing environments. In addition to the basic network functionalities supported by SDN in CloudSimSDN, we added new NFV features, such as virtualized network functions allocation, migration, and autoscaling with the support of corresponding network functionalities, such as flow load balancing, rerouting, and service function chaining (SFC) maintenance. We evaluated our simulation framework with autoscaling and placement policies for SFC in the integrated edge and cloud computing environments. The results demonstrate its effectiveness in measuring and evaluating the end‐to‐end delay, response time, resource utilization, network traffic, and power consumption with different algorithms in each scenario.

With the rapid development of cloud computing, various types of cloud services are available in the marketplace. However, it remains a significant challenge for cloud users to find suitable services for two major reasons: (1) Providers are unable to offer services in complete accordance with their declared Service Level Agreements, and (2) it is difficult for customers to describe their requirements accurately. To help users select cloud services efficiently, this article presents a Trust enabled Self-Learning Agent Model for service Matching (TSLAM). TSLAM is a multi-agent-based three-layered cloud service market model, in which different categories of agents represent the corresponding cloud entities to perform market behaviors. The unique feature of brokers is that they are not only the service recommenders but also the participants of market competition. We equip brokers with a learning module enabling them to capture implicit service demands and find user preferences. Moreover, a distributed and lightweight trust model is designed to help cloud entities make service decisions. Extensive experiments prove that TSLAM is able to optimize the cloud service matching process and compared to the state-of-the-art studies, TSLAM improves user satisfaction and the transaction success rate by at least 10%.

Performance of streaming applications are significantly impacted by the deployment decisions made at infrastructure level, i.e., number and configuration of resources allocated for each functional unit of the application. The current deployment practices are mostly platform-oriented, meaning that the deployment configuration is tuned to a static resource-set environment and thus is inflexible to use in cloud with an on-demand resource pool. In this paper, we propose P-Deployer, a deployment framework that enables streaming applications to run on IaaS clouds with satisfactory performance and minimal resource consumption. It achieves performance-oriented, cost-efficient and automated deployment by holistically optimizing the decisions of operator parallelization, resource provisioning, and task mapping. Using a Monitor-Analyze-Plan-Execute (MAPE) architecture, P-Deployer iteratively builds the connection between performance outcome and resource consumption through task profiling and models the deployment problem as a bin-packing variant. Extensive experiments using both synthetic and real-world streaming applications have shown the correctness and scalability of our approach, and demonstrated its superiority compared to platform-oriented methods in terms of resource cost.

Cloud Storage Providers (CSPs) offer geographically data stores providing several storage classes with different prices. An important problem facing by cloud users is how to exploit these storage classes to serve an application with a time-varying workload on its objects at minimum cost. This cost consists of residential cost (i.e., storage, Put and Get costs) and potential migration cost (i.e., network cost). To address this problem, we first propose the optimal offline algorithm that leverages dynamic and linear programming techniques with the assumption of available exact knowledge of workload on objects. Due to the high time complexity of this algorithm and its requirement for a priori knowledge, we propose two online algorithms that make a trade-off between residential and migration costs and dynamically select storage classes across CSPs. The first online algorithm is deterministic with no need of any knowledge of workload and incurs no more than 2γ-1 times of the minimum cost obtained by the optimal offline algorithm, where γ is the ratio of the residential cost in the most expensive data store to the cheapest one in either network or storage cost. The second online algorithm is randomized that leverages “Receding Horizon Control” (RHC) technique with the exploitation of available future workload information for w time slots. This algorithm incurs at most 1 + γ/w times the optimal cost. The effectiveness of the proposed algorithms is demonstrated through simulations using a workload synthesized based on characteristics of the Facebook workload.

The fog computing paradigm has drawn significant research interest as it focuses on bringing cloud-based services closer to Internet of Things (IoT) users in an efficient and timely manner. Most of the physical devices in the fog computing environment, commonly named fog nodes, are geographically distributed, resource constrained, and heterogeneous. To fully leverage the capabilities of the fog nodes, large-scale applications that are decomposed into interdependent Application Modules can be deployed in an orderly way over the nodes based on their latency sensitivity. In this article, we propose a latency-aware Application Module management policy for the fog environment that meets the diverse service delivery latency and amount of data signals to be processed in per unit of time for different applications. The policy aims to ensure applications’ Quality of Service (QoS) in satisfying service delivery deadlines and to optimize resource usage in the fog environment. We model and evaluate our proposed policy in an iFogSim-simulated fog environment. Results of the simulation studies demonstrate significant improvement in performance over alternative latency-aware strategies.

Cloud computing has been empowered with the introduction of Software-Defined Networking (SDN) which enabled dynamic controllability of cloud network infrastructure. Despite the increasing popularity of studies for joint resource optimization in the cloud environment with SDN technology, the realization is still limited for developing integrated management platform providing a simultaneous controllability of computing and networking infrastructures. In this paper, we propose SDCon, a practical platform developed on OpenStack and OpenDaylight to provide integrated manageability for both resources in cloud infrastructure. The platform can perform VM placement and migration, network flow scheduling and bandwidth allocation, real-time monitoring of computing and networking resources, and measuring power usage of the infrastructure with a single platform. We also propose a network topology aware VM placement algorithm for heterogeneous resource configuration (TOPO-Het) that consolidates the connected VMs into closely connected compute nodes to reduce the overall network traffic. The proposed algorithm is evaluated on SDCon and compared with the results from the state-of-the-art baselines. Results of the empirical evaluation with Wikipedia application show that the proposed algorithm can effectively improve the response time while reducing the total network traffic. It also shows the effectiveness of SDCon to manage both resources simultaneously.

Today, cloud computing applications are rapidly constructed by services belonging to different cloud providers and service owners. This work presents the inter-cloud elasticity framework, which focuses on cloud load balancing based on dynamic virtual machine reconfiguration when variations on load or on user requests volume are observed. We design a dynamic reconfiguration system, called inter-cloud load balancer (ICLB), that allows scaling up or down the virtual resources (thus providing automatized elasticity), by eliminating service downtimes and communication failures. It includes an inter-cloud load balancer for distributing incoming user HTTP traffic across multiple instances of inter-cloud applications and services and we perform dynamic reconfiguration of resources according to the real time requirements. The experimental analysis includes different topologies by showing how real-time traffic variation (using real world workloads) affects resource utilization and by achieving better resource usage in inter-cloud.

In the multi-tenant architecture of cloud computing, different applications have different requirements and priorities. In the case of network congestion within a data center, failure of transferring data on time may cause a significant performance degradation of an application. It may result in a severe failure for a critical application that needs a certain level of QoS satisfaction, therefore, efficient resource provisioning techniques are vital to ensure transferring the high-priority data over the other traffics even in network congestion. In Software-Defined Networking (SDN)-enabled clouds, this is possible by reconfiguring network flows dynamically adapting to network traffics. In this paper, we propose priority-aware resource placement algorithms considering both host and network resources. Our priority-aware VM allocation (PAVA) algorithm places VMs of the high-priority application to closely connected hosts to reduce the chance of network congestion caused by other tenants. The required bandwidth of a critical application is also guaranteed by bandwidth allocation with a configuration of priority queues on each networking device in a data center network managed by SDN controller. Our experiment results show that the combination of proposed approaches can allocate sufficient resources for high-priority applications to meet the application's QoS requirement in a multi-tenant cloud data center.

Energy consumption of Cloud data centers has been a major concern of many researchers, and one of the reasons for huge energy consumption of Clouds lies in the inefficient utilization of computing resources. Besides energy consumption, another challenge of data centers is the unexpected loads, which leads to the overloads and performance degradation. Compared with VM consolidation and Dynamic Voltage Frequency Scaling that cannot function well when the whole data center is overloaded, brownout has shown to be a promising technique to handle both overloads and energy consumption through dynamically deactivating application optional components, which are also identified as containers/microservices. In this work, we propose an integrated approach to manage energy consumption and brownout in container-based cloud data centers. We also evaluate our proposed scheduling policies with real traces in a prototype system. The results show that our approach reduces about 40, 20, and 10 percent energy than the approach without power-saving techniques, brownout-overbooking approach and auto-scaling approach, respectively, while ensuring Quality of Service.

In Software as a Service (SaaS)cloud marketplace, several functionally equivalent services tend to be available with different Quality of Service (QoS)values. For processing end-users multi-dimensional QoS and functional requirements, the application engineers are required to choose suitable services and optimize the service composition plans for each category of users. However, existing approaches for dynamic services composition tend to support execution plans that search for service provisions of equivalent functionalities with varying QoS or cost constraints to meet the tenants' QoS requirements or to dynamically respond to changes in QoS. These approaches tend to ignore the fact that multi-tenant execution plans need to provide variant execution plans, each offering a customized plan for a given tenant with its functionality, QoS and cost requirements. Henceforth, the dynamic selection and composition of multi-tenant service composition is a NP-hard dynamic multiobjective optimization problem. To address these challenges, we propose a novel multi-tenant middleware for dynamic service composition in the SaaS cloud. In particular, we present new encoding representation and fitness functions that model the service selection and composition as an evolutionary search. We incorporate our approach with two Multi-Objective Evolutionary Algorithms (MOEA), i.e., MOEA/D-STM and NSGA-II, to perform a comparative study. The experiment results show that the MOEA/D-STM outperforms NSGA-II in terms of quality of solutions and computation time.

Cloud computing is a suitable solution for professionals, companies, and institutions that need to have access to computational resources on demand. Clouds rely on proper management to provide such computational resources with adequate quality of service, which is established by Service Level Agreements (SLAs), to customers. In this context, cloud monitoring is a critical function to achieve such proper management. Cloud monitoring systems have to accomplish requirements to perform its functions properly, and currently, there are plenty of requirements which includes: timeliness, adaptability, comprehensiveness, and scalability. However, such requirements usually have mutual influence, which is positive or negative, among themselves, and it has prevented the development of complete cloud monitoring solutions. This paper presents a mathematical model to predict the mutual influence between timeliness and scalability, which is a step forward in cloud monitoring because it paves the way for the development of complete monitoring solutions. It complements our previous work that identified the monitoring parameters (e.g., frequency sampling, amount of monitoring data) that influence timeliness and scalability. Evaluations present the effectiveness of the mathematical model based on a comparison of the results provided by the mathematical model and the results obtained via simulation.

Apache Cassandra has emerged as one of the most widely adopted NoSQL databases. However, there is still a limited understanding on how to optimally operate Cassandra in the cloud using autoscaling methods, by which resources can be scaled up or down to reduce operational costs and meet service-level objectives (SLOs). To address this limitation, we present PAX, a partition-aware elastic resource management system for Apache Cassandra. PAX uses low-overhead query sampling and knowledge of the data-partitioning across the nodes to automatically adapt capacity in Cassandra clusters. Differently from existing autoscaling methods for Cassandra, which incur large acquisition times for new nodes, PAX exploits Cassandra's hinted handoff mechanism and a shared hints storage to minimize the time needed to acquire a node into the cluster. We propose a reactive and a proactive implementation of PAX and compare their performance against different workloads with varying intensities and item popularity distributions, finding that the proactive version significantly reduces SLO violations.

The cover image, by Rustem Dautov et al., is based on the Research Article Metropolitan Intelligent Surveillance Systems for Urban Areas by Harnessing IoT and Edge Computing Paradigms, https://doi.org/10.1002/spe.2586. Photo Credit: Rustem Dautov.

Summary Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber‐physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city‐wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off‐load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off‐loading pattern, where processing tasks are pushed “vertically” to the cloud.

Summary Large amount of data that is generated by Internet and enterprize applications are stored in the form of graphs. Graph processing systems are broadly used in enterprizes to process such data. With the rapid growth in mobile and social applications and complicated connections of Internet websites, massive concurrent operations need to be handled. On the other hand, the intrinsic structure and the size of real‐world graphs make distributed processing of graphs more challenging. Low balanced communication and computation, low preprocessing overhead, low memory footprint, and scalability should be offered by distributed graph analytics frameworks. Moreover, the effects of network factors such as bandwidth and traffic as well as monetary cost of processing such large‐scale graphs and the mutual impact of these elements have been less studied. To address these issues, we proposed two dynamic repartitioning algorithms that consider network factors, affecting public cloud environments to decrease the monetary cost of processing. A new classification of graph algorithms and processing is also introduced, which will be used to choose the best strategy for processing at any operation. We plugged these algorithms to our extended graph processing system (iGiraph) and compared them with those supported in other graph processing systems such as Giraph and Surfer on Australian National Cloud Infrastructure. We observed that up to 30% faster execution time, up to 50% network traffic decline, and more than 50% cost reduction are achieved by our algorithms compared to a framework such as the popular Giraph.

An economic and sustainable provision and use of cloud computing requires efficient decision support and policies for cloud providers and consumers, respectively.Therefore, the application of metaheuristics for solving complex real-world optimization problems in the area of cloud computing has become increasingly important.This special issue compiles a number of excellent contributions advancing the state-ofthe-art regarding the application of metaheuristics in cloud environments.Its first part covers optimization problems in the context of cloud resource management, in particular with respect to energy-aware resource provisioning, allocation, and consolidation schemes.The second part of this special issue is specifically focused on the consumers' perspective by presenting optimization approaches for supporting the selection of virtual machines as well as placement decisions.Overall, all articles present important insights from a methodological standpoint based on extensive computational experiments and benchmark studies.

Data stream management systems (DSMSs) are scalable, highly available, and fault-tolerant systems that aggregate and analyze real-time data in motion. To continuously perform analytics on the fly within the stream, state-of-the-art DSMSs host streaming applications as a set of interconnected operators, with each operator encapsulating the semantic of a specific operation. For parallel execution on a particular platform, these operators need to be appropriately replicated in multiple instances that split and process the workload simultaneously. Because the way operators are partitioned affects the resulting performance of streaming applications, it is essential for DSMSs to have a method to compare different operators and make holistic replication decisions to avoid performance bottlenecks and resource wastage. To this end, we propose a stepwise profiling approach to optimize application performance on a given execution platform. It automatically scales distributed computations over streams based on application features and processing power of provisioned resources and builds the relationship between provisioned resources and application performance metrics to evaluate the efficiency of the resulting configuration. Experimental results confirm that the proposed approach successfully fulfills its goals with minimal profiling overhead.

Cloud computing has become increasingly popular model for delivering applications hosted in large data centers as subscription oriented services. Hadoop is a popular system supporting the MapReduce function, which plays a crucial role in cloud computing. The resources required for executing jobs in a large data center vary according to the job type. In Hadoop, jobs are scheduled by default on a first-come-first-served basis, which may unbalance resource utilization. This paper proposes a job scheduler called the job allocation scheduler (JAS), designed to balance resource utilization. For various job workloads, the JAS categorizes jobs and then assigns tasks to a CPU-bound queue or an I/O-bound queue. However, the JAS exhibited a locality problem, which was addressed by developing a modified JAS called the job allocation scheduler with locality (JASL). The JASL improved the use of nodes and the performance of Hadoop in heterogeneous computing environments. Finally, two parameters were added to the JASL to detect inaccurate slot settings and create a dynamic job allocation scheduler with locality (DJASL). The DJASL exhibited superior performance than did the JAS, and data locality similar to that of the JASL.

The large-scale utilization of cloud computing services for hosting industrial/enterprise applications has led to the emergence of cloud service reliability as an important issue for both cloud service providers and users. To enhance cloud service reliability, two types of fault tolerance schemes, reactive and proactive, have been proposed. Existing schemes rarely consider the problem of coordination among multiple virtual machines (VMs) that jointly complete a parallel application. Without VM coordination, the parallel application execution results will be incorrect. To overcome this problem, we first propose an initial virtual cluster allocation algorithm according to the VM characteristics to reduce the total network resource consumption and total energy consumption in the data center. Then, we model CPU temperature to anticipate a deteriorating physical machine (PM). We migrate VMs from a detected deteriorating PM to some optimal PMs. Finally, the selection of the optimal target PMs is modeled as an optimization problem that is solved using an improved particle swarm optimization algorithm. We evaluate our approach against five related approaches in terms of the overall transmission overhead, overall network resource consumption, and total execution time while executing a set of parallel applications. Experimental results demonstrate the efficiency and effectiveness of our approach.

Mobile cloud computing is emerging as a promising approach to enrich user experiences at the mobile device end. Computation offloading in a heterogeneous mobile cloud environment has recently drawn increasing attention in research. The computation offloading decision making and tasks scheduling among heterogeneous shared resources in mobile clouds are becoming challenging problems in terms of providing global optimal task response time and energy efficiency. In this article, we address these two problems together in a heterogeneous mobile cloud environment as an optimization problem. Different from conventional distributed computing system scheduling problems, our joint offloading and scheduling optimization problem considers unique contexts of mobile clouds such as wireless network connections and mobile device mobility, which makes the problem more complex. We propose a context-aware mixed integer programming model to provide off-line optimal solutions for making the offloading decisions and scheduling the offloaded tasks among the shared computing resources in heterogeneous mobile clouds. The objective is to minimize the global task completion time (i.e., makespan). To solve the problem in real time, we further propose a deterministic online algorithm—the Online Code Offloading and Scheduling (OCOS) algorithm—based on the rent/buy problem and prove the algorithm is 2-competitive. Performance evaluation results show that the OCOS algorithm can generate schedules that have around two times shorter makespan than conventional independent task scheduling algorithms. Also, it can save around 30% more on makespan of task execution schedules than conventional offloading strategies, and scales well as the number of users grows.

Video streams, either in the form of Video On-Demand (VOD) or live streaming, usually have to be converted (i.e., transcoded) to match the characteristics of viewers' devices (e.g., in terms of spatial resolution or supported formats). Transcoding is a computationally expensive and time-consuming operation. Therefore, streaming service providers have to store numerous transcoded versions of a given video to serve various display devices. With the sharp increase in video streaming, however, this approach is becoming cost-prohibitive. Given the fact that viewers' access pattern to video streams follows a long tail distribution, for the video streams with low access rate, we propose to transcode them in an on-demand (i.e., lazy) manner using cloud computing services. The challenge in utilizing cloud services for on-demand video transcoding, however, is to maintain a robust QoS for viewers and cost-efficiency for streaming service providers. To address this challenge, in this paper, we present the Cloud-based Video Streaming Services (CVS2) architecture. It includes a QoS-aware scheduling component that maps transcoding tasks to the Virtual Machines (VMs) by considering the affinity of the transcoding tasks with the allocated heterogeneous VMs. To maintain robustness in the presence of varying streaming requests, the architecture includes a cost-efficient VM Provisioner component. The component provides a self-configurable cluster of heterogeneous VMs. The cluster is reconfigured dynamically to maintain the maximum affinity with the arriving workload. Simulation results obtained under diverse workload conditions demonstrate that CVS2 architecture can maintain a robust QoS for viewers while reducing the incurred cost of the streaming service provider by up to 85 percent.

Inter-cloud is an approach that facilitates scalable resource provisioning across multiple cloud infrastructures. In this paper, we focus on the performance optimization of Infrastructure as a Service (IaaS) using the meta-scheduling paradigm to achieve an improved job scheduling across multiple clouds. We propose a novel inter-cloud job scheduling framework and implement policies to optimize performance of participating clouds. The framework, named as Inter-Cloud Meta-Scheduling (ICMS), is based on a novel message exchange mechanism to allow optimization of job scheduling metrics. The resulting system offers improved flexibility, robustness and decentralization. We implemented a toolkit named “Simulating the Inter-Cloud” (SimIC) to perform the design and implementation of different inter-cloud entities and policies in the ICMS framework. An experimental analysis is produced for job executions in inter-cloud and a performance is presented for a number of parameters such as job execution, makespan, and turnaround times. The results highlight that the overall performance of individual clouds for selected parameters and configuration is improved when these are brought together under the proposed ICMS framework.

Big RDF (Resource Description Framework) graphs, which populate the emerging Semantic Web, are the core data structure of the so-called Big Web Data, the "natural" transposition of Big Data on the Web. Managing big RDF graphs is gaining momentum, essentially due to the fact that this task represents the "baseline operation" of fortunate Web big data analytics. Here, it is required to access, manage and process large-scale, million-node (big) RDF graphs, thus dealing with severe spatio-temporal complexity challenges. A possible solution to this problem is represented by the so-called MapReduce-model-based algorithms for managing big RDF graphs, which try to exploit the computational power offered by the MapReduce processing model in order to tame the complexity above. In this so-depicted scientific context, this paper provides a critical survey on MapReduce-based algorithms for managing big RDF graphs, with analysis of state-of-the-art proposals, paradigms and trends, along with a comprehensive overview of future research trends in the investigated scientific area.

The use of cloud computing, particularly of Infrastructure as a Service clouds, for the execution of largescale scientific workflows has been a topic of interest in recent years. These environments offer on-demand access to all of the infrastructure required for the deployment of workflows, allowing users to pay only for what they use. This leads to schedulers having to find a trade-off between two conflicting quality of service requirements: time and cost. The majority of research in this area has focused on developing scheduling algorithms that have as objective minimizing the infrastructure cost while meeting a deadline constraint. Few algorithms, however, have addressed the problem of minimizing the execution time of the workflow while meeting a budget constraint. This paper focuses on the latter case. We propose a budget-distribution algorithm that assigns a portion of the overall workflow budget to the individual tasks. This task-level budget then guides the dynamic scheduling process and is continuously refined to reflect any unexpected costs. When compared to the state-of-the-art algorithm, the performance evaluation results demonstrate that in 88% of the cases, our proposal achieves equal or better performance in terms of meeting the budget constraint and achieves lower execution times in 84% of the cases.

Large-scale data processing framework like Apache Spark is becoming more popular to process large amounts of data either in a local or a cloud deployed cluster. When an application is deployed in a Spark cluster, all the resources are allocated to it unless users manually set a limit on the available resources. In addition, it is not possible to impose any user-specific constraints and minimize the cost of running applications. In this paper, we present dSpark, a lightweight, pluggable resource allocation framework for Apache Spark. In dSpark, we have modelled the application completion time with respect to the number of executors and application input/iteration. This model is further used in our proposed resource allocation model where a deadlinebased, cost-efficient resource allocation scheme can be selected for any application. As opposed to the existing frameworks that focus more on modelling the number of VMs to use for an application, we have modelled both the application cost and completion time with respect to executors, hence providing a finegrained resource allocation scheme. In addition, users do not need to specify any application types in dSpark. We have evaluated our proposed framework through extensive experimentation, which shows significant performance benefits. The application completion time prediction model has a mean relative error (RE) less than 7% for different types of applications. Furthermore, we have shown that our proposed resource allocation model minimizes the cost of running applications and selects effective resource allocation schemes under varying user-specific deadlines.

Scheduling streaming applications in Data Stream Management Systems (DSMS) has been investigated for years. However, there lacks an intelligent system that is capable of monitoring application execution, modelling its resource usages, and then adjusting the scheduling plan under different sizes of inputs without requiring users' intervention. In this paper, we model the scheduling problem as a bin-packing variant and propose a heuristic-based algorithm to solve it with minimised inter-node communication. We also implement the D-Storm prototype to validate the efficacy and efficiency of our scheduling algorithm, by extending the Apache Storm framework into a self-adaptive MAPE (Monitoring, Analysis, Planning, Execution) architecture. The evaluation carried out on both synthetic and realistic applications proves that D-Storm outperforms the existing resource-aware scheduler and the default Storm scheduler by at least 16.25% in terms of the inter-node traffic reduction and yields a significant amount of resource savings through consolidation.

Apache Storm is a fault-tolerant, distributed inmemory computation system for processing large volumes of high-velocity data in real-time. As an integral part of the fault-tolerance mechanism, Storm's state management is achieved by a checkpointing framework, which commits states regularly and recovers lost states from the latest checkpoint. However, this method involves a remote data store for state preservation and access, resulting in significant overheads to the performance of error-free execution.In this paper, we propose E-Storm, a replication-based state management system that actively maintains multiple state backups on different worker nodes. We build a prototype on top of Storm by extending it with monitoring and recovery modules to support inter-task state transfer whenever needed. The experiments carried out on synthetic and real-world streaming applications confirm that E-Storm outperforms the existing checkpointing method in terms of the resulting application performance, obtaining as much as 9.44 times throughput improvement while reducing the application latency down to 9.8%.

Recent developments of the enhanced hardware on mobile devices such as quad-core CPUs and various sensors have made it possible to build a powerful heterogeneous mobile cloud offloading service that consists of a mobile ad-hoc cloud, nearby servers and public cloud services. However, the availability and mobility management of mobile devices in the network can significantly hinder the performance of mobile cloud systems due to the frequent system faults caused by dynamic changes, and prevent applications from offloading to mobile ad-hoc networks. In order to improve the mobile cloud service reliability, we propose a group based fault tolerant mechanism GFT-mCloud that classifies mobile devices into groups based on its processing capacity, mobility, and reliability. Different fault tolerance techniques are then devised adaptively based on the task offloading schedules and the specific group of machines it's offloaded. GFT-mCloud is designed as a standalone module that can work with existing mobile cloud code offloading systems. Extensive experiments have been conducted to evaluate the proposed mechanism. The results show that our fault tolerant mechanism is able to outperform conventional fault tolerant algorithms in the mobile cloud offloading environment.

Summary Computational demand in data centers is increasing because of the growing popularity of Cloud applications. However, data centers are becoming unsustainable in terms of power consumption and growing energy costs so Cloud providers have to face the major challenge of placing them on a more scalable curve. Also, Cloud services are provided under strict Service Level Agreement conditions, so trade‐offs between energy and performance have to be taken into account. Techniques as Dynamic Voltage and Frequency Scaling (DVFS) and consolidation are commonly used to reduce the energy consumption in data centers, although they are applied independently and their effects on Quality of Service are not always considered. Thus, understanding the relationship between power, DVFS, consolidation, and performance is crucial to enable energy‐efficient management at the data center level. In this work, we propose a DVFS policy that reduces power consumption while preventing performance degradation, and a DVFS‐aware consolidation policy that optimizes consumption, considering the DVFS configuration that would be necessary when mapping Virtual Machines to maintain Quality of Service. We have performed an extensive evaluation on the CloudSim toolkit using real Cloud traces and an accurate power model based on data gathered from real servers. Our results demonstrate that including DVFS awareness in workload management provides substantial energy savings of up to 41.62% for scenarios under dynamic workload conditions. These outcomes outperforms previous approaches, that do not consider integrated use of DVFS and consolidation strategies.

Summary The high volume of monitoring information generated by large‐scale cloud infrastructures poses a challenge to the capacity of cloud providers in detecting anomalies in the infrastructure. Traditional anomaly detection methods are resource‐intensive and computationally complex for training and/or detection, what is undesirable in very dynamic and large‐scale environment such as clouds. Isolation‐based methods have the advantage of low complexity for training and detection and are optimized for detecting failures. In this work, we explore the feasibility of Isolation Forest, an isolation‐based anomaly detection method, to detect anomalies in large‐scale cloud data centers. We propose a method to code time‐series information as extra attributes that enable temporal anomaly detection and establish its feasibility to adapt to seasonality and trends in the time‐series and to be applied online and in real‐time.

Summary Energy consumption and its associated costs represent a huge part of cloud providers' operational costs. In this study, we explore how much energy cost savings can be made knowing the future level of renewable energy (solar/wind) available in data centers. Since renewable energy sources have intermittent nature, we take advantage of migrating virtual machines to the nearby data centers with excess renewable energy. In particular, we first devise an optimal offline algorithm with full future knowledge of renewable level in the system. Since in practice, accessing long‐term and exact future knowledge of renewable energy level is not feasible, we propose two online deterministic algorithms, one with no future knowledge called deterministic and one with limited knowledge of the future renewable availability called future‐aware. We show that the deterministic and future‐aware algorithms are 1+1/ s and 1+1/ s − ω / s . T m competitive in comparison to the optimal offline algorithm, respectively, where s is the network to the brown energy cost, ω is the look‐ahead window‐size, and T m is the migration time. The effectiveness of the proposed algorithms is analyzed through extensive simulation studies using real‐world traces of meteorological data and Google cluster workload.

Summary The cloud infrastructures provide a suitable environment for the execution of large‐scale scientific workflow application. However, it raises new challenges to efficiently allocate resources for the workflow application and also to meet the user's quality of service requirements. In this paper, we propose an adaptive penalty function for the strict constraints compared with other genetic algorithms. Moreover, the coevolution approach is used to adjust the crossover and mutation probability, which is able to accelerate the convergence and prevent the prematurity. We also compare our algorithm with baselines such as Random, particle swarm optimization, Heterogeneous Earliest Finish Time, and genetic algorithm in a WorkflowSim simulator on 4 representative scientific workflows. The results show that it performs better than the other state‐of‐the‐art algorithms in the criterion of both the deadline‐constraint meeting probability and the total execution cost.

Summary Managed by an auto‐scaler in the clouds, applications may still be overloaded by sudden flash crowds or resource failures as the auto‐scaler takes time to make scaling decisions and provision resources. With more cloud providers building geographically dispersed data centers, applications are commonly deployed in multiple data centers to better serve customers worldwide. In this case, instead of sufficiently over‐provisioning each data center to prepare for occasional overloads, it is more cost‐efficient to over‐provision each data center a small amount of capacity and to balance the extra load among them when resources in any data center are suddenly saturated. In this paper, we present a decentralized system that timely detects short‐term overload situations and autonomously handles them using geographical load balancing and admission control to minimize the resulted performance degradation. Our approach also includes a new algorithm that optimally distributes the excessive load to remote data centers causing minimum increase of overall response times. We developed a prototype and evaluated it on Amazon Web Services. The results show that our approach is able to maintain acceptable quality of service while greatly increase the number of requests served during overloading periods.

Summary Large‐scale scientific problems are often modeled as workflows. The ever‐growing data and compute requirements of these applications has led to extensive research on how to efficiently schedule and deploy them in distributed environments. The emergence of the latest distributed systems paradigm, cloud computing, brings with it tremendous opportunities to run scientific workflows at low costs without the need of owning any infrastructure. It provides a virtually infinite pool of resources that can be acquired, configured, and used as needed and are charged on a pay‐per‐use basis. However, along with these benefits come numerous challenges that need to be addressed to generate efficient schedules. This work identifies these challenges and studies existing algorithms from the perspective of the scheduling models they adopt as well as the resource and application model they consider. A detailed taxonomy that focuses on features particular to clouds is presented, and the surveyed algorithms are classified according to it. In this way, we aim to provide a comprehensive understanding of existing literature and aid researchers by providing an insight into future directions and open issues.

Summary The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure, and be charged on pay‐per‐use basis. However, cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements violations. So as to achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be nondeterministic polynomial time (NP)‐hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to hosts in infrastructure clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated, and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.

Summary Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real‐time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim , to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.

Summary Hadoop distributed file system (HDFS) and MapReduce model have become popular technologies for large‐scale data organization and analysis. Existing model of data organization and processing in Hadoop using HDFS and MapReduce are ideally tailored for search and data parallel applications, for which there is no need of data dependency with its neighboring/adjacent data. However, many scientific applications such as image mining, data mining, knowledge data mining, and satellite image processing are dependent on adjacent data for processing and analysis. In this paper, we identify the requirements of the overlapped data organization and propose a two‐phase extension to HDFS and MapReduce programming model, called XHAMI, to address them. The extended interfaces are presented as APIs and implemented in the context of image processing application domain. We demonstrated effectiveness of XHAMI through case studies of image processing functions along with the results. Although XHAMI has little overhead in data storage and input/output operations, it greatly enhances the system performance and simplifies the application development process. Our proposed system, XHAMI, works without any changes for the existing MapReduce models and can be utilized by many applications where there is a requirement of overlapped data. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Containers are increasingly gaining popularity and becoming one of the major deployment models in cloud environments. To evaluate the performance of scheduling and allocation policies in containerized cloud data centers, there is a need for evaluation environments that support scalable and repeatable experiments. Simulation techniques provide repeatable and controllable environments, and hence, they serve as a powerful tool for such purpose. This paper introduces ContainerCloudSim , which provides support for modeling and simulation of containerized cloud computing environments. We developed a simulation architecture for containerized clouds and implemented it as an extension of CloudSim. We described a number of use cases to demonstrate how one can plug in and compare their container scheduling and provisioning policies in terms of energy efficiency and SLA compliance. Our system is highly scalable as it supports simulation of large number of containers, given that there are more containers than virtual machines in a data center. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Because of the rapid increasing of malware attacks on the Internet of Things in recent years, it is critical for resource‐constrained devices to guard against potential risks. The traditional host‐based security solution becomes puffy and inapplicable with the development of malware attacks. Moreover, it is hard for the cloud‐based security solution to achieve both the high performance detection and the data privacy protection simultaneously. This paper proposes a cloud‐based anti‐malware system, called CloudEyes, which provides efficient and trusted security services for resource‐constrained devices. For the cloud server, CloudEyes presents suspicious bucket cross‐filtering, a novel signature detection mechanism based on the reversible sketch structure, which provides retrospective and accurate orientations of malicious signature fragments. For the client, CloudEyes implements a lightweight scanning agent which utilizes the digest of signature fragments to dramatically reduce the range of accurate matching. Furthermore, by transmitting sketch coordinates and the modular hashing, CloudEyes guarantees both the data privacy and low‐cost communications. Finally, we evaluate the performance of CloudEyes by utilizing both the campus suspicious traffic and normal files. The results demonstrate that the mechanisms in CloudEyes are effective and practical, and our system can outperform other existing systems with less time and communication consumption. Copyright © 2016 John Wiley & Sons, Ltd.

With the advent of cloud computing and the availability of data collected from increasingly powerful scientific instruments, workflows have become a prevailing mean to achieve significant scientific advances at an increased pace. Scheduling algorithms are crucial in enabling the efficient automation of these large-scale workflows, and considerable effort has been made to develop novel heuristics tailored for the cloud resource model. The majority of these algorithms focus on coarse-grained billing periods that are much larger than the average execution time of individual tasks. Instead, our work focuses on emerging finer-grained pricing schemes (e.g., per-minute billing) that provide users with more flexibility and the ability to reduce the inherent wastage that results from coarser-grained ones. We propose a scheduling algorithm whose objective is to optimize a workflow’s execution time under a budget constraint; quality of service requirement that has been overlooked in favor of optimizing cost under a deadline constraint. Our proposal addresses fundamental challenges of clouds such as resource elasticity, abundance, and heterogeneity, as well as resource performance variation and virtual machine provisioning delays. The simulation results demonstrate our algorithm’s responsiveness to environmental uncertainties and its ability to generate high-quality schedules that comply with the budget constraint while achieving faster execution times when compared to state-of-the-art algorithms.

Web service composition concerns the building of new value added services by integrating the sets of existing web services. Due to the seamless proliferation of web services, it becomes difficult to find a suitable web service that satisfies the requirements of users during web service composition. This paper systematically reviews existing research on QoS-aware web service composition using computational intelligence techniques (published between 2005 and 2015). This paper develops a classification of research approaches on computational intelligence based QoS-aware web service composition and describes future research directions in this area. In particular, the results of this study confirms that new meta-heuristic algorithms have not yet been applied for solving QoS-aware web services composition.

State-of-the-art cloud simulators in use today are limited in the number of features they provide, lack real network communication models, and do not provide extensive Graphical User Interface (GUI) to support developers and researchers to extend the behavior of the cloud environment. We propose CloudNetSim++, a comprehensive packet level simulator that enables simulation of cloud environments. CloudNetSim++ can be used to evaluate a wide spectrum of cloud components, such as processing elements, storage, networking, Service Level Agreement (SLA), scheduling algorithms, fine grained energy consumption, and VM consolidation algorithms. CloudNetSim++ offers extendibility, which means that the developers and researchers can easily incorporate own algorithms for scheduling, workload consolidation, VM migration, and SLA agreement. The simulation environment of CloudNetSim++ offers a rich GUI that provides a high level view of distributed data centers connected with various network topologies. The package also includes an energy computation module that provides a fine grained analysis of energy consumed by each component. This paper shows the flexibility and effectiveness of CloudNetSim++ through experimental results demonstrated using real-world data center workloads. Moreover, to demonstrate the correctness of CloudNetSim++, we performed formal modeling, analysis, and verification using High-level Petri Nets, Satisfiability Modulo Theories (SMT), and Z3 solver.

Mobile cloud computing (MCC) has become a significant paradigm for bringing the benefits of cloud computing to mobile devices' proximity. Service availability along with performance enhancement and energy efficiency are primary targets in MCC. This paper proposes a code offloading framework, called mCloud, which consists of mobile devices, nearby cloudlets and public cloud services, to improve the performance and availability of the MCC services. The effect of the mobile device context (e.g., network conditions) on offloading decisions is studied by proposing a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and appropriate cloud resources for offloading. We also investigate failure detection and recovery policies for our mCloud system. We explain in details the design and implementation of the mCloud prototype framework. We conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm are able to provide decisions on selecting wireless medium and cloud resources based on different context of the mobile devices, and achieve significant reduction on makespan and energy, with the improved service availability when compared with existing offloading schemes.

With rapid adoption of the cloud computing model, many enterprises have begun deploying cloud-based services. Failures of virtual machines (VMs) in clouds have caused serious quality assurance issues for those services. VM replication is a commonly used technique for enhancing the reliability of cloud services. However, when determining the VM redundancy strategy for a specific service, many state-of-the-art methods ignore the huge network resource consumption issue that could be experienced when the service is in failure recovery mode. This paper proposes a redundant VM placement optimization approach to enhancing the reliability of cloud services. The approach employs three algorithms. The first algorithm selects an appropriate set of VM-hosting servers from a potentially large set of candidate host servers based upon the network topology. The second algorithm determines an optimal strategy to place the primary and backup VMs on the selected host servers with k-fault-tolerance assurance. Lastly, a heuristic is used to address the task-to-VM reassignment optimization problem, which is formulated as finding a maximum weight matching in bipartite graphs. The evaluation results show that the proposed approach outperforms four other representative methods in network resource consumption in the service recovery stage.

Cloud data centers consume a large amount of energy that leads to a high carbon footprint. Taking into account a carbon tax imposed on the emitted carbon makes energy and carbon cost play a major role in data centers' operational costs. To address this challenge, we investigate parameters that have the biggest effect on energy and carbon footprint cost to propose more efficient VM placement approaches. We formulate the total energy cost as a function of the energy consumed by servers plus overhead energy, which is computed through power usage effectiveness (PUE) metric as a function of IT load and outside temperature. Furthermore, we consider that data center sites have access to renewable energy sources. This helps to reduce their reliance on “brown” electricity delivered by off-site providers, which is typically drawn from polluting sources. We then propose multiple VM placement approaches to evaluate their performance and identify the parameters with the greatest impact on the total renewable and brown energy consumption, carbon footprint, and cost. The results show that the approach which considers dynamic PUE, renewable energy sources, and changes in the total energy consumption outperforms the others while still meeting cloud users' service level agreements.

Power management of cloud data centers has received great attention from industry and academia as they are expensive to operate due to their high energy consumption. While hosts are dominant to consume electric power, networks account for 10 to 20 percent of the total energy costs in a data center. Resource overbooking is one way to reduce the usage of active hosts and networks by placing more requests to the same amount of resources. Network resource overbooking can be facilitated by Software Defined Networking (SDN) that can consolidate traffics and control Quality of Service (QoS) dynamically. However, the existing approaches employ fixed overbooking ratio to decide the amount of resources to be allocated, which in reality may cause excessive Service Level Agreements (SLA) violation with workloads being unpredictable. In this paper, we propose dynamic overbooking strategy which jointly leverages virtualization capabilities and SDN for VM and traffic consolidation. With the dynamically changing workload, the proposed strategy allocates more precise amount of resources to VMs and traffics. This strategy can increase overbooking in a host and network while still providing enough resources to minimize SLA violations. Our approach calculates resource allocation ratio based on the historical monitoring data from the online analysis of the host and network utilization without any pre-knowledge of workloads. We implemented it in simulation environment in large scale to demonstrate the effectiveness in the context of Wikipedia workloads. Our approach saves energy consumption in the data center while reducing SLA violations.

Market oriented methodologies have been extensively used for solving dynamic allocation problems in online systems including the Cloud. Despite their extensive use, very little has been known about their security against market specific security threats (e.g. monopoly, shill bidding, etc.). This work follows an experimental driven approach for: (i) promoting the development of threat-aware, market-oriented Clouds, (ii) exposing existing market specific security vulnerabilities and (iii) developing security mechanisms for online markets. We show that the designs of existing market-oriented Clouds are limited when facing market specific attacks and when thwarting malicious bidders and sellers from manipulating auction mechanisms for personal gains. Furthermore, we show that our solutions can effectively resolve market specific attacks and secure bidders, sellers and auctioning mechanisms in the context of Cloud.

Large-scale graph analytics has gained attention during the past few years. As the world is going to be more connected by appearance of new technologies and applications such as social networks, Web portals, mobile devices, Internet of things, etc, a huge amount of data are created and stored every day in the form of graphs consisting of billions of vertices and edges. Many graph processing frameworks have been developed to process these large graphs since Google introduced its graph processing framework called Pregel in 2010. On the other hand, cloud computing which is a new paradigm of computing that overcomes restrictions of traditional problems in computing by enabling some novel technological and economical solutions such as distributed computing, elasticity and pay-as-you-go models has improved service delivery features. In this paper, we present iGiraph, a cost-efficient Pregel-like graph processing framework for processing large-scale graphs on public clouds. iGiraph uses a new dynamic re-partitioning approach based on messaging pattern to minimize the cost of resource utilization on public clouds. We also present the experimental results on the performance and cost effects of our method and compare them with basic Giraph framework. Our results validate that iGiraph remarkably decreases the cost and improves the performance by scaling the number of workers dynamically.

Video streams, either in form of on-demand streaming or live streaming, usually have to be converted (i.e., transcoded) based on the characteristics of clients' devices (e.g., spatial resolution, network bandwidth, and supported formats). Transcoding is a computationally expensive and time-consuming operation, therefore, streaming service providers currently store numerous transcoded versions of the same video to serve different types of client devices. Due to the expense of maintaining and upgrading storage and computing infrastructures, many streaming service providers (e.g., Netflix) recently are becoming reliant on cloud services. However, the challenge in utilizing cloud services for video transcoding is how to deploy cloud resources in a cost-efficient manner without any major impact on the quality of video streams. To address this challenge, in this paper, we present the Cloud-based Video Streaming Service (CVSS) architecture to transcode video streams in an on-demand manner. The architecture provides a platform for streaming service providers to utilize cloud resources in a cost-efficient manner and with respect to the Quality of Service (QoS) demands of video streams. In particular, the architecture includes a QoS-aware scheduling method to efficiently map video streams to cloud resources, and a cost-aware dynamic (i.e., elastic) resource provisioning policy that adapts the resource acquisition with respect to the video streaming QoS demands. Simulation results based on realistic cloud traces and with various workload conditions, demonstrate that the CVSS architecture can satisfy video streaming QoS demands and reduces the incurred cost of stream providers up to 70%.

Energy usage of large-scale data centers has become a major concern for cloud providers. There has been an active effort in techniques for the minimization of the energy consumed in the data centers. However, most approaches lack the analysis and application of real cloud backend traces. In existing approaches, the variation of cloud workloads and its effect on the performance of the solutions are not investigated. Furthermore, the focus of existing approaches is on virtual machine migration and placement algorithms, with little regard to tailoring virtualmachine configuration to workload characteristics, which can further reduce the energy consumption and resource wastage in a typical data center. To address these weaknesses and challenges, we propose a new architecture for cloud resource allocation that maps groups of tasks to customized virtual machine types. This mapping is based on the task usage patterns obtained from the analysis of the historical data extracted from utilization traces. In our work, the energy consumption is decreased via efficient resource allocation based on the actual resource usage of tasks. Experimental results show that, when resources are allocated based on the discovered usage patterns, significant energy saving can be achieved.

Summary Designing dynamic pricing mechanisms that efficiently price resources in line with a provider's profit maximization goal is a key challenge in cloud computing environments. Despite the large volume of research published on this topic, there is no publicly available software system implementing dynamic pricing for Infrastructure as a Service cloud spot markets. This paper presents the implementation of a framework calledSpot instance pricing as a Service(SipaaS) that supports an auction mechanism to price and allocate virtual machine instances. SipaaS is an open‐source project offering a set of web services to price and sell virtual machine instances in a spot market resembling the Amazon EC2 spot instances. Cloud providers, who aim at utilizing SipaaS, should install add‐ons in their existing platform to make use of the framework. As an instance, we provide an extension to theHorizon– the OpenStack dashboard project – to employ SipaaS web services and to add a spot market environment to OpenStack. To validate and evaluate the system, we conducted an experimental study with a group of 10 users utilizing the provided spot market in a real environment. Results show that the system performs reliably in a practical test environment. Copyright © 2015 John Wiley & Sons, Ltd.

Summary Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and resource allocation methodology that can provision resources in advance, thereby minimizing the virtual machine downtime required for resource provisioning. In this paper, we present a dynamic resource demand prediction and allocation framework in multi‐tenant service clouds. The novel contribution of our proposed framework is that it classifies the service tenants as per whether their resource requirements would increase or not; based on this classification, our framework prioritizes prediction for those service tenants in which resource demand would increase, thereby minimizing the time needed for prediction. Furthermore, our approach adds the service tenants to matched virtual machines and allocates the virtual machines to physical host machines using a best‐fit heuristic approach. Performance results demonstrate how our best‐fit heuristic approach could efficiently allocate virtual machines to hosts so that the hosts are utilized to their fullest capacity. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Advances in information technology and its widespread growth in several areas of business, engineering, medical, and scientific studies are resulting in information/data explosion. Knowledge discovery and decision‐making from such rapidly growing voluminous data are a challenging task in terms of data organization and processing, which is an emerging trend known as big data computing, a new paradigm that combines large‐scale compute, new data‐intensive techniques, and mathematical models to build data analytics. Big data computing demands a huge storage and computing for data curation and processing that could be delivered from on‐premise or clouds infrastructures. This paper discusses the evolution of big data computing, differences between traditional data warehousing and big data, taxonomy of big data computing and underpinning technologies, integrated platform of big data and clouds known as big data clouds, layered architecture and components of big data cloud, and finally open‐technical challenges and future directions. Copyright © 2015 John Wiley & Sons, Ltd.

Cloud environments offer low-cost computing resources as a subscription-based service. These resources are elastically scalable and dynamically provisioned. Furthermore, cloud providers have also pioneered new pricing models like spot instances that are cost-effective. As a result, scientific workflows are increasingly adopting cloud computing. However, spot instances are terminated when the market price exceeds the users bid price. Likewise, cloud is not a utopian environment. Failures are inevitable in such large complex distributed systems. It is also well studied that cloud resources experience fluctuations in the delivered performance. These challenges make fault tolerance an important criterion in workflow scheduling. This article presents an adaptive, just-in-time scheduling algorithm for scientific workflows. This algorithm judiciously uses both spot and on-demand instances to reduce cost and provide fault tolerance. The proposed scheduling algorithm also consolidates resources to further minimize execution time and cost. Extensive simulations show that the proposed heuristics are fault tolerant and are effective, especially under short deadlines, providing robust schedules with minimal makespan and cost.

Dynamic forms of resource pricing have recently been introduced by cloud providers that offer Infrastructure as a Service (IaaS) capabilities in order to maximize profits and balance resource supply and demand. The design of a mechanism that efficiently prices perishable cloud resources in line with a provider’s profit maximization goal remains an open research challenge, however. In this article, we propose the Online Extended Consensus Revenue Estimate mechanism in the setting of a recurrent, multiunit and single price auction for IaaS cloud resources. The mechanism is envy-free, has a high probability of being truthful, and generates a near optimal profit for the provider. We combine the proposed auction design with a scheme for dynamically calculating reserve prices based on data center Power Usage Effectiveness (PUE) and electricity costs. Our simulation-based evaluation of the mechanism demonstrates its effectiveness under a broad variety of market conditions. In particular, we show how it improves on the classical uniform price auction, and we investigate the value of prior knowledge on the execution time of virtual machines for maximizing profit. We also developed a system prototype and conducted a small-scale experimental study with a group of 10 users that confirms the truthfulness property of the mechanism in a real test environment.

Service-oriented architecture offers a flexible paradigm for information flow among collaborating organizations. As information moves out of an organization boundary, various security concerns may arise, such as confidentiality, integrity, and authenticity that needs to be addressed. Moreover, verifying the correctness of the communication protocol is also an important factor. This paper focuses on the formal verification of the xDAuth protocol, which is one of the prominent protocols for identity management in cross domain scenarios. We have modeled the information flow of xDAuth protocol using high-level Petri nets to understand the protocol information flow in a distributed environment. We analyze the rules of information flow using Z language, while Z3 SMT solver is used for the verification of the model. Our formal analysis and verification results reveal the fact that the protocol fulfills its intended purpose and provides the security for the defined protocol specific properties, e.g., secure secret key authentication, and Chinese wall security policy and secrecy specific properties, e.g., confidentiality, integrity, and authenticity.

The Earth Mover's Distance (EMD) similarity join has a number of important applications such as near duplicate image retrieval and distributed based pattern analysis. However, the computational cost of EMD is super cubic and consequently the EMD similarity join operation is prohibitive for datasets of even medium size. We propose to employ the Hadoop platform to speed up the operation. Simply porting the state-of-the-art metric distance similarity join algorithms to Hadoop results in inefficiency because they involve excessive distance computations and are vulnerable to skewed data distributions. We propose a novel framework, named HEADS-JOIN, which transforms data into the space of EMD lower bounds and performs pruning and partitioning at a low cost because computing these EMD lower bounds has constant or linear complexity. We investigate both range and top-k joins, and design efficient algorithms on three popular Hadoop computation paradigms, i.e., MapReduce, Bulk Synchronous Parallel, and Spark. We conduct extensive experiments on both real and synthetic datasets. The results show that HEADS-JOIN outperforms the state-of-the-art metric similarity join technique, i.e., Quickjoin, by up to an order of magnitude and scales out well.

It is common for cloud data centers meeting unexpected loads like request bursts, which may lead to overloaded situation and performance degradation. Dynamic Voltage Frequency Scaling and VM consolidation have been proved effective to manage overloads. However, they cannot function when the whole data center is overloaded. Brownout provides a promising direction to avoid overloads through configuring applications to temporarily degrade user experience. Additionally, brownout can also be applied to reduce data center energy consumption. As a complementary option for Dynamic Voltage Frequency Scaling and VM consolidation, our combined brownout approach reduces energy consumption through selectively and dynamically deactivating application optional components, which can also be applied to self-contained microservices. The results show that our approach can save more than 20 percent energy consumption and there are trade-offs between energy saving and discount offered to users.

Cloud applications are typically composed of multiple cloud service components communicating with each other through web service interfaces, where each component fulfills specified functionalities. Lack of effective fault tolerance scheme is one of major obstacles for enhancing availability and efficiency of complex and aging cloud application systems. In this paper, we propose a holistic software rejuvenation based fault tolerance scheme for cloud applications, which contains three indispensible parts: adaptive failure detection, aging degree evaluation, and checkpoint with trace replay based component rejuvenation. Through a preliminary and qualitative evaluation, it shows that our new fault tolerance scheme brings promising improvement on the availability of cloud applications.

Geographically dispersed cloud data centers (DCs) enable web application providers to improve their services' response time and availability by deploying application replicas in multiple DCs. To allow applications requiring strong consistency to be deployed in multiple clouds, industry and academia have developed various scalable database systems that can guarantee strong inter-DC consistency with alleviated network overhead. For applications using these database systems, it is essential to take both the network latencies to the end users and the communication overhead of the databases into account when selecting the hosting DCs. In this paper, we study how to identify the satisfactory deployment plan (hosting DCs and request routing) considering SLO satisfaction, migration cost, and operational cost for applications using these databases. The proposed approach involves two steps. First, it searches the deployment plan with minimum amount of SLO violations using genetic algorithm when the application is first migrated to the clouds. Then it continuously optimizes the deployment in a certain time interval according to the changing workload and the current deployment plan. We illustrate how our approach works for the applications using two databases (Cassandra and Galera Cluster), and demonstrate the effectiveness of our approach through simulation studies using settings of two example applications (TPC-W and Twissandra). Our solution is extensible to applications using other database systems that have similar properties.

Mobile cloud computing (MCC) has drawn significant research attention as the popularity and capability of mobile devices have been improved in recent years. In this paper, we propose a prototype MCC offloading system that considers multiple cloud resources such as mobile ad-hoc network, cloudlet and public clouds to provide an adaptive MCC service. We propose a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and which potential cloud resources as the offloading location based on the device context. We also conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm can select suitable wireless medium and cloud resources based on different context of the mobile devices, and achieve significant performance improvement.

Nowadays, data centers consume about 2% of the worldwide energy production, originating more than 43 million tons of CO2 per year. Cloud providers need to implement an energy-efficient management of physical resources in order to meet the growing demand for their services and ensure minimal costs. From the application-framework viewpoint, Cloud workloads present additional restrictions as 24/7 availability, and SLA constraints among others. Also, workload variation impacts on the performance of two of the main strategies for energy-efficiency in Cloud data centers: Dynamic Voltage and Frequency Scaling (DVFS) and Consolidation. Our work proposes two contributions: 1) a DVFS policy that takes into account the trade-offs between energy consumption and performance degradation; 2) a novel consolidation algorithm that is aware of the frequency that would be necessary when allocating a Cloud workload in order to maintain QoS. Our results demonstrate that including DVFS awareness in workload management provides substantial energy savings of up to 39.14% for scenarios under dynamic workload conditions.

Software-Defined Networking not only addresses the shortcoming of traditional network technologies in dealing with frequent and immediate changes in cloud data centers but also made network resource management open and innovation-friendly. To further accelerate the innovation pace, accessible and easy-to-learn testbeds are required which estimate and measure the performance of network and host capacity provisioning approaches simultaneously within a data center. This is a challenging task and is often costly if accomplished in a physical environment. Thus, a lightweight and scalable simulation environment is necessary to evaluate the network allocation capacity policies while avoiding such a complicated and expensive facility. This paper introduces CloudSimSDN, a simulation framework for SDN-enabled cloud environments based on CloudSim. This paper develops and presents the overall architecture and features of the framework and provides several use cases. Moreover, we empirically validate the accuracy and effectiveness of CloudSimSDN through a number of simulations of a cloud-based three-tier web application.

The emergence of cloud computing has made dynamic provisioning of elastic capacity to applications on-demand. Cloud data centers contain thousands of physical servers hosting orders of magnitude more virtual machines that can be allocated on demand to users in a pay-as-you-go model. However, not all systems are able to scale up by just adding more virtual machines. Therefore, it is essential, even for scalable systems, to project workloads in advance rather than using a purely reactive approach. Given the scale of modern cloud infrastructures generating real time monitoring information, along with all the information generated by operating systems and applications, this data poses the issues of volume, velocity, and variety that are addressed by Big Data approaches. In this paper, we investigate how utilization of Big Data analytics helps in enhancing the operation of cloud computing environments. We discuss diverse applications of Big Data analytics in clouds, open issues for enhancing cloud operations via Big Data analytics, and architecture for anomaly detection and prevention in clouds along with future research directions.

Cloud computing has emerged as a new computing paradigm which has revolutionized the IT industry. It has particularly transformed the licensing of software products which are now being offered as a Service on pay-as-you-go basis. This has tremendously increased the complexity for software providers as they now have to not only manage their resources on which software are hosted but also they need to provide expected Quality of Service for customers. The Quality of Service (QoS) required by customers is guaranteed using a legal document SLA (Service Level Agreement). Current, resource management systems do not cater to the needs of a Software as a Service (SaaS) provider who requires to provide flexible and low cost services while not affecting their profit and market share. Most of them focus either at infrastructure level or at platform level. This work fills this gap by proposing a novel SLA based resource management system designed after analysing requirements of SaaS in Clouds. The proposed system is implemented using latest technologies and can scale in and out depending on updates in the user demand. We present the architectural design and evaluate the implementation with a real case study in a real Cloud environment.

Scientific workflows are used to process vast amounts of data and to conduct large-scale experiments and simulations. They are time consuming and resource intensive applications that benefit from running in distributed platforms. In particular, scientific workflows can greatly leverage the ease-of-access, affordability, and scalability offered by cloud computing. To achieve this, innovative and efficient ways of orchestrating the workflow tasks and managing the compute resources in a cost-conscious manner need to be developed. We propose an adaptive, resource provisioning and scheduling algorithm for scientific workflows deployed in Infrastructure as a Service clouds. Our algorithm was designed to address challenges specific to clouds such as the pay-as-you-go model, the performance variation of resources and the on-demand access to unlimited, heterogeneous virtual machines. It is capable of responding to the dynamics of the cloud infrastructure and is successful in generating efficient solutions that meet a user-defined deadline and minimise the overall cost of the used infrastructure. Our simulation experiments demonstrate that it performs better than other state-of-the-art algorithms.

Data analytics plays a significant role in gaining insight of big data that can benefit in decision making and problem solving for various application domains such as science, engineering, and commerce. Cloud computing is a suitable platform for Big Data Analytic Applications (BDAAs) that can greatly reduce application cost by elastically provisioning resources based on user requirements and in a pay as you go model. BDAAs are typically catered for specific domains and are usually expensive. Moreover, it is difficult to provision resources for BDAAs with fluctuating resource requirements and reduce the resource cost. As a result, BDAAs are mostly used by large enterprises. Therefore, it is necessary to have a general Analytics as a Service (AaaS) platform that can provision BDAAs to users in various domains as consumable services in an easy to use way and at lower price. To support the AaaS platform, our research focuses on efficiently scheduling Cloud resources for BDAAs to satisfy Quality of Service (QoS) requirements of budget and deadline for data analytic requests and maximize profit for the AaaS platform. We propose an admission control and resource scheduling algorithm, which not only satisfies QoS requirements of requests as guaranteed in Service Level Agreements (SLAs), but also increases the profit for AaaS providers by offering a cost-effective resource scheduling solution. We propose the architecture and models for the AaaS platform and conduct experiments to evaluate the proposed algorithm. Results show the efficiency of the algorithm in SLA guarantee, profit enhancement, and cost saving.

Cloud computing is a groundbreaking solution to acquire computational resources on demand. To deliver high quality cloud services and provide features such as reduced costs and availability to customers, a cloud, like any other computational system, needs to be properly managed in accordance with its characteristics (e.g., scalability, elasticity, timeliness). In this scenario, cloud monitoring is a key to achieve it. To properly work, cloud monitoring systems need to meet several requirements such as scalability, accuracy, and timeliness. This paper aims to unveil the trade-off between timeliness and scalability. Evaluations demonstrate the mutual influence between scalability and timeliness based on monitoring parameters (e.g., monitoring topologies, frequency sampling). Results show that non-deep monitoring topologies and decreasing the frequency sampling assist to reduce the mutual influence between timeliness and scalability.

There has been a growing effort in decreasing energy consumption of large-scale cloud data centers via maximization of host-level utilization and load balancing techniques. However, with the recent introduction of Container as a Service (CaaS) by cloud providers, maximizing the utilization at virtual machine (VM) level becomes essential. To this end, this paper focuses on finding efficient virtual machine sizes for hosting containers in such a way that the workload is executed with minimum wastage of resources on VM level. Suitable VM sizes for containers are calculated, and application tasks are grouped and clustered based on their usage patterns obtained from historical data. Furthermore, tasks are mapped to containers and containers are hosted on their associated VM types. We analyzed clouds' trace logs from Google cluster and consider the cloud workload variances, which is crucial for testing and validating our proposed solutions. Experimental results showed up to 7.55% improvement in the average energy consumption compared to baseline scenarios where the virtual machine sizes are fixed. In addition, comparing to the baseline scenarios, the total number of VMs instantiated for hosting the containers is also improved by 68% on average.

Cloud service level agreement negotiation is a process of joint decision-making between cloud clients and providers to resolve their conflicting objectives. With the advances of cloud technology, operations such as discovery, scaling, monitoring and decommissioning are accomplished automatically. Therefore, negotiation between cloud clients and providers can be a bottleneck if it is carried out manually. Our objective is to propose a state-of-the-art solution to automate the negotiation process for cloud environments and specifically infrastructure as a service category. The proposed negotiation strategy is based on a time-dependent tactic. For cloud providers, the strategy uniquely considers utilization of resources when generating new offers and automatically adjusts the tactic's parameters to concede more on the price of less utilized resources. In addition, while the previous negotiation strategies in literature trust offered quality of service values regardless of their dependability, our proposed strategy is capable of assessing reliability of offers received from cloud providers. Furthermore, to find the right configuration of the time-dependent tactic in cloud computing environments, we investigate the effect of modifying parameters such as initial offer value and deadline on negotiation outputs that include ratio of deals made, and inequality index. The proposed negotiation strategy is tested with different workloads and in diverse market conditions to show how the time-dependent tactic's settings can dynamically adapt to help cloud providers increase their profits.

A significant number of Cloud applications follow the 3-tier architectural pattern. Many of them serve customers worldwide and must meet non-functional requirements such as reliability, responsiveness and Quality of Experience (QoE). Thus the flexibility and scalability offered by clouds make them a suitable deployment environment. Latest developments show that using multiple clouds can further increase an application's reliability and user experience to a level that has not been achievable before. However, the research in scheduling and provisioning 3-tier applications in clouds and across clouds is still in its infancy. To foster the research efforts in the area, we propose an analytical performance model of 3-tier applications in Cloud and Multi-Cloud environments. It takes into account the performance of the persistent storage and the heterogeneity of cloud data centres in terms of Virtual Machine (VM) performance. Furthermore, it allows for modelling of heterogeneous workloads directed to different data centres. Based on our model, we have extended the CloudSim simulator, through which we validate the plausibility of our approach. The conducted experiments with the RUBiS workload show that the predicted performance characteristics by the simulation approximate well those of the modelled system.

Summary Dynamic consolidation of virtual machines (VMs) is an efficient approach for improving the utilization of physical resources and reducing energy consumption in cloud data centers. Despite the large volume of research published on this topic, there are very few open‐source software systems implementing dynamic VM consolidation. In this paper, we propose an architecture and open‐source implementation of OpenStack Neat, a framework for dynamic VM consolidation in OpenStack clouds. OpenStack Neat can be configured to use custom VM consolidation algorithms and transparently integrates with existing OpenStack deployments without the necessity of modifying their configuration. In addition, to foster and encourage further research efforts in the area of dynamic VM consolidation, we propose a benchmark suite for evaluating and comparing dynamic VM consolidation algorithms. The proposed benchmark suite comprises OpenStack Neat as the base software framework, a set of real‐world workload traces, performance metrics and evaluation methodology. As an application of the proposed benchmark suite, we conduct an experimental evaluation of OpenStack Neat and several dynamic VM consolidation algorithms on a five‐node testbed, which shows significant benefits of dynamic VM consolidation resulting in up to 33% energy savings. Copyright © 2014 John Wiley & Sons, Ltd.

A note on resource orchestration for cloud computingWelcome to the special issue of Concurrency and Computation: Practice and Experience (CCPE) journal.This special issue compiles a number of excellent technical contributions that significantly advance the state-of-the-art in the areas of orchestrating cloud resources, composing new cloud services from existing ones, increasing energy efficiency via cloud resource orchestration, and developing cloud-based image processing solutions.Over the past few years, cloud computing [1-4] has emerged as the latest and most dominant utility computing solution offering both hardware and software resources as virtualization-enabled services.Cloud computing providers such as Amazon Web Services and Microsoft Azure currently provide application owners the option of deploying their applications over a network of a virtually infinite resource pool with practically no up-front capital investment and with operating cost proportional to the actual use (i.e., implementing a pay-as-you-go model).An increasing number of cloud vendors offer information and communication technology (ICT) resources such as hardware (CPUs, GPUs, storage, and networks), software infrastructure (e.g., databases, webservers, stream-processing systems, and data-mining packages), and collaboration/communication applications (e.g., email, video on demand, and social networks) as infrastructure as a service (IAAS), platform as a service (PAAS), and software as a service (SAAS), respectively.This approach allows enterprises to easily, cost effectively, and reliably offer business services that are supported by computing and software resources that are provided and maintained by IAAS, PAAS, and SAAS providers.This makes cloud computing attractive to especially small and medium size enterprises (SMEs), as it allows them to focus more on their core business and less on ICT infrastructure.One of the fundamental issues in exploiting cloud computing in this fashion is developing better Resource Orchestration (RO) [1-5] techniques and programming frameworks.More specifically, Resource Orchestration (RO) is 'the set of operations that cloud providers (e.g., AWS) and application owners (e.g., Netflix) undertake (either manually or automatically via computer programs) for selecting, deploying, monitoring, and dynamically controlling configuration of hardware and software resources as a system of QoS assured components that can be seamlessly delivered to end-users' [1].Since RO operations span across all layers of cloud computing stack [1], an overall goal of RO is to ensure successful hosting and delivery of applications (SAAS) by managing the fulfillment of the QoS objectives of both the application owners (e.g., maximize availability, maximize throughput, minimize latency, and avoid overloading) and the Cloud resource providers (e.g., maximize utilization, maximize energy efficiency, and maximize profit).One of the main complexities in Cloud resource management is that Cloud resources are typically identified by unique functional specifications, and then evaluated via their Quality of Service (QoS) properties.However, in practice, each resource may have multiple unique functional specifications that enable serving diverse user needs.For example, a song retrieval application is a Cloud resource that can be identified and enacted by the name of a song or via its lyrics, as users do may not know or remember the names of all song they want to find.The paper titled 'A Service Evaluation Method for Cross-cloud Service Choreography' [6] addresses this challenge by proposing a multifunctional specification solution for cross-cloud service choreography.This solution is based on a mixed integer programming model for multifunctional specification/identification that decomposes global resource and application constraints into local constraints.This allows constraint evaluations to be performed for cross-cloud service choreography.Experimental verification of this approach is also provided.Provisioning cloud resources requires providers and consumers to reach an agreement on the service usage terms and conditions.Such agreements are captured as Service Level Agreements (SLAs).The paper titled 'AutoSLAM -A Policy-based Framework for Automated SLA Establishment in Cloud

SUMMARY The cloud computing paradigm allows on‐demand access to computing and storage services over the Internet. Multiple providers are offering a variety of software solutions in the form of virtual appliances and computing units in the form of virtual machines with different pricing and QoS in the market. Thus, it is important to exploit the benefit of hosting virtual appliances on multiple providers to not only reduce the cost and provide better QoS but also achieve failure‐resistant deployment. This paper presents a framework called CloudPick to simplify cross‐cloud deployment and particularly focuses on QoS modeling and deployment optimization. For QoS modeling, cloud services have been automatically enriched with semantic descriptions using our translator component to increase precision and recall in discovery and benefit from descriptive QoS from multiple domains. In addition, an optimization approach for deploying networks of appliances is required to guarantee minimum cost, low latency, and high reliability. We propose and compare two different deployment optimization approaches: genetic‐based and forward‐checking‐based backtracking. They take into account QoS criteria such as reliability, data communication cost, and latency between multiple clouds to select the most appropriate combination of virtual machines and appliances. We evaluate our approach using a real case study and different request types. Experimental results suggest that both algorithms reach near‐optimal solution. Further, we investigate the effects of factors such as latency, reliability requirements, and data communication between appliances on the performance of the algorithms and placement of appliances across multiple clouds. The results show the efficiency of optimization algorithms depends on the data transfer rate between appliances. Copyright © 2014 John Wiley & Sons, Ltd.

It has been the author's privilege and honor to serve as the inaugural Editor-in-Chief of TCC since it launched in 2013 as a quarterly journal. In a short time span TCC has made excellent progress and established itself as one of the best venues for dissemination of fundamental and innovative research results in Cloud computing- thanks to the outstanding support of the IEEE and the world-wide Cloud computing community. He goes on to share TCC's status and key accomplishments during the last two years.

As companies shift from desktop applications to cloud-based software as a service (SaaS) applications deployed on public clouds, the competition for end-users by cloud providers offering similar services grows. In order to survive in such a competitive market, cloud-based companies must achieve good quality of service (QoS) for their users, or risk losing their customers to competitors. However, meeting the QoS with a cost-effective amount of resources is challenging because workloads experience variation overtime. This problem can be solved with proactive dynamic provisioning of resources, which can estimate the future need of applications in terms of resources and allocate them in advance, releasing them once they are not required. In this paper, we present the realization of a cloud workload prediction module for SaaS providers based on the autoregressive integrated moving average (ARIMA) model. We introduce the prediction based on the ARIMA model and evaluate its accuracy of future workload prediction using real traces of requests to Web servers. We also evaluate the impact of the achieved accuracy in terms of efficiency in resource utilization and QoS. Simulation results show that our model is able to achieve an average accuracy of up to 91 percent, which leads to efficiency in resource utilization with minimal impact on the QoS.

In Cloud computing, clients would like to pay fair price for the resources while providers would like to make profit for their services. In this study, we propose a Cloud Compute Commodity (C3) pricing architecture called Clabacus(Cloud-Abacus) to serve both parties. We use concepts and algorithms from financial option theory to develop Clabacus. We propose a general formula, called compound-Moores law, that captures the technological advances of the resources, rate of inflation and depreciation etc. We map these Cloud parameters to the option pricing parameters to effectively modify the option pricing algorithm in order to compute Cloud resource price. Using financial value-at-risk (VaR) analysis, we adjust the computed resource price to incorporate the inherent risks of the Cloud provider. We propose fuzzy logic and genetic algorithm based approaches to compute the VaR of the provider's resources. We have incorporated this into our Clabacus architecture. Finally, we study the effects of quality of service, rate of depreciation, rate of inflation, capital investment on the Cloud resource price for both client and provider. We show that if the prices are adjusted within a lower and upper bound, SLA can be guaranteed.

Infrastructure-as-a-Service cloud providers offer diverse purchasing options and pricing plans, namely on-demand, reservation, and spot market plans. This allows them to efficiently target a variety of customer groups with distinct preferences and to generate more revenue accordingly. An important consequence of this diversification however, is that it introduces a non-trivial optimization problem related to the allocation of the provider's available data center capacity to each pricing plan. The complexity of the problem follows from the different levels of revenue generated per unit of capacity sold, and the different commitments consumers and providers make when resources are allocated under a given plan. In this work, we address a novel problem of maximizing revenue through an optimization of capacity allocation to each pricing plan by means of admission control for reservation contracts, in a setting where aforementioned plans are jointly offered to customers. We devise both an optimal algorithm based on a stochastic dynamic programming formulation and two heuristics that trade-off optimality and computational complexity. Our evaluation, which relies on an adaptation of a large-scale real-world workload trace of Google, shows that our algorithms can significantly increase revenue compared to an allocation without capacity control given that sufficient resource contention is present in the system. In addition, we show that our heuristics effectively allow for online decision making and quantify the revenue loss caused by the assumptions made to render the optimization problem tractable.

Dynamic resource provisioning and the notion of seemingly unlimited resources are attracting scientific workflows rapidly into Cloud computing. Existing works on workflow scheduling in the context of Clouds are either on deadline or cost optimization, ignoring the necessity for robustness. Robust scheduling that handles performance variations of Cloud resources and failures in the environment is essential in the context of Clouds. In this paper, we present a robust scheduling algorithm with resource allocation policies that schedule workflow tasks on heterogeneous Cloud resources while trying to minimize the total elapsed time (make span) and the cost. Our results show that the proposed resource allocation policies provide robust and fault-tolerant schedule while minimizing make span. The results also show that with the increase in budget, our policies increase the robustness of the schedule.

Cloud computing is an utility computing paradigm that allows users to flexibly acquire virtualized computing resources in a pay-as-you-go model. To realize the benefits of using cloud, users need to first select the suitable cloud services that can satisfy their applications' functional and non-functional requirements. However, this is a difficult task due to large number of available services, users' unclear requirements, and performance variations in cloud. In this paper, we propose a system that evaluates trust of clouds according to users' fuzzy Quality of Service (QoS) requirements and services' dynamic performances to facilitate service selection. We demonstrate the effectiveness and efficiency of our system through simulations and case studies.

The broad adoption of cloud services led to an increasing concentration of servers in a few data centers. Reports estimate the energy consumptions of these data centers to be between 1.1% and 1.5% of the worldwide electricity consumption. This extensive energy consumption precludes massive CO2 emissions, as a significant number of data centers are backed by "brown" power plants. While most researchers have focused on reducing energy consumption of cloud data centers via server consolidation, we propose an approach for reducing the power required to execute urgent, CPU-intensive Bag-of-Tasks applications on cloud infrastructures. It exploits intelligent scheduling combined with the Dynamic Voltage and Frequency Scaling (DVFS) capability of modern CPU processors to keep the CPU operating at the minimum voltage level (and consequently minimum frequency and power consumption) that enables the application to complete before a user-defined deadline. Experiments demonstrate that our approach reduces energy consumption with the extra feature of not requiring virtual machines to have knowledge about its underlying physical infrastructure, which is an assumption of previous works.

The Earth Mover's Distance (EMD) similarity join retrieves pairs of records with EMD below a given threshold. It has a number of important applications such as near duplicate image retrieval and pattern analysis in probabilistic datasets. However, the computational cost of EMD is super cubic to the number of bins in the histograms used to represent the data objects. Consequently, the EMD similarity join operation is prohibitive for large datasets. This is the first paper that specifically addresses the EMD similarity join and we propose to use MapReduce to approach this problem. The MapReduce algorithms designed for generic metric distance similarity joins are inefficient for the EMD similarity join because they involve a large number of distance computations and have unbalanced workloads on reducers when dealing with skewed datasets. We propose a novel framework, named Melody-Join, which transforms data into the space of EMD lower bounds and performs pruning and partitioning at a low cost because computing these EMD lower bounds has a constant complexity. Furthermore, we address two key problems, the limited pruning power and the unbalanced workloads, by enhancing each phase in the Melody-Join framework. We conduct extensive experiments on real datasets. The results show that Melody-Join outperforms the state-of-the-art technique by an order of magnitude, scales up better on large datasets than the state-of-the-art technique, and scales out well on distributed machines.

This paper presents a quantitative study on the energy-traffic tradeoff problem from the perspective of entire Wireless Local Area Network (WLAN). We propose a novel Energy-Efficient Cooperative Offloading Model (E2COM) for energy-traffic tradeoff, which can ensure the fairness of energy consumption of mobile devices and reduce the computation repetition and eliminate the Internet data traffic redundancy through cooperative execution and sharing computation results. We design an Online Task Scheduling Algorithm (OTS) based on a pricing mechanism and Lyapunov optimization to address the problem without predicting future information on task arrivals, transmission rates and so on. OTS can achieve a desirable tradeoff between the energy consumption and Internet data traffic by appropriately setting the tradeoff coefficient. Simulation results demonstrate that E2COM is more efficient than no offloading and cloud offloading for a variety of typical mobile devices, applications and link qualities in WLAN.