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.

Federated Recommendation Systems (FRS) enable privacy-preserving model training by keeping user data on edge devices. However, the practical deployment of FRS in Edge-Cloud environments faces significant challenges due to system and statistical heterogeneity. Existing FRS participant selection strategies struggle to dynamically balance the trade-off between model convergence speed and recommendation quality in such volatile environments. To address this, we formulate the FRS participant selection problem as a normalized utility cost addressing the model quality and system efficiency. Next, we propose a dynamic participant selection framework incorporating a Multi-Armed Bandit (MAB)-based solver for multimodal FRS. We design a client-utility function that jointly evaluates historical Client Performance Reputation, data quality, and real-time system latency. By leveraging an Upper Confidence Bound strategy, our framework effectively balances the exploration of under-sampled clients with the exploitation of high-performing ones. We validate the proposed approach on a realistic edge-cloud testbed implementation using a multimodal movie-recommendation task. Experimental results demonstrate that our MAB-driven approach outperforms other baselines across eight different data-skew scenarios. Specifically, it improves training efficiency by 32-50% while improving model quality metrics such as Recall@50 by up to around 5%

Edge computing enables data processing closer to the source, significantly reducing latency, an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environments. However, these tasks place substantial demands on resource-constrained edge devices, making the joint optimization of energy consumption and detection accuracy critical. To address this challenge, we propose ECORE , a framework that integrates multiple dynamic routing strategies, including a novel estimation-based techniques and an innovative greedy selection algorithm, to direct image processing requests to the most suitable edge device–model pair. ECORE dynamically balances energy efficiency and detection performance based on object characteristics. We evaluate our framework through extensive experiments on real-world datasets, comparing against widely used baseline techniques. The evaluation leverages established object detection models (YOLO, SSD, EfficientDet) and diverse edge platforms, including Jetson Orin Nano, Raspberry Pi 4 and 5, and TPU accelerators. Results demonstrate that our proposed context-aware routing strategies can reduce energy consumption and latency by 35% and 49%, respectively, while incurring only a 2% loss in detection accuracy compared to accuracy-centric methods.

Abstract Field-captured video enables detailed study of animal locomotion, decision-making, and environmental interactions such as predator–prey dynamics and habitat use. While low-cost hardware makes data capture accessible, the storage, processing, and transmission demands of high-resolution video remain a major hurdle for field-deployed edge computing devices. Motion tracking in natural environments presents unique challenges that require tailored video compression strategies not well addressed in other domains. We present a novel end-to-end system comprising a motion analysis-based video compression algorithm specifically designed for camera traps, and a custom video processing methodology for automated analysis of compressed footage to extract behavioural data. We evaluate it through a case study on insect–pollinator motion tracking using three popular edge computing platforms. The compression algorithm operates alongside standard codecs, identifying and storing only image regions containing motion relevant to monitoring tasks, reducing data size by an average of 87% across diverse datasets. When combined with the H.265/HEVC codec, our approach achieved an additional 47.1% improvement in compression compared to stand-alone H.265. The accompanying video processing algorithm builds upon existing Polytrack software, incorporating new preprocessing and trajectory reconstruction techniques for automated processing of compressed footage with a 97.5% detection rate. Our experiments demonstrate that the system retains critical behavioural information, as verified through both automated and manual analyses. The method presented in this paper enhances the applicability of low-powered computer vision edge devices to remote, in situ animal motion monitoring, and improves the efficiency of playback during behavioural analyses.

Hierarchical Federated Learning (HFL) frameworks place edge servers between IoT devices and the cloud server to reduce communication costs and preserve privacy. In practice, however, HFL must handle hierarchical non-IID data across both device and edge levels. At the edge-level, heterogeneity arises because devices connected to the same edge server often share geographic or contextual similarities, giving each server its own optimization goal aligned with its region-specific data distribution rather than with a shared global objective. Existing HFL methods largely ignore this distinction, focusing on training a single global model that can obscure severe underperformance at the edge-level with underrepresented data. Since edge servers often act as operational units, poor performance at an edge implies degraded service quality, undermining system reliability and user trust. We propose Personalized Hierarchical Edge-enabled Federated Learning (PHE-FL), a novel method that produces personalized edge models by adaptively integrating edge- and cloud-level knowledge based on the data distribution of each edge, without incurring additional computational overhead or compromising client privacy. We deploy edge-specific test sets at each edge to ensure its unique data distribution is accurately reflected during evaluation. To the best of our knowledge, this is the first work to explicitly address hierarchical data heterogeneity in a 3-level HFL framework, both in terms of personalization and evaluation. Extensive experiments show that PHE-FL achieves up to 83% higher accuracy than existing edge-accommodated FL methods and maintains robust performance across edge-level non-IIDness, with reduced accuracy fluctuations compared to the state-of-the-art FedAvg with two levels (edge and cloud) aggregation.

With the rising popularity of electric vehicles (EVs), modern service systems, such as ride-hailing delivery services, are increasingly integrating EVs into their operations. Unlike conventional vehicles, EVs often have a shorter driving range, necessitating careful consideration of charging when fulfilling requests. With recent advances in Vehicle-to-Grid (V2G) technology—allowing EVs to also discharge energy back to the grid—new opportunities and complexities emerge. We introduce the Electric Vehicle Orienteering Problem with V2G (EVOP-V2G): a profit-maximisation problem where EV drivers must select a subset of customer requests while managing when and where to charge or discharge. This involves navigating dynamic electricity prices, charging station selection, and route constraints. We formulate the problem as a Mixed Integer Programming (MIP) model and propose two near-optimal metaheuristic algorithms: one evolutionary (EA) and the other based on large neighbourhood search (LNS). We compare these three algorithms with a greedy baseline on real-world data, showing that the proposed methods achieve up to twice the profit. V2G contributes about 20 % of the total profit in the default settings. MIP finds optimal solutions for small cases (30 orders, 3 stations) but does not scale well. EA and LNS give near-optimal results for small cases and handle large ones (900 orders, 70 stations) efficiently. Our work highlights a promising path toward smarter, more profitable EV-based mobility systems that actively support the energy grid.

With the rise of large-scale data centers and increasing demand for energy-efficient operations, there is a growing need to optimize the use of green energy in cloud computing environments. However, current schedulers focus solely on performance, lacking awareness of energy types and opportunities to promote green, low-carbon operations. This paper presents a Green-Aware Scheduling Framework for Kubernetes, named GreenK8s, aimed at minimizing the use of brown energy and maximizing the utilization of renewable energy sources, specifically solar power. Our framework integrates real-time power consumption monitoring with predictive solar energy models to intelligently schedule workloads based on energy availability. The proposed solution incorporates an AI-based solar power prediction model, Pod oversubscription strategies, and a novel scheduler, enabling Kubernetes to dynamically adapt to both the type and availability of green energy. Extensive experiments using the real-world Google Borg dataset and a realistic Kubernetes testbed demonstrate that GreenK8s reduces total energy consumption by up to 39 % and increases the average share of green energy in total consumption to 50.65 %, compared to state-of-the-art baselines. This work provides a promising approach to improve operational efficiency and sustainability in data centers.

The increasing proliferation of loT devices and AI applications has created a demand for scalable and efficient computing solutions, particularly for applications requiring real-time processing. The compute continuum integrates edge and cloud resources to meet this need, balancing the low-latency demands of the edge with the high computational power of the cloud. However, managing resources in such a distributed environment presents challenges due to the diversity and complexity of these systems. Traditional resource management methods, often relying on heuristic algorithms, struggle to manage the increasing complexity, scale, and dynamics of these systems, as well as adapt to dynamic workloads and changing network conditions. Moreover, designing such approaches is often time-intensive and highly tailored to specific applications, demanding deep expertise. In this paper, we introduce a novel framework for intent-driven resource management in the compute continuum, using large language models (LLMs) to help automate decision-making processes. Our framework ensures that user-defined intents - such as achieving the required response times for time-critical applications - are consistently fulfilled. In the event of an intent violation, our system performs root cause analysis by examining system data to identify and address issues. This approach reduces the need for human intervention and enhances system reliability, offering a more dynamic and efficient solution for resource management in distributed environments.

Federated Learning (FL) enables collaborative model training across various distributed devices without sharing raw data. Client failures, variable energy availability, and outages of edge servers contribute to unreliable training participation, incomplete model updates, and failures at the system level during the aggregation process. In this study, we introduce a reliability-aware workload allocation FL framework (FedRAW) aimed at improving system reliability in failure-prone edge computing systems. Our approach dynamically modifies client workloads based on their failure history and integrates a lightweight backup mechanism to maintain aggregation continuity during edge server failures by backup servers handling. Additionally, we employ Bayesian optimization to fine-tune workload parameters, achieving improved energy efficiency. Experimental results reveal that our proposed method improves model accuracy while reducing energy consumption compared to recent federated learning algorithms.

Serverless computing is a cloud computing model that abstracts server management, allowing developers to focus solely on writing code without concerns about the underlying infrastructure. This paradigm shift is transforming application development by reducing time to market, lowering costs, and enhancing scalability. In serverless computing, functions are event-driven and automatically scale in response to events such as data changes or user requests. Despite its advantages, serverless computing presents several research challenges, including managing state for ephemeral functions, mitigating cold start delays, optimizing function composition, debugging, efficient auto-scaling, resource management, and ensuring security and compliance. This special issue focused on addressing these challenges by promoting research on innovative solutions and exploring the potential of serverless computing in new application domains.

The shift towards renewable energy sources for powering data centers is increasingly important in the era of cloud computing. However, integrating renewable energy sources into cloud data centers presents a challenge due to their variable and intermittent nature. The unpredictable workload demands in cloud data centers further complicate this problem. In response to this pressing challenge, we propose a novel approach in this paper: adapting the workload to match the renewable energy supply. Our solution involves dynamic overbooking of resources, providing energy flexibility to data center operators. We propose a framework that stochastically models both workload and energy source information, leveraging Markov Decision Processes (MDP) to determine the optimal overbooking degree based on the workload flexibility of data center clients. We validate the proposed algorithm in realistic settings through extensive simulations. Results demonstrate the superiority of our proposed method over existing approaches, achieving better matching with the renewable energy supply by 55.6%, 34.65%, and 40.7% for workload traces fromNectarCloud,Google, andWikipedia, respectively.

The traditional MapReduce frameworks were originally designed for processing data within a single cluster and are not suitable for handling geo-distributed data. Consequently, alternative approaches such as Hierarchical and Geo-Hadoop have been proposed to address this limitation. However, these approaches still face challenges in efficiently managing inter-cluster data transfer, particularly considering the heterogeneity of clusters and varying bandwidth among them. Moreover, the need to transmit results to a central global reducer for geo-distributed MapReduce operations adds unnecessary complexity. To tackle these issues, we introduce Extended Cross-MapReduce (ECMR), a framework that integrates resource heterogeneity and network links in geo-distributed MapReduce workflows. ECMR optimizes data management and determines the necessary data volume for generating final results. To enhance performance, ECMR leverages the overlap between data transfer and execution time by utilizing multiple global reducers and grouping temporary results that require data transfer over the Internet. In ECMR, we propose a bipartite graph and extend the Gale-Shapley algorithm to determine the optimal number of clusters and select the most suitable locations for global reducers. Through extensive experimental evaluations conducted on a real testbed, we demonstrate the effectiveness of our proposed ECMR method. The results exhibit significant improvements over traditional Hierarchical and Geo-Hadoop approaches, achieving reductions of up to 81% and 85% in overall makespan, respectively.

The Internet of Things (IoT) has led to a surge in smart devices, generating vast volumes of data. Cloud computing offers scalability but does not suffice for many real-time and privacy-sensitive IoT applications. This limitation has prompted a blend of both edge and cloud resources, creating the need for seamless integration, known as the “compute continuum“. Testing applications and resource management techniques within this continuum is vital but can be very complex. Simulation and emulation are preferred methods, with emulation providing more accurate representations of real-world environments. In this paper, we introduce iContinuum, a novel emulation toolkit facilitating an intent-based platform for edge-to-cloud testing and experimentation. Leveraging Software-Defined Networking (SDN) and containerization, iContinuum enables experimentation and performance evaluation while aligning application requirements with actual performance. We present our detailed architecture, implementation, and evaluation of iContinuum, showcasing how our proposed toolkit bridges the gap between simulation and real-world deployment within compute continuum environments, and further demonstrate the effectiveness of Intent-Based Scheduling through a specific use case.

Cloud native solutions are widely applied in various fields, placing higher demands on the efficient management and utilization of resource platforms. To achieve the efficiency, load forecasting and elastic scaling have become crucial technologies for dynamically adjusting cloud resources to meet user demands and minimizing resource waste. However, existing prediction-based methods lack comprehensive analysis and integration of load characteristics across different time scales. For instance, long-term trend analysis helps reveal long-term changes in load and resource demand, thereby supporting proactive resource allocation over longer periods, while short-term volatility analysis can examine short-term fluctuations in load and resource demand, providing support for real-time scheduling and rapid response. In response to this, our research introduces TempoScale, which aims to enhance the comprehensive understanding of temporal variations in cloud workloads, enabling more intelligent and adaptive decision-making for elastic scaling. TempoScale utilizes the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm to decompose time-series load data into multiple Intrinsic Mode Functions (IMF) and a Residual Component (RC). First, we integrate the IMF, which represents both long-term trends and short-term fluctuations, into the time series prediction model to obtain intermediate results. Then, these intermediate results, along with the RC, are transferred into a fully connected layer to obtain the final result. Finally, this result is fed into the resource management system based on Kubernetes for resource scaling. Our proposed approach can reduce the Mean Square Error by 5.80% to 30.43% compared to the baselines, and reduce the average response time by 5.58% to 31.15%. The results demonstrate the effectiveness of our proposed method in reducing violations of service-level objectives and providing better performance in terms of resource utilization.

Electric vehicles (EVs) have the potential to serve as energy storage solutions through bidirectional charging technology, which allows them to both draw power from and feed power back into the grid, homes, or other vehicles. This capability enables EVs to reduce emissions, optimize costs, and support the grid by storing energy during periods of high production and supplying it when demand is high. In this vision paper, we focus on unlocking the potential of EVs as energy storage solutions while ensuring they remain readily available for transportation, their primary purpose. A significant research gap exists in that most current studies prioritize energy management, often using simplistic approaches that inadequately address the travel needs of EV owners. We believe the database community can be instrumental in maximizing the dual role of EVs as transportation and energy storage. We present a non-exhaustive list of research directions for various EV stakeholders, including individual EV owners, groups of independent yet cooperative EVs, commercial EV fleets, and autonomous EVs, and hope to inspire the database community for further exploration.

The adoption of the Internet of Things (IoT) has proliferated across various domains, where everyday objects like refrigerators and washing machines are now equipped with sensors and connected to the internet. Undeniably, the security of such devices, which were not primarily designed for internet connectivity, is of utmost importance but has been largely neglected. In this paper, we propose a framework for real-time DDoS attack detection and mitigation in SDN-enabled smart home networks. We capture network traffic during regular operations and during DDoS attacks. This captured traffic is used to train several machine learning (ML) models, including Support Vector Machine (SVM), Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN) algorithms. These trained models are executed as SDN controller applications and subsequently employed for real-time attack detection. While we utilize ML techniques to protect IoT devices, we propose the use of SNORT, a signature-based detection technique, to secure the SDN controller itself. Real-world experiments demonstrate that without SNORT, the SDN controller goes offline shortly after an attack, resulting in a 100% packet loss. Furthermore, we show that ML algorithms can efficiently classify traffic into benign and attack traffic, with the Decision Tree algorithm outperforming others with an accuracy of 99%.

Serverless edge systems simplify the deployment of real-time AI-based Internet of Things (IoT) applications at the edge. However, the heterogeneity of edge computing nodes – in terms of both hardware and software – makes load balancing challenging in these systems. In this paper, we propose a performance-driven, empirical weight-tuning approach to achieve effective load balancing based on the characteristics and capabilities of the nodes. By extensively profiling the nodes, we gather knowledge on performance metrics such as throughput, energy efficiency, response time, AI accuracy, and cost. Using this acquired knowledge, we introduce a weighted round-robin strategy to optimize the performance metrics according to their observed significance. To address multiple objectives, we introduce a multi-objective method that aims to strike a balance between any arbitrary set of performance objectives simultaneously. Additionally, we explore a coordinated distributed approach to overcome the limitations of centralized load balancing. Next, we introduce Hedgi, a heterogeneous serverless edge architecture designed to efficiently configure and utilize the derived load balancing policies, validated empirically. To demonstrate the practicality of Hedgi, we containerize and serverlessize a real-time object detection application. Extensive empirical studies are conducted using Hedgi to evaluate the performance of the proposed load balancing approach. The results provide valuable insights into the design trade-offs of various load balancing policies and system designs in the heterogeneous serverless edge.

Alternative route planning requires finding$k$alternative paths (including the shortest path) between a given source and target. These paths should be significantly different from each other and meaningful/natural (e.g., must not contain loops or unnecessary detours). While there exists many work on finding high-quality alternative paths, the existing techniques are computationally expensive and are unable to accommodate the high volume of queries required by modern navigation systems. To address this, in this paper, we propose an efficient approach to compute high-quality alternative paths. Our approach employs hub-labeling to efficiently identify candidate alternative paths. The candidate paths are then ranked considering multiple quality metrics and the top-$k$alternative paths are returned. We propose several non-trivial optimizations to significantly improve the computation time. In our experimental study, we conduct experiments on three diverse real-world road networks and compare our proposed algorithm against six state-of-the-art algorithms. The results demonstrate that our algorithm is not only up to 3 orders of magnitude faster compared to most algorithms but also consistently generates alternative paths that are comparable or even superior in terms of quality across various metrics.

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.

Serverless edge computing is a specialized system design tailored for Internet of Things (IoT) applications. It leverages serverless computing to minimize operational management and enhance resource efficiency, and utilizes the concept of edge computing to allow code execution near the data sources. However, edge devices powered by renewable energy face challenges due to energy input variability, resulting in imbalances in their operational availability. As a result, high-powered nodes may waste excess energy, while lowpowered nodes may frequently experience unavailability, impacting system sustainability. Addressing this issue requires energy-aware resource schedulers, but existing cloud-native serverless frameworks are energy-agnostic. To overcome this, we propose an energyaware scheduler for sustainable serverless edge systems. We introduce a reference architecture for such systems and formally model energy-aware resource scheduling, treating the function-to-node assignment as an imbalanced energy-minimizing assignment problem. We then design an optimal offline algorithm and propose faasHouse, an online energy-aware scheduling algorithm that utilizes resource sharing through computation offloading. Lastly, we evaluate faasHouse against benchmark algorithms using real-world renewable energy traces and a practical cluster of single-board computers managed by Kubernetes. Our experimental results demonstrate significant improvements in balanced operational availability (by 46%) and throughput (by 44%) compared to the Kubernetes scheduler.

Video games feature a dynamic environment where locations of objects (e.g., characters, equipment, weapons, vehicles etc.) frequently change within the game world. Although searching for relevant nearby objects in such a dynamic setting is a fundamental operation, this problem has received little research attention. In this paper, we propose a simple lightweight index, called Grid Tree, to store objects and their associated textual data. Our index can be efficiently updated with the underlying updates such as object movements, and supports a variety of object search queries, including k nearest neighbors (returning the k closest objects), keyword k nearest neighbors (returning the k closest objects that satisfy query keywords), and several other variants. Our extensive experimental study, conducted on standard game maps benchmarks and real-world keywords, demonstrates that our approach has up to 2 orders of magnitude faster update times for moving objects compared to state-of-the-art approaches such as navigation mesh and IR-tree. At the same time, query performance of our approach is similar to or better than that of IR-tree and up to two orders of magnitude faster than the other competitor.

The rapid development of emerging vehicular edge computing (VEC) brings new opportunities and challenges for dynamic resource management. The increasing number of edge data centers, roadside units (RSUs), and network devices, how-ever, makes resource management a complex task in VEC. On the other hand, the exponential growth of service applications and end-users makes corresponding QoS hard to maintain. Intent-Based Networking (IBN), based on Software-Defined Networking, was introduced to provide the ability to automatically handle and manage the networking requirements of different applications. Motivated by the IBN concept, in this paper, we propose a novel approach to jointly orchestrate networking and computing resources based on user requirements. The proposed solution constantly monitors user requirements and dynamically re-configures the system to satisfy desired states of the application. We compared our proposed solution with the state-of-the-art networking embedding algorithms using real-world taxi GPS traces. Results show that our proposed method is significantly faster (up to 95%) and can improve resource utilization (up to 76%) and the acceptance ratio of computing and networking requests with various priorities (up to 71%). We also present a small-scale prototype of the proposed intent management framework to validate our solution.

The deployment of fog computing resources in industrial internet of things (IIoT) is essential to support time-sensitive applications. To utilize resources efficiently, a brand-new request dispatcher is required to sit between the IIoT devices and the pool of fog resources. The need for such a dispatcher stems from the challenges specific to these systems. Firstly, fog-enabled IIoT systems are highly dynamic and distributed. Second, fog nodes are typically power and resource limited. Finally, many IIoT applications feature critical time-sensitivity, referred to as timeliness, and cannot tolerate response delay beyond a specific threshold. This paper proposes an efficient dispatching algorithm to minimize energy consumption and deadline misses while keeping the system stability at a satisfactory level. We leverage Lyapunov Optimization technique to tackle the problem and handle the system dynamics. We perform extensive simulations to verify the effectiveness of the proposed method and provide sensitivity, scalability and model parameter analysis. The simulation results prove the superiority of the proposed method over the state-of-the-art method up to 22% and 10% in terms of average deadline misses and energy consumption, respectively. Further, we perform practical experiments to prove the validity of the proposed method in a real testbed.

Efficient utilization of renewable energy when powering Cloud Data Centers is a challenging problem due to the variable and intermittent nature of both workload demand and renewable energy supply. This work aims to develop an innovative dynamic resource management algorithm to provide energy flexibility to data center operators for shaping their energy demand to match renewable energy supply. We present a novel framework, calledElastic Power Utilization(EPU), to serve this purpose.EPUutilizes energy source information to dynamically manage data center resources for matching the renewable energy supply with the energy demand to serve the workload. We propose a resource management algorithm that exploits overbooking, consolidation and migration of virtual machines (VMs) to implement the power elasticity required by theEPUframework. We compare our approach to a state-of-the-art algorithm and baseline approaches with three different workloads. The results from extensive simulations show that our proposed algorithm outperforms the state-of-the-art approach in saving brown energy by 23.1%, 21.3%, and 27.0% forGoogle,Wikipedia, andNectarworkloads, respectively.

In this paper, we present energy-aware scheduling for Serverless edge computing. Energy awareness is critical since edge nodes, in many Internet of Things (IoT) domains, are meant to be powered by renewable energy sources that are variable, making low-powered and/or overloaded (bottleneck) nodes unavailable and not operating their services. This awareness is also required since energy challenges have not been previously addressed by Serverless, largely due to its origin in cloud computing. To achieve this, we formally model an energy-aware resource scheduling problem in Serverless edge computing, given a cluster of battery-operated and renewable-energy powered nodes. Then, we devise zone-oriented and priority-based algorithms to improve the operational availability of bottleneck nodes. As assets, our algorithm coins terms “sticky offloading” and “warm scheduling” in the interest of the Quality of Service (QoS). We evaluate our proposal against well-known benchmarks using real-world implementations on a cluster of Raspberry Pis enabled with container orchestration, Kubernetes, and Serverless computing, OpenFaaS, where edge nodes are powered by real-world solar irradiation. Experimental results achieve significant improvements, up to 33%, in helping bottleneck node's operational availability while preserving the QoS. With energy awareness, now Serverless can unconditionally offer its resource efficiency and portability at the edge.

Computing multiple alternative routes from a source$s$to a target$t$has received significant research attention. However, it is unclear which of the existing approaches generates alternative routes of better quality because the quality of these alternatives is mostly subjective. Motivated by this, in this paper, we present a user study conducted on the road networks of Melbourne, Dhaka and Copenhagen comparing four of the most popular existing approaches including Google Maps. We report the average ratings received by the four approaches, and our statistical analysis shows that there is no credible evidence that the four approaches receive different ratings on average. We also discuss the limitations of this user study and recommend the readers interpret these results with caution.

Edge and Fog computing paradigms overcome the limitations of cloud-centric execution for different latency-sensitive Internet of Things (IoT) applications by offering computing resources closer to the data sources. Small single-board computers (SBCs) like Raspberry Pis (RPis) are widely used as computing nodes in both paradigms. These devices are usually equipped with moderate speed processors and provide support for peripheral interfacing and networking, making them well suited to deal with IoT-driven operations, such as data sensing, analysis, and actuation. However, these small Edge devices are constrained in facilitating multitenancy and resource sharing. The management of computing and peripheral resources through centralized entities further degrades their performance and service quality significantly. To address these issues, a fully distributed framework, namedCon-Pi, is proposed in this work to manage resources at the Edge or Fog environments. Con-Pi exploits the concept of containerization and harnesses Docker containers to run IoT applications as microservices. The software system of the proposed framework also provides a scope to integrate different IoT applications, resource and energy management policies for Edge and Fog computing. Its performance is compared with the state-of-the-art frameworks through real-world experiments. The experimental results show that Con-Pi outperforms others in enhancing response time and managing energy usage and computing resources through its distributed offloading model. Further, we have developed an automated pest bird deterrent system using Con-Pi to demonstrate its suitability in developing practical solutions for various IoT-enabled use cases, including smart agriculture.

Many modern navigation systems and map-based services do not only provide the fastest route from a source location$s$to a target location$t$but also provide a few alternative routes to the users as more options to choose from. Consequently, computing alternative paths has received significant research attention. However, it is unclear which of the existing approaches generates alternative routes of better quality because the quality of these alternatives is mostly subjective. Motivated by this, in this paper, we present a user study conducted on the road networks of Melbourne, Dhaka and Copenhagen that compares the quality (as perceived by the users) of the alternative routes generated by four of the most popular existing approaches including the routes provided by Google Maps. We also present a web-based demo system that can be accessed using any internet-enabled device and allows users to see the alternative routes generated by the four approaches for any pair of selected source and target. We report the average ratings received by the four approaches and our statistical analysis shows that there is no credible evidence that the four approaches receive different ratings on average. We also discuss the limitations of this user study and recommend the readers to interpret these results with caution because certain factors may have affected the participants’ ratings.

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.

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.

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.

Fog computing introduces a distributed processing capability close to end users. The proximity of computing to end users leads to lower service time and bandwidth requirements. Energy consumption is a matter of concern in such a system with a large number of computing nodes. Renewable energy sources can be utilized to lessen the burden on the main power grid and reduce the carbon footprint, but due to fluctuations, the effective utilization of renewable energy sources needs proper resource management. In this article, we deal with properly managing the resources in a fog environment where the fog nodes are equipped with onsite renewable energy. This article aims to design an efficient mechanism to dynamically dispatch requests among computing nodes and scale frequency and modulation level, based on the current workload and the availability of renewable energy sources, to minimize the service time while keeping the renewable energy utilization and stability at a satisfactory level. We state the problem as the design of a controller for a system with time-varying nonlinear state equations. Accordingly, we borrow the Lyapunov optimization technique from the control theory to design the request dispatching mechanism and prove its asymptotic optimality. We perform extensive simulations to evaluate the effectiveness of the proposed method. The simulation results demonstrate that our proposed method outperforms the naive time-aware baseline scheme up to 26% and 39%, respectively, in terms of service time and renewable energy utilization.

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.

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.

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.

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 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.

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.

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.

SUMMARY The paper describes creation of a contention‐aware environment in a large‐scale distributed system where the contention occurs to access resources between external and local requests. To resolve the contention, we propose and implement a preemption mechanism in the InterGrid platform, which is a platform for large‐scale distributed system and uses virtual machines for resource provisioning. The implemented mechanism enables the resource providers to increase their resource utilization through contributing resources to the InterGrid platform without delaying their local users. The paper also evaluates the impact of applying various policies for preempting user requests. These policies affect resource contention, average waiting time, and imposed overhead to the system. Experiments conducted in real settings demonstrate efficacy of the preemption mechanism in resolving resource contention and the influence of preemption policies on the amount of imposed overhead and average waiting time. Copyright © 2013 John Wiley & Sons, Ltd.

In recent years, cloud storage providers have gained popularity for personal and organizational data, and provided highly reliable, scalable and flexible resources to cloud users. Although cloud providers bring advantages to their users, most cloud providers suffer outages from time-to-time. Therefore, relying on a single cloud storage services threatens service availability of cloud users. We believe that using multi-cloud broker is a plausible solution to remove single point of failure and to achieve very high availability. Since highly reliable cloud storage services impose enormous cost to the user, and also as the size of data objects in the cloud storage reaches magnitude of exabyte, optimal selection among a set of cloud storage providers is a crucial decision for users. To solve this problem, we propose an algorithm that determines the minimum replication cost of objects such that the expected availability for users is guaranteed. We also propose an algorithm to optimally select data centers for striped objects such that the expected availability under a given budget is maximized. Simulation experiments are conducted to evaluate our algorithms, using failure probability and storage cost taken from real cloud storage providers.

Cloud Federation is a recent paradigm that helps Infrastructure as a Service (IaaS) providers to overcome resource limitation during spikes in demand for Virtual Machines (VMs) by outsourcing requests to other federation members. IaaS providers also have the option of terminating spot VMs, i.e, cheaper VMs that can be canceled to free resources for more profitable VM requests. By both approaches, providers can expect to reject less profitable requests. For IaaS providers, pricing and profit are two important factors, in addition to maintaining a high Quality of Service (QoS) and utilization of their resources to remain in the business. For this, a clear understanding of the usage pattern, types of requests, and infrastructure costs are necessary while making decisions to terminate spot VMs, outsourcing or contributing to the federation. In this paper, we propose policies that help in the decision-making process to increase resources utilization and profit. Simulation results indicate that the proposed policies enhance the profit, utilization, and QoS (smaller number of rejected VM requests) in a Cloud federation environment.