In the last years, we have seen an increase in the number of Artificial Intelligence (AI)-powered applications for information retrieval and data science. This fact led to an increasing reliance on distributed computing infrastructures, including Cloud, Edge, and IoT environments. These architectures enable powerful and scalable solutions but also introduce new security and privacy risks that must be addressed at both the system and data levels. Even a single breach on any of the links of the data-service-infrastructure chain may seriously compromise the security of the end-user application. With such a wide attack surface, security must definitely be approached in a holistic way and addressed in any layer where concerns may potentially arise. SIoTEC solicits novel and innovative ideas, proposals, positions and best practices that address the modelling, design, implementation, and enforcement of security in Cloud/Edge/IoT environments. Workshop website: https://siotec.netsons.org/

Advanced Vector Extensions 512 (AVX-512), a modern SIMD instruction set for x86 architectures, enables data-level parallelism through 512-bit wide ZMM registers capable of processing multiple data elements concurrently within a single instruction cycle. In this study, we present a high-throughput, lock-free, in-memory architecture for key-value data-stores that exploits AVX-512 vector operations to accelerate fundamental operations such as insertion and lookup. Our design introduces an optimized memory layout that partitions the key space into two disjoint regions (primary and secondary) and employs three independent hash functions to identify candidate slots. This asymmetric layout improves key distribution, reduces collision probability, and enhances overall lookup efficiency. Experimental evaluation shows that this strategy yields the lowest insertion failure rate among tested memory partitioning schemes. By leveraging AVX-512 instructions in combination with most optimized memory layout, our implementation achieves insertion throughput within 6% of Intel TBB's highly optimized multithreaded hash map, despite avoiding explicit synchronization or thread-level parallelism. Under workloads with 550 million entries and a 90% miss rate, our approach delivers 4.0-5.1x speedup over standard STL, Boost, Robin-Hood, and Abseil hash maps, and up to$2.5 x$improvement relative to TBB and Abseil. These gains are consistently observed for both 32-bit and 64-bit floating-point key types. The results confirm the viability of AVX-512-centric designs as a cost-effective alternative to thread-level parallelism, particularly in environments where minimizing synchronization overhead and ensuring deterministic execution are critical. Our findings suggest for a paradigm shift in CPU and system architecture, emphasizing wider vector units and improved memory bandwidth utilization as primary levers for scalable high-performance computing. These findings suggest that future extensions of AVX-512 capabilities, such as non-blocking memory loads, expanded vector registers, and asynchronous prefetching, could enhance the efficiency of data-intensive workloads.

Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services. Edge-to-cloud computing overcomes the limitations of end-devices by offloading their computations to nearby edge devices or remote cloud servers. Although this proves to be sufficient for many applications, optimal placement of latency sensitive AR/VR services in edge-to-cloud infrastructures (to provide desirable service response times and reliability) remain a formidable challenging. To address this challenge, this paper develops a Multi-Objective Genetic Algorithm (MOGA) to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments. The primary objective of the proposed MOGA is to minimize the response time of all running services, while maximizing the reliability of the underlying system from both software and hardware perspectives. To evaluate its performance, we mathematically modeled all components and developed a tailor-made simulator to assess its effectiveness on various scales. MOGA was compared with several heuristics to prove that intuitive solutions, which are usually assumed sufficient, are not efficient enough for the stated problem. The experimental results indicated that MOGA can significantly reduce the response time of deployed services by an average of 67% on different scales, compared to other heuristic methods. MOGA also ensures reliability of the 97% infrastructure (hardware) and 95% services (software).

In the realm of cloud native environments, Ku-bernetes has emerged as the de facto orchestration system for containers, and the service mesh architecture, with its interconnected microservices, has become increasingly prominent. Efficient scheduling and resource allocation for these microservices play a pivotal role in achieving high performance and maintaining system reliability. In this paper, we introduce a novel approach for container scheduling within Kubernetes clusters, leveraging Graph Attention Networks (GATs) for representation learning. Our proposed method captures the intricate dependencies among containers and services by constructing a representation graph. The deep Q-learning algorithm is then employed to optimize scheduling decisions, focusing on container-to-node placements, CPU request-response allocation, and adherence to node affinity and anti-affinity rules. Our experiments demonstrate that our GATs-based method outperforms traditional scheduling strategies, leading to enhanced resource utilization, reduced service latency, and improved overall system throughput. The insights gleaned from this study pave the way for a new frontier in cloud native performance optimization and offer tangible benefits to industries adopting microservice-based architectures.

Emerging computing paradigms like mist, edge, and fog computing address challenges in the real-time processing of vast Internet of Things (IoT) applications. Alongside, cloud computing offers a suitable platform for executing services. Together, they form a multi-tier computing environment known as compute-continuum to efficiently enhance data management and task execution of real-time tasks. The primary considerations for compute-continuum include variations in resource configuration and network architecture, rental cost model, application security needs, energy consumption, transmission latency, and system reliability. To address these problems, we propose two scheduling algorithms (RCSECH and RSECH) for real-time multi-workflow scheduling frameworks. Both algorithms optimize for rental cost, energy consumption, and task reliability when scheduling real-time workflows while considering deadlines and security requirements as constraints. RCSECH also factors in reliability alongside these constraints. The environment under investigation consists of a compute-continuum architecture consisting of mist, edge, fog, and cloud layers, each potentially composed of heterogeneous resources. The framework undergoes evaluation via simulation experiments, revealing promising results. Specifically, the framework exhibits the capability to enhance reliability by up to 7%, reduce energy consumption by 8%, surpass reliability constraints by more than 25%, and generate cost savings by at least 15%.

Military drones can be used for surveillance or spying on enemies. They, however, can be either destroyed or captured, therefore photos contained inside them can be lost or revealed to the attacker. A possible solution to solve such a problem is to adopt Secret Share (SS) techniques to split photos into several sections/chunks and distribute them among a fleet of drones. The advantages of using such a technique are two folds. Firstly, no single drone contains any photo in its entirety; thus even when a drone is captured, the attacker cannot discover any photos. Secondly, the storage requirements of drones can be simplified, and thus cheaper drones can be produced for such missions. In this scenario, a fleet of drones consists of t+r drones, where t (threshold) is the minimum number of drones required to reconstruct the photos, and r (redundancy) is the maximum number of lost drones the system can tolerate. The optimal configuration of t+r is a formidable task. This configuration is typically rigid and hard to modify in order to fit the requirements of specific missions. In this work, we addressed such an issue and proposed the adoption of a flexible Nested Secret Share (NSS) technique. In our experiments, we compared two of the major SS algorithms (Shamir's schema and the Redundant Residue Number System (RRNS)) with their Two-Level NSS (2NSS) variants to store/retrieve photos. Results showed that Redundant Residue Number System (RRNS) is more suitable for a drone fleet scenario.

With the development of multiaccess edge computing (MEC) technology, an increasing number of researchers and developers are deploying their computation-intensive and IO-intensive services (especially AI services) on edge devices. These devices, being close to end users, provide better performance in mobile environments. By constructing a service provisioning system at the network edge, latency is significantly reduced due to short-distance communication with edge servers. However, since the MEC-based service provisioning system is resource-sensitive and the network may be unstable, careful resource allocation and traffic scheduling strategies are essential. This paper investigates and quantifies the cost-effectiveness and robustness of the MEC-based service provisioning system with the applied resource allocation and traffic scheduling strategies. Based on this analysis, acost-effective androbust service provisioningalgorithm, termedCERA, is proposed to minimize deployment costs while maintaining system robustness. Extensive experiments are conducted to compare the proposed approach with well-known baseline algorithms and evaluate factors impacting the results. The findings demonstrate thatCERAachieves at least 15.9% better performance than other baseline algorithms across various instances.

Summary Apache Storm is a distributed processing engine that can reliably process unbounded streams of data for real‐time applications. While recent research activities mostly focused on devising a resource allocation and task scheduling algorithm to satisfy high performance or low latency requirements of Storm applications across a distributed and multi‐core system, finding a solution that can optimize the energy consumption of running applications remains an important research question to be further explored. In this article, we present a controlling strategy for CPU throttling that continuously optimize the level of consumed energy of a Storm platform by adjusting the voltage and frequency of the CPU cores while running the assigned tasks under latency constraints defined by the end‐users. The experimental results running over a Storm cluster with 4 physical nodes (total 24 cores) validates the effectiveness of proposed solution when running multiple compute‐intensive operations. In particular, the proposed controller can keep the latency of analytic tasks, in terms of 99th latency percentile, within the quality of service requirement specified by the end‐user while reducing the total energy consumption by 18% on average across the entire Storm platform.

Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can then simulate the performance behavior of all services over a given period and provide an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ~81-99\% simulation accuracy in approximating the average response time of incoming requests and ~16-1200 times speed-up factor for the simulation.

Computationally intensive applications with a wide range of requirements are advancing to cloud computing platforms. However, with the growing demands from users, cloud providers are not always able to provide all the prerequisites of the application. Hence, flexible computation and storage systems, such as multi-cloud systems, emerged as a suitable solution. Different charging mechanisms, vast resource configuration, different energy consumption, and reliability are the key issues for multi-cloud systems. To address these issues, we propose a multi-workflow scheduling framework for multi-cloud systems, intending to lower the monetary cost and energy consumption while enhancing the reliability of application execution. Our proposed platform presents different methods (utilizing resource gaps, the DVFS utilized method, and a task duplication mechanism) to ensure each application's requirement. The Weibull distribution is used to model task reliability at different resource fault rates and fault behavior. Various synthetic workflow applications are used to perform simulation experiments. The results of the performance evaluation demonstrated that our proposed algorithms outperform (in the terms of resource rental cost, efficient energy consumption, and improved reliability) state-of-the-art algorithms for multi-cloud systems.

The optimal placement of virtual network functions (VNFs) improves the overall performance of service function chains (SFCs) and decreases the operational costs for mobile network operators. To cope with changes in demands, VNF instances may be added or removed dynamically, resource allocations may be adjusted, and servers may be consolidated. To maintain an optimal placement of SFCs when conditions change, SFC reconfiguration is required, including the migration of VNFs and the rerouting of service-flows. However, such reconfigurations may lead to stress on the VNF infrastructure, which may cause service degradation. On the other hand, not changing the placement may lead to suboptimal operation, and servers and links may become congested or underutilized, leading to high operational costs. In this paper, we investigate the trade-off between the reconfiguration of SFCs and the optimality of their new placement and service-flow routing. We develop a multi-objective genetic algorithm that explores the Pareto front by balancing the optimality of the new placement and the cost to achieve it. Our numerical evaluations show that a small number of reconfigurations can significantly reduce the operational cost of the VNF infrastructure. In contrast, too much reconfiguration may not pay off due to high costs. We believe that our work provides an important tool that helps network providers to plan a good reconfiguration strategy for their service chains.

The emergence of Network Functions Virtualization (NFV) is being heralded as an enabler of the recent technologies such as 5G/6G, IoT and heterogeneous networks. Existing NFV monitoring frameworks either do not have the capabilities to express the range of telemetry items needed to perform management or do not scale to large traffic volumes and rates. We present IntOpt, a scalable and expressive telemetry system designed for flexible NFV monitoring using active probing and P4. IntOpt allows us to specify monitoring requirements for individual service chain, which are mapped to telemetry item collection jobs that fetch the required telemetry items from P4 programmable data-plane elements. We propose mixed integer linear program (MILP) as well as a simulated annealing based random greedy (SARG) meta-heuristic approach to minimize the overhead due to active probing and collection of telemetry items. Using P4-FPGA, we benchmark the overhead for telemetry collection. Our numerical evaluation shows that the proposed approach can reduce monitoring overheads by 39% and monitoring delays by 57%. Such optimization may as well enable existing expressive monitoring frameworks to scale for larger real-time networks.

Abstract In a virtualized computer system with shared resources, consolidated virtual services (VSs) fiercely compete with each other to obtain the required capacity of resources, and this causes significant system's performance degradation. The performance of input output (I/O)‐bound applications running inside their own VS is mainly determined by the total time required to schedule every read/write request, plus the actual time needed by the device driver to complete the request. To achieve a right performance isolation of shared resources (e.g., the last level cache, memory bandwidth, and the disk buffer), it is essential to limit the performance degradation level among collocated applications, as simultaneously several I/O operations are requested by VSs, perhaps with different priorities. This article proposes a resource allocation controller that uses a fully polynomial‐time randomized approximation scheme to enable performance isolation of concurrent I/O requests in a shared system with multiple consolidated VSs. This controller uses a Monte Carlo sampling approach to measure and estimate the unknown attributes of operational requests originating from each VS. This is formalized as an optimization problem with the aim to minimize the degree of total quality of service (QoS) violation incidents in the entire platform. We associated a reward function to every working machine that represents the fulfillment degree of quality of service metric among all running VSs. The conducted comprehensive set of experiments showed that the proposed algorithm can reduce the QoS violation incidents by 32%, compared with the result which is obtained by employing the default resource allocation policy embedded in the existing Linux container layer.

Cloud computing offers a wide range of services through a pool of heterogeneous Physical Machines (PMs) hosted on cloud data centers, where each PM can host several Virtual Machines (VMs). Resource sharing among VMs comes with major benefits, but it can create technical challenges that have a detrimental effect on the performance. To ensure a specific service level requested by the cloud-based applications, there is a need for an approach to assign adequate resources to each VM. To this end, we present our novel Multi-Loop Control approach, calledMultiScaler, to allocate resources to VMs based on the Service Level Agreement (SLA) requirements and the run-time conditions.MultiScaleris mainly composed of three different levels working closely with each other to achieve an optimal resource allocation. We propose a set of tailor-made controllers to monitor VMs and take actions accordingly to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one PM to another. The evaluation in a VMware cluster have shown that theMultiScalerapproach can meet applications performance goals and guarantee the SLA by assigning the exact resources that the applications require. Compared with sophisticated baselines,MultiScalerproduces significantly better reaction to changes in workloads even under the presence of noisy neighbors.

The papers in this special section focus on sustainability of edge computing systems. Edge computing is an emerging architectural and technical approach aimed at addressing various shortcomings in traditional cloud computing paradigms and responding to today’s constantly increasing data-demanding services such as Internet-of-Things, 5G embedded artificial intelligence and smart cities. In Fog/Edge Computing, nodes at the edge of a network are equipped with processing, storage, networking, etc. capabilities to take over several tasks that were used to be sent to cloud services. Pre-filtering and aggregation of data as well as online processing and actuation are sample procedures envisaged/dedicated to fog/ edge nodes.

In the fifth generation (5G) mobile networks, the number of user-plane gateways has increased, and, in contrast to previous generations they can be deployed in a decentralized way and auto-scaled independently from their control-plane functions. Moreover, the performance of the user-plane gateways can be boosted with the adoption of advanced acceleration techniques such as Vector Packet Processing (VPP). However, the increased number of user-plane gateways has also made load balancing a necessity, something we find has so far received little attention. Moreover, the introduction of VPP poses a challenge to the design of the auto-scaling of user-plane gateways. In this paper, we address these two challenges by proposing a novel performance indicator for making better auto-scaling decisions, and by proposing three new dynamic load-balancing algorithms for the user plane of a VPP-based, softwarized 5G network. The novel performance indicator is estimated based on the VPP vector rate and is used as a threshold for the auto-scaling process. The dynamic load-balancing algorithms take into account the number of bearers allocated for each user-plane gateway and their VPP vector rate. We validate and evaluate our proposed solution in a 5G testbed. Our experiment results show that the scaling helps to reduce the packet latency for the user-plane traffic, and that our proposed load-balancing algorithms can give a better distribution of traffic load as compared to traditional static algorithms.

The rapid development of the Internet-of-Things (IoT) makes it convenient to sense and collect real-world information with different kinds of widely distributed sensors. With plenty of web services providing diverse functions on the cloud, the collected information can be sufficiently used to complete complex tasks after being uploaded. However, the latency brought by long-distance communication and network congestion limits the development of IoT platforms. A feasible approach to solve this problem is to establish an edge-cloud collaboration (ECC) system based on the multi-access edge computing (MEC) paradigm where the collected information can be refined with the services deployed on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to services, as well as designing the traffic scheduling strategy. In this paper, we investigated the edge-cloud cooperation mechanism of service provisioning in ECC systems, and to that end, proposed an energy-consumption model for it; we also proposed a performance model and balancing model to quantify the running state of ECC systems. Based on these, we further formulated the energy-effective ECC system optimization problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. With the convexity of this problem proved, we proposed an algorithm to solve it and conducted a series of experiments to evaluate its performance. The results showed that our approach can improve at least 4.3 % of the performance compared with representative baselines.

Time-Slotted Channel Hopping (TSCH) is a synchronous Medium Access Control (MAC) technology standardized as a part of IEEE 802.15.4e to provide highly reliable communications for resource-constrained devices. While IETF and IEEE standards defined solutions for the configuration of TSCH nodes, the problem of creating dynamic TSCH schedules has been left open. In this poster, we introduce GT-SF, a distributed TSCH scheduling function that is designed based on the non-cooperative game-theory for low-power Internet of Things (IoT) applications. We implement GT-SF on Zolerita firefly IoT motes and the Contiki-NG operating system to examine its effectiveness. The evaluation results demonstrate that GT-SF outperforms Orchestra (the state-of-the-art method) by enhancing the packet delivery ratio and reducing the latency.

By increasing the number of IoT-devices, cloud-computing faces challenges for some computation and time-sensitive applications. Edge-computing has emerged to enable IoT-devices offload their computation tasks. Offloading tasks is a complex and challenging issue. We propose a comprehensive model including user, edge and cloud layers for scheduling continuous offering of services. Furthermore, we modeled the tasks of service as recurrent (repetitive) with a given frequency. The service-placement problem is formulated as a Mixed-Integer Linear Programming problem that aims to minimize the total delay of all services. We solve the problem with CPLEX, and proposed four fast heuristics to find near-optimal solutions. We compared the results of our proposed heuristics with the result obtained with CPLEX, in terms of problem-solving speed and accuracy, as well as resource utilization of all nodes. The results show that two of our proposed heuristics produce near-optimal solutions in a fraction of the time taken by CPLEX.

A significant amount of research studies in the past years has been devoted on developing efficient mechanisms to control the level of degradation among consolidate workloads in a shared platform. Workload consolidation is a promising feature that is employed by most service providers to reduce the total operating costs in traditional computing systems [1]–[3]. Serverless paradigm - also known as Function as a Service, FaaS, and Lambda - recently emerged as a new virtualization run-time model that disentangles the traditional state of applications' users from the burden of provisioning physical computing resources, leaving the difficulty of providing the adequate resource capacity on the service provider's side. This paper focuses on a number of challenges associated with workload consolidation when a serverless platform is expected to execute several data-intensive functional units. Each functional unit is considered to be the atomic component that reacts to a stream of input data. A serverless application in the proposed model is composed of a series of functional units. Through a systematic approach, we highlight the main challenges for devising an efficient workload consolidation process in a data-intensive serverless platform. To this end, we first study the performance interference among multiple workloads to obtain the capacity of last level cache (LLC). We show how such contention among workloads can lead to a significant throughput degradation on a single physical server. We expand our investigation into a general case with the aim to prevent the total throughput never falling below a predefined utilization level. Based on the empirical results, we develop a consolidation model and then design a computationally efficient controller to optimize the throughput degradation among a platform consists fs multiple machines. The performance evaluation is conducted using modern workloads inspired by data management services, and data analytic benchmark tools in our in-house four node platform showing the efficiency of the proposed solution to mitigate the QoS violation rate for high priority applications by 90% while can enhance the normalized throughput usage of disk devices by 39 %.

A significant portion of research work in the past decade has been devoted on developing resource allocation and task scheduling solutions for large-scale data processing platforms. Such algorithms are designed to facilitate deployment of data analytic applications across either conventional cluster computing systems or modern virtualized data-centers. The main reason for such a huge research effort stems from the fact that even a slight improvement in the performance of such platforms can bring a considerable monetary savings for vendors, especially for modern data processing engines that are designed solely to perform high throughput or/and low-latency computations over massive-scale batch or streaming data. A challenging question to be yet answered in such a context is to design an effective resource allocation solution that can prevent low resource utilization while meeting the enforced performance level (such as 99-th latency percentile) in circumstances where contention among applications to obtain the capacity of shared resources is a non negligible performance-limiting parameter. This paper proposes a resource controller system, called QSpark, to cope with the problem of (i) low performance (i.e., resource utilization in the batch mode and p-99 response time in the streaming mode), and (ii) the shared resource interference among collocated applications in a multi-tenancy modern Spark platform. The proposed solution leverages a set of controlling mechanisms for dynamic partitioning of the allocation of computing resources, in a way that it can fulfill the QoS re-quirements of latency-critical data processing applications, while enhancing the throughput for all working nodes without reaching their saturation points. Through extensive experiments in our in-house Spark cluster, we compared the achieved performance of proposed solution against the default Spark resource allocation policy for a variety of Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL) applications. Experimental results show the effectiveness of the proposed solution by reducing the p-99 latency of high priority applications by 32 % during the burst traffic periods (for both batch and stream modes), while it can enhance the QoS satisfaction level by 65 % for applications with the highest priority (compared with the results of default Spark resource allocation strategy).

Time-Slotted Channel Hopping (TSCH) was standardized as a part of IEEE 802.15.4e to address the strict reliability and timeliness requirements of low-power Internet of Things (IoT) applications. Setting the size of the TSCH slotframe has a considerable effect on the performance of scheduling algorithms used in IoT networks. Although IETF and IEEE standards define general mechanisms for communication of TSCH nodes, finding the optimal size of the TSCH slotframe has been left open and unresolved. In this poster, we propose an algorithm called S-TSCH to find the optimal size of the TSCH slotframe for maximizing network throughput based on 1) the number of nodes placed in the topology, 2) the data generation rate of applications running on IoT nodes, 3) and the maximum rate of generating TSCH/RPL control packets. To evaluate the performance of our contribution, we implement S-TSCH on Zolerita Firefly IoT motes and the Contiki-NG operating system. Evaluation results show that our proposed method improves the performance of distributed TSCH scheduling algorithms in terms of reliability and delay.

Secret Share (SS) is becoming a very hot topic within the scientific community. It allows us to split a secret into fragments and to share them among parties in such a way that a subset can recompose the original information. SS techniques assure a high redundancy degree, but the security level is fixed. Therefore, if a minimum number of peers collude then attackers can recompose the secret easily. A possible approach to improve the security of SS is designing nest fragment sharing techniques. In this paper, we propose the Nested Secret Share (NSS) as a more reliable and scalable strategy. In particular, we discuss the security of NSS considering the number of recomposition attempts that an attacker has to perform to retrieve the secret and then we deeply analyse the impact of the redundancy and the number of peers on the secret management against the percentage of compromised nodes. Experiments were promising and showed that the redundancy degree of SS can be highly improved by NSS.

In cloud computing model, resource sharing introduces major benefits for improving resource utilization and total cost of ownership, but it can create technical challenges on the running performance. In practice, orchestrators are required to allocate sufficient physical resources to each Virtual Machine (VM) to meet a set of predefined performance goals. To ensure a specific service level objective, the orchestrator needs to be equipped with a dynamic tool for assigning computing resources to each VM, based on the run-time state of the target environment. To this end, we present LOOPS, a multi-loop control approach, to allocate resources to VMs based on the service level agreement (SLA) requirements and the run-time conditions. LOOPS is mainly composed of one essential unit to monitor VMs, and three control levels to allocate resources to VMs based on requests from the essential node. A tailor-made controller is proposed with each level to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one host to another. The three levels work together to meet the required SLA. The experimental results have shown that the proposed approach can meet applications' performance goals by assigning the resources required by cloud-based applications.

The dramatically increasing of mobile applications make it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on a mobile edge computing (MEC) paradigm. In the MEC paradigm, plenty of machines are placed at the edge of the network so that the performance of applications can be optimized by using the involved microservice instances deployed on them. In this paper, we explore the deployment problem of microserivce-based applications in the MEC environment and propose an approach to help to optimize the cost of application deployment with the constraints of resources and the requirement of performance. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of mobile services.

Dependence of computing resources on each other in cloud computing systems (CCS) makes them prone to fail in correlated manner which significantly impacts their service reliability and energy efficiency. Focusing on these two metrics of CCS while considering correlated failures remained an open question, which is the focus of this work. This paper proposes mechanisms for improving reliability and energy efficiency jointly under correlated failures in CCS. In order to model failure correlation, statistical cluster analysis techniques are applied to real failure traces. Then, mathematical models are built to calculate reliability and energy consumption of failure prone CCS. These mathematical models are used to design fault-tolerant and energy-aware resource provisioning mechanisms/policies. In order to further reduce the energy consumption, a correlated failure-aware VM consolidation policy is also proposed in this paper. A simulation based study of the proposed resource management policies and fault tolerance mechanisms is conducted by using real failure traces and Bag-of-Tasks workload. The results demonstrate that by exploiting failure correlation with the proposed resource management policies, we reduce the occurrence of failures on tasks by 34 percent and increase the energy efficiency of the system by 20 percent, approximately in comparison to the environments where failures are handled independently.

Spark has emerged as one of the most widely and successfully used data analytical engine for large-scale enterprise, mainly due to its unique characteristics that facilitate computations to be scaled out in a distributed environment. This paper deals with the performance degradation due to resource contention among collocated analytical applications with different priority and dissimilar intrinsic characteristics in a shared Spark platform. We propose an auto-tuning strategy of computing resources in a distributed Spark platform for handling scenarios in which submitted analytical applications have different quality of service (QoS) requirements (e.g., latency constraints), while the interference among computing resources is considered as a key performance-limiting parameter. We compared Spark-Tuner to two widely used resource allocation heuristics in a large scale Spark cluster through extensive experimental settings across several traffic patterns with uncertain rate and application types. Experimental results show that with Spark-Tuner, the Spark engine can decrease the p-99 latency of high priority applications by 43% during the high-rate traffic periods, while maintaining the same level of CPU throughput across a cluster.

Modern stream-data processing platforms are required to execute processing pipelines over high-volume, yet high-velocity, datasets under tight latency constraints. Apache Flink has emerged as an important new technology of large-scale platform that can distribute processing over a large number of computing nodes in a cluster (i.e., scale-out processing). Flink allows application developers to design and execute queries over continuous raw-inputs to analyze a large amount of streaming data in a parallel and distributed fashion. To increase the throughput of computing resources in stream processing platforms, a service provider might be tempted to use a consolidation strategy to pack as many processing applications as possible on the working nodes, with the hope of increasing the total revenue by improving the overall resource utilization. However, there is a hidden trap for achieving such a higher throughput solely by relying on an interference-oblivious consolidation strategy. In practice, collocated applications in a shared platform can fiercely compete with each others for obtaining the capacity of shared resources (e.g., cache and memory bandwidth) which in turn can lead to a severe performance degradation for all consolidated workloads.This paper addresses the shared resource contention problem associated with the auto-resource controlling mechanism of Apache Flink engine running across a distributed cluster. A controlling strategy is proposed to handle scenarios in which stream processing applications may have different quality of service (QoS) requirements while the resource interference is considered as the key performance-limiting parameter. The performance evaluation is carried out by comparing the proposed controller with the default Flink resource allocation strategy in a testbed cluster with total 32 Intel Xeon cores under different workload traffic with up to 4000 streaming applications chosen from various benchmarking tools. Experimental results demonstrate that the proposed controller can successfully decrease the average latency of high priority applications by 223% during the burst traffic while maintaining the requested QoS enforcement levels.

As more analytical applications today require real-time processing over high volume data streams, finding an optimal implementation of traditional algorithms which possess iterative computations are gaining popularity and become crucial in most commercial contexts, particularly in edge processing and cloud applications. In this work, we propose an auto-tuning mechanism for enhancing the run-time performance of real-world iterative and cyclic stream processing applications (Multi-Join Operation as the study case) to correctly adjust the right performance bounds for workloads with different characteristics and data-sizes running on modern streaming data processing platform.

The increasing number of mobile web services makes it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on the mobile edge computing (MEC) paradigm, in which the latency can be reduced and the computation can be offloaded with the help of services deployed on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to services, as well as designing the traffic scheduling strategy. In this paper, we investigate the edge-cloud cooperation mechanism in service provisioning as well as the billing model of it. To minimize the average service response time and make the expense acceptable, we model and formulate the performance-cost service provisioning problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. Then we propose an efficient online algorithm, called PCA- CATS, to decompose this problem into two individual subproblems. We conduct a series of experiments to evaluate the performance of our approach. The results show that PCA- CATS can easily balance the performance and expense with a factor V, and can reduce up to 53.3 % service response time as compared with the baselines.

Devising an elastic resource allocation controller of data analytical applications in virtualized data-center has received a great attention recently, mainly due to the fact that even a slight performance improvement can translate to huge monetary savings in practical large-scale execution. Apache Flink is among modern streamed data processing run-times that can provide both low latency and high throughput computation in to execute processing pipelines over high-volume and high-velocity data-items under tight latency constraints. However, a yet to be answered challenge in a large-scale platform with tens of worker nodes is how to resolve the run-time violation in the quality of service (QoS) level in a multi-tenant data streaming platforms, particularly when the amount of workload generated by different users fluctuates. Studies showed that a static resource allocation algorithm (round-robin), which is used by default in Apache Flink, suffer from lack of responsiveness to sudden traffic surges happening unpredictably during the run-time. In this paper, we address the problem of resource management in a Flink platform for ensuring different QoS enforcement levels in a platform with shared computing resources. The proposed solution applies theoretical principals borrowed from close-loop control theory to design a CPU and memory adjustment mechanism with the primary goal to fulfill the different QoS levels requested by submitted applications while the resource interference is considered as the critical performance-limiting factor. The performance evaluation is carried out by comparing the proposed resource allocation mechanism with two static heuristics (round robin and class-based weighted fair queuing) in a 80-core cluster under multiple traffic patterns resembling sudden changes in the incoming workloads of low-priory streaming applications. The experimental results confirm the stability of the proposed controller to regulate the underlying platform resources to smoothly follow the target values (QoS violation rates). Particularly, the proposed solution can achieve higher efficiency compared to the other heuristics by reducing the response-time of high priority applications by 53% while maintaining the enforced QoS levels during the burst traffic periods.

Network Function Virtualization (NFV) becomes the primary driver for the evolution of 5G networks, and in recent years, Network Function Cloudification (NFC) proved to be an inevitable part of this evolution. Microservice architecture also becomes the de facto choice for designing a modern Cloud Native Network Function (CNF) due to its ability to decouple components of each CNF into multiple independently manageable microservices. Even though taking advantage of microservice architecture in designing CNFs solves specific problems, this additional granularity makes estimating resource requirements for a Production Environment (PE) a complex task and sometimes leads to an over-provisioned PE. Traditionally, performance engineers dimension each CNF within a Service Function Chain (SFC) in a smaller Performance Testing Environment (PTE) through a series of performance benchmarks. Then, considering the Quality of Service (QoS) constraints of a Service Provider (SP) that are guaranteed in the Service Level Agreement (SLA), they estimate the required resources to set up the PE. In this paper, we used a machine learning approach to model the impact of each microservice's resource configuration (i.e., CPU and memory) on the QoS metrics (i.e. serving throughput and latency) of each SFC in a PTE. Then, considering an SP's Service Level Objectives (SLO), we proposed an algorithm to predict each microservice's resource capacities in a PE. We evaluated the accuracy of our prediction on a prototype of a cloud native 5G Home Subscriber Server (HSS). Our model showed 95%-78% accuracy in a PE that has 2-5 times more computing resources than the PTE.

Time-Slotted Channel Hopping (TSCH) is defined in the IEEE 802.15.4e standard as a share medium access control technology to address reliability and timeliness requirements of low-power Internet of Things (IoT) applications. While standards define mechanisms for the basic configuration and communication of TSCH nodes, the adaptation of the TSCH schedule to traffic dynamics has been left as an open research problem. In this poster, we propose an Optimized Adaptive TSCH Scheduling Function (OA-TSCH) to dynamically adjust the TSCH schedule to the changes in the data traffic loads. We implement OA-TSCH on Zolerita Firefly IoT motes and the Contiki-NG operating system to evaluate its performance. Evaluation results show that our proposed scheduling function can improve the packet delivery ratio and throughput significantly.

Abstract Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).

Edge computing (EC) is now emerging as a key paradigm to handle the increasing Internet-of-Things (IoT) devices connected to the edge of the network. By using the services deployed on the service provisioning system which is made up of edge servers nearby, these IoT devices are enabled to fulfill complex tasks effectively. Nevertheless, it also brings challenges in trustworthiness management. The volatile environment will make it difficult to comply with the service-level agreement (SLA), which is an important index of trustworthiness declared by these IoT services. In this article, by denoting the trustworthiness gain with how well the SLA can comply, we first encode the state of the service provisioning system and the resource allocation scheme and model the adjustment of allocated resources for services as a Markov decision process (MDP). Based on these, we get a trained resource allocating policy with the help of the reinforcement learning (RL) method. The trained policy can always maximize the services' trustworthiness gain by generating appropriate resource allocation schemes dynamically according to the system states. By conducting a series of experiments on the YouTube request dataset, we show that the edge service provisioning system using our approach has 21.72% better performance at least compared to baselines.

Managing and scaling virtual network function (VNF) service chains require the collection and analysis of network statistics and states in real time. Existing network function virtualization (NFV) monitoring frameworks either do not have the capabilities to express the range of telemetry items needed to perform management or do not scale to large traffic volumes and rates. We present IntOpt, a scalable and expressive telemetry system designed for flexible VNF service chain network monitoring using active probing. IntOpt allows to specify monitoring requirements for individual service chain, which are mapped to telemetry item collection jobs that fetch the required telemetry items from P4 (programming protocol-independent packet processors) programmable dataplane elements. In our approach, the SDN controller creates the minimal number of monitoring flows to monitor the deployed service chains as per their telemetry demands in the network. We propose a simulated annealing based random greedy metaheuristic (SARG) to minimize the overhead due to active probing and collection of telemetry items. Using P4-FPGA, we benchmark the overhead for telemetry collection and compare our simulated annealing based approach with a naïve approach while optimally deploying telemetry collection probes. Our numerical evaluation shows that the proposed approach can reduce the monitoring overhead by 39% and the total delays by 57%. Such optimization may as well enable existing expressive monitoring frameworks to scale for larger real-time networks.

This paper focuses on Timely dataflow programming model for processing streams of data. We propose a technique to define CPU resource allocation (i.e., CPU capping) with the goal to improve response time latency in such type of applications with different quality of service (QoS) level, as they are concurrently running in a shared multi-core computing system with unknown and volatile demand. The proposed solution predicts the expected performance of the underlying platform using an online approach based on queuing theory and adjusts the corrections required in CPU allocation to achieve the most optimized performance. The experimental results confirms that measured performance of the proposed model is highly accurate while it takes into account the percentiles on the QoS metrics. The theoretical model used for elastic allocation of CPU share in the target platform takes advantage of design principals in model predictive control theory and dynamic programming to solve an optimization problem. While the prediction module in the proposed algorithm tries to predict the temporal changes in the arrival rate of each data flow, the optimization module uses a system model to estimate the interference among collocated applications by continuously monitoring the available CPU utilization in individual nodes along with the number of outstanding messages in every intermediate buffer of all TDF applications. The optimization module eventually performs a cost-benefit analysis to mitigate the total amount of QoS violation incidents by assigning the limited CPU shares among collocated applications. The proposed algorithm is robust (i.e., its worst-case output is guaranteed for arbitrarily volatile incoming demand coming from different data streams), and if the demand volatility is not large, the output is optimal, too. Its implementation is done using the TDF framework in Rust for distributed and shared memory architectures. The experimental results show that the proposed algorithm reduces the average and p99 latency of delay-sensitive applications by 21% and 31.8%, respectively, while can reduce the amount of QoS violation incidents by 98% on average.

The rapid growth in the number of Internet of Things (IoT) devices has increased the demand for exploring high-throughput communications. Low power IoT networks perform poorly under heavy traffic due to severe congestion and high packet loss problems. Controlling the rate of traffic load is advocated as an effective way to reduce the congestion in traditional networks. However, it poses a major challenge to low power IoT networks due to the lack of infrastructure, dynamic changes of the network topology, and using multi-hop communication through unstable lossy wireless links. To overcome this problem, in this paper we propose an optimized Mobility-Aware Rate Adaptation (MARA) framework based on the game theory. We model the rate control problem as a non-cooperative game where IoT nodes compete for higher bandwidth as selfish players. Based on the Rosen's theorem for concave N-person games, we prove the existence and uniqueness of Nash equilibrium. Finding the optimal solution of the game is modeled as a nonlinear programming (NLP) problem which is solved by using Lagrange multipliers and Karush-Kuhn-Tucker (KKT) optimality conditions. MARA can effectively adapt the transmission rate of each node to the changes in the network topology, traffic dynamics, and energy resources. We implement MARA on Zolerita IoT motes and Contiki operating system to evaluate its performance. Emulation results show that MARA improves the packet delivery ratio by up to 42%, and reduces the end-to-end delay and the energy consumption by up to 32% and 30% respectively.

Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service. Therefore, an effective service chain placement strategy is required to optimize the resource allocation and consequently to reduce the operating cost of the substrate network. To this end, we propose four genetic-based algorithms using roulette wheel and tournament selection techniques in order to place service chains considering two different placement strategies. Since mapping of service chains sequentially (One-at-a-time strategy) may lead to suboptimal placement, we also propose Simultaneous strategy that places all service chains at the same time to improve performance. Our goal in this work is to reduce deployment cost of VNFs while satisfying constraints. We consider Geant network as the substrate network along with its characteristics extracted from SndLib. The proposed algorithms are able to place service chains with any type of service graph. The performance benefits of the proposed algorithms are highlighted through extensive simulations.

In this paper, we aim at tackling the scalability problem of the Mobility Management Entity (MME), which plays a crucial role of handling control plane traffic in the current 4G Evolved Packet Core as well as the next generation mobile core, 5G. One of the solutions to this problem is to virtualize the MME by applying Network Function Virtualization principles and then deploy it as a cluster of multiple virtual MME instances (vMMEs) with a front-end load balancer. Although several designs have been proposed, most of them assume the use of simple algorithms such as random and round-robin to balance the incoming traffic without any performance assessment. To this end, we implemented a weighted round robin algorithm which takes into account the heterogeneity of resources such as the capacity of vMMEs. We compare this algorithm with a random and a round-robin algorithm under two different system settings. Experimental results suggest that carefully selected load balancing algorithms can significantly reduce the control plane latency as compared to simple random or round-robin schemes.

Network Function Virtualization (NFV) is an emerging network architecture to increase flexibility and agility within operator's networks by placing virtualized services on demand in Cloud data centers (CDCs). One of the main challenges for the NFV environment is how to minimize network latency in the rapidly changing network environments. Although many researchers have already studied in the field of Virtual Machine (VM) migration and Virtual Network Function (VNF) placement for efficient resource management in CDCs, VNF migration problem for low network latency among VNFs has not been studied yet to the best of our knowledge. To address this issue in this article, we i) formulate the VNF migration problem and ii) develop a novel VNF migration algorithm called VNF Real-time Migration (VNF-RM) for lower network latency in dynamically changing resource availability. As a result of experiments, the effectiveness of our algorithm is demonstrated by reducing network latency by up to 70.90% after latency-aware VNF migrations.

Network Function Virtualization (NFV) is an emerging network architecture to increase flexibility and agility within operator's networks by placing virtualized services on demand in Cloud data centers (CDCs). One of the main challenges for the NFV environment is how to efficiently allocate Virtual Network Functions (VNF) to Virtual Machines (VMs). Although a significant amount of work/research has been already conducted for the generic VNF placement problem, network latency among various network components has not been comprehensively considered yet. To address this concern, in this article, we design a more comprehensive model based on real measurements to capture network latency among VNFs with more granularity to optimize placement of VNFs in CDCs. Experimental results are promising and indicate that our approach, namely VNF Low-Latency Placement (VNF-LLP), can reduce network latency by up to 64.24% (50.33% in average) compared with two generic algorithms. Furthermore, it has a lower lead time (time to find a suitable VM to host a VNF) as compared with two classic approaches.

Lambda architecture is a novel event-driven serverless paradigm that allows companies to build scalable and reliable enterprise applications. As an attractive alternative to traditional service oriented architecture (SOA), Lambda architecture can be used in many use cases including BI tools, in-memory graph databases, OLAP, and streaming data processing. In practice, an important aim of Lambda's service providers is devising an efficient way to co-locate multiple Lambda functions with different attributes into a set of available computing resources. However, previous studies showed that consolidated workloads can compete fiercely for shared resources, resulting in severe performance variability/degradation. This paper proposes a resource allocation mechanism for a Lambda platform based on the model predictive control framework. Performance evaluation is carried out by comparing the proposed solution with multiple resource allocation heuristics, namely enhanced versions of spread and binpack, and best-effort approaches. Results confirm that the proposed controller increases the overall resource utilization by 37% on average and achieves a significant improvement in preventing QoS violation incidents compared to others.

Virtual machine (VM) live migration has been applied to system load balancing in cloud environments for the purpose of minimizing VM downtime and maximizing resource utilization. However, the migration process is both time- and cost-consuming as it requires the transfer of large size files or memory pages and consumes a huge amount of power and memory for the origin and destination physical machine (PM), especially for storage VM migration. This process also leads to VM downtime or slowdown. To deal with these shortcomings, we develop a Multi-objective Load Balancing (MO-LB) system that avoids VM migration and achieves system load balancing by transferring extra workload from a set of VMs allocated on an overloaded PM to other compatible VMs in the cluster with greater capacity. To reduce the time factor even more and optimize load balancing over a cloud cluster, MO-LB contains a CPU Usage Prediction (CUP) sub-system. The CUP not only predicts the performance of the VMs but also determines a set of appropriate VMs with the potential to execute the extra workload imposed on the VMs of an overloaded PM. We also design a Multi-Objective Task Scheduling optimization model using Particle Swarm Optimization to migrate the extra workload to the compatible VMs. The proposed method is evaluated using a VMware-vSphere-based private cloud in contrast to the VM migration technique applied by vMotion. The evaluation results show that the MO-LB system dramatically increases VM performance while reducing service response time, memory usage, job makespan, power consumption and the time taken for the load balancing process.

Hybrid clouds have gained popularity in recent times in a variety of organizations due to their ability to provide additional capacity in a public cloud, to augment private cloud capacity, when it is needed. However, scheduling distributed applications' jobs (e.g, workflow tasks) on hybrid cloud resources introduces new challenges. One key problem is the danger of exposing private data and jobs in a third-party public cloud infrastructure, for example in healthcare applications. In this article, we tackle the problem of designing workflow scheduling algorithms to meet customers' deadlines, while not compromising data and task privacy requirements. Our work is different from most studies on workflow scheduling where the main goal is to achieve a balance between desirable, yet incompatible constraints, such as meeting the deadline and/or minimizing the execution time. Although many others have addressed the trade-off between cost and time, or privacy and cost, their work still suffers from an insufficient consideration of the trade-off between privacy and time. To address such shortcomings in the literature, we present a new SaaS scheduling broker composed of MPHC-P1, MPHCP2, and MPHC-P3 policies to preserve privacy while scheduling the workflows' tasks under customers' deadlines. We evaluated our approach using real workflows running on a VMware based hybrid cloud. Results demonstrate that under our scheduling policies, MPHC-P2 and MPHC-P3 are promising in time-critical scenarios by reducing the total cost by 10-20 percent compared to alternatives. Overall, results show that our approach is efficient in reducing the cost of executing workflows while satisfying both their privacy and deadline constraints.

The advances in mobile technologies enable mobile devices to perform tasks that are traditionally run by personal computers as well as provide services to the others. Mobile users can form a service sharing community within an area by using their mobile devices. This paper highlights several challenges involved in building such service compositions in mobile communities when both service requesters and providers are mobile. To deal with them, we first propose a mobile service provisioning architecture named a mobile service sharing community and then propose a service composition approach by utilizing the Krill-Herd algorithm. To evaluate the effectiveness and efficiency of our approach, we build a simulation tool. The experimental results demonstrate that our approach can obtain superior solutions as compared with current standard composition methods in mobile environments. It can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

In virtualized datacenters (vDCs), dynamic consolidation of virtual machines (VMs) is used to achieve both energy-efficiency and load balancing among different physical machines (PMs). Using VM live migrations, we can consolidate VMs on a smaller number of hosts to power down unused PMs and save energy. Most migration schemes are however oblivious to the characteristics of services that run inside VMs, and thus may lead to migrations where VMs competing for the same resource type are packed on the same PM. As a result, VMs may suffer from significant resource contention and noticeable degradation in their performance. Using resource sensitivity values of VMs (i.e., quantitative measures to reflect how much a VM is sensitive to its requested resources such as CPU, Mem, and Disk), we have designed a novel VM consolidation approach to optimize placement of VMs on available PMs. We validated our approach using five well-known applications/benchmarks with various resource demand signatures: varying from pure CPU/Mem/Disk-intensive to mixtures of them. Our extensive numerical evaluation illustrates that, for the same power consumption, our approach improve the performance of cloud services by 9 - 12%, on average, when compared with current sensitivity oblivious approaches.

Virtualization is central to cloud computing systems. It abstracts computing resources to be shared among multiple virtual machines (VMs) that can be easily managed to run multiple applications and services. To benefit from the advantages of cloud computing, and to cope with increasing traffic demands, telecom operators have adopted cloud computing. Telecom services and applications are, however, characterized by real- time responsiveness, strict end-to-end latency, and high reliability. Due to the inherent overhead of virtualization, the network performance of applications and services can be degraded. To improve the performance of emerging applications and services that demand stringent end-to-end latency, and to understand the network performance bottleneck of virtualization, a comprehensive performance measurement and analysis is required. To this end, we conducted controlled and detailed experiments to understand the impact of virtualization on end-to-end latency and the performance of transport protocols in a virtualized environment. We also provide a packet delay breakdown in the virtualization layer which helps in the optimization of hypervisor components. Our experimental results indicate that the end-to-end latency and packet delay in the virtualization layer are increased with co-located VMs.

Summary This work presents a genetic algorithm (GA)‐based optimization technique, called GA‐ParFnt, to find the Pareto frontier for optimizing data transfer versus job execution time in grids. As the performance of a generic GA is not suitable to find such Pareto relationship, major modifications are applied to it so that it can efficiently discover such relationship. The frontier curve representing this relationship is then matched against performance of several scheduling techniques—for both data intensive and computationally intensive applications—to measure their overall performances. Results show that few of these algorithms are far from the Pareto front despite their claims of being efficient in optimizing their targeted objectives. Results also provide invaluable insights into this formidable problem and should aid in the design of future schedulers. Copyright © 2012 John Wiley & Sons, Ltd.

Service mashups are applications created by combining single-functional services (or APIs) dispersed over the web. With the development of cloud computing and web technologies, service mashups are becoming more and more widely used and a large number of mashup platforms have been produced. However, due to the proliferation of services on the web, how to select component services to create mashups has become a challenging issue. Most developers pay more attention to the quality of service (QoS) and cost of services. Beside service selection, mashup deployment is another pivotal process, as the platform can significantly affect the quality of mashups. In this paper, we focus on creating service mashups from the perspective of developers. A genetic algorithm-based method, genetic algorithm for mashup creation (GA4MC), is proposed to select component services and deployment platforms in order to create service mashups with optimal cost performance. A series of experiments are conducted to evaluate the performance of GA4MC. The results show that the GA4MC method can achieve mashups whose cost performance is extremely close to the optimal. Moreover, the execution time of GA4MC is in a low order of magnitude and the algorithm performs good scalability as the experimental scale increases.

The development of cloud computing and virtualization techniques enables mobile devices to overcome the severity of scarce resource constrained by allowing them to offload computation and migrate several computation parts of an application to powerful cloud servers. A mobile device should judiciously determine whether to offload computation as well as what portion of an application should be offloaded to the cloud. This paper considers a mobile computation offloading problem where multiple mobile services in workflows can be invoked to fulfill their complex requirements and makes decision on whether the services of a workflow should be offloaded. Due to the mobility of portable devices, unstable connectivity of mobile networks can impact the offloading decision. To address this issue, we propose a novel offloading system to design robust offloading decisions for mobile services. Our approach considers the dependency relations among component services and aims to optimize execution time and energy consumption of executing mobile services. To this end, we also introduce a mobility model and a trade-off fault-tolerance mechanism for the offloading system. A genetic algorithm (GA) based offloading method is then designed and implemented after carefully modifying parts of a generic GA to match our special needs for the stated problem. Experimental results are promising and show nearoptimal solutions for all of our studied cases with almost linear algorithmic complexity with respect to the problem size.

Organizations overcome resource shortages by utilizing the multiple services of cloud providers. This leads to sharing resources among various public and private clouds in order to improve the performance while executing the organization's complex workflow systems. Executing multiple workflows in such a hybrid environment needs an effective mapping between workflow's tasks and cloud resources that considers the trade-off between budget and time. There is also a challenge when organizations are forced to deploy workflow's tasks on public resources to execute the tasks before their requested deadlines without violating customers' privacy. In recent years, several online and static approaches were presented to schedule single or multiple workflows considering deadline and budget in cloud environments. However, these studies neglect the privacy constraint along with other SLAs such as deadline and budget. In this paper, we present two online algorithms to schedule multiple workflows under deadline and privacy constraints, while considering the dynamic nature of hybrid cloud environment. The proposed algorithms were evaluated with a series of simulation as well as real experiments using real-life privacy constrained healthcare workflows. Our two algorithms use different methods to rank the tasks: one utilises a novel technique for ranking, the other uses a similar approach to current existing studies. Results show that the novel approach outperforms the current existing ranking methods.

Cloud computing has been developed in response to demand from companies seeking to deal with the execution cost of their complex distributed applications. Introducing the notion of hybrid clouds to the cloud computing paradigm brings out many challenges in resource provisioning for workflows. Hybrid clouds encounter the following two main obstacles in reaching their full potential: (1) customers' dissatisfaction due to the conflicting nature of the constraints (budget and deadline), and (2) exposure of customers' private data/jobs in hybrid cloud infrastructures. We believe that too little attention is paid to privacy issues for workflow scheduling under deadline constraint. Many algorithms exist to address the cost and time trade-off, however, they suffer from insufficient consideration of privacy. In this study, we present an algorithm that preserves privacy in scheduling of workflows, whilst still considering customers' deadlines and cost. We evaluated our approach using real workflows running on a private HTCondor-based hybrid cloud. Results were promising and demonstrated the efficiency of our approach in not only reducing the cost of executing workflows, but also satisfying both the privacy and deadline constraints of the submitted workflows.

SUMMARY In this paper, we study CPU utilization time patterns of several MapReduce applications. After extracting running patterns of several applications, the patterns along with their statistical information are saved in a reference database to be later used to tweak system parameters to efficiently execute future unknown applications. To achieve this goal, CPU utilization patterns of new applications along with its statistical information are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different pattern lengths, dynamic time warping (DTW) is utilized for such comparison; a statistical analysis is then applied to DTWs' outcomes to select the most suitable candidates. Furthermore, under a hypothesis, we also proposed another algorithm to classify applications under similar CPU utilization patterns. Finally, dependency between minimum distance/maximum similarity of applications and scalability (in both input size and number of virtual nodes) is studied. Here, we used widely used applications (WordCount, Distributed Grep, and Terasort) as well as an Exim MainLog parsing application to evaluate our hypothesis in automatic tweaking MapReduce configuration parameters in executing similar applications scalable on both size of input data and number of virtual nodes. Results are very promising and showed the effectiveness of our approach on a private cloud with up to 25 virtual nodes. Concurrency and Computation: Practice and Experience, 2012. Copyright © 2012 John Wiley & Sons, Ltd.

With popularity of data centers, energy efficiency of Ethernet switches in them is becoming a critical issue. Most existing energy saving approaches use a centralized methodology that assumes global knowledge of data center networks. Though these approaches can achieve nearly optimal energy saving for static traffic patterns, they are not suitable when the traffic patterns can change rapidly or the data centers have a large size. To overcome these limitations, this paper proposes a novel distributed approach called eAware that dynamically idles a port or a switch to save energy by examining the queue lengths and utilizations at switch ports. Through extensive simulations in ns-2, we compare eAware with an existing energy oblivious approach, showing that eAware can save 30%-50% on the total energy consumption by switches in data centers, and only increases the average end-to-end delay of packets by 3%-20% and the packet loss ratio by 0%-0.9%.

In this paper, we study the dependency between MapReduce configuration parameters and network load of fixed-size MapReduce jobs during the shuffle phase, then we propose an analytical method to model this dependency. Our approach consists of three key phases: profiling, modeling, and prediction. In the first stage, an application is run several times with different sets of MapReduce configuration parameters (here number of map tasks and number of reduce tasks) to profile the network load of an application in the shuffle phase on a given cluster. Then, the relation between these parameters and the network load is modeled by multivariate linear regression. For evaluation, three applications (Word Count, Exim Main log parsing, and TeraSort) are utilized to evaluate our technique on a 5-node MapReduce private cluster.

The study of protein-protein interactions (PPI) is an active area of research in biology as it mediates most of the biological functions in any organism. Although, there are no concrete properties in predicting PPI, extensive wet-lab experiments suggest (with a high probability) that interacting proteins in the fine level share similar functions, cellular roles and sub-cellular locations. In this study, we developed a technique to predict PPI based on their secondary structures, co-localization, and function annotation. We proposed our approach, namely FIS-PNN, to predict the interacting proteins in yeast using hybrid machine learning algorithms. FIS-PNN has been trained and tested using 1029 proteins with 2965 known positive interactions; it could successfully predict PPI with 96% of accuracy - a level that is significantly greater than all other existing sequence-based prediction methods.

This paper describes the architecture, implementation and illustrates the usage of WebFlowAH, an environment for ad-hoc specifying and executing Web services-based business processes. WebFlowAH build on the adoption of common domain ontology to describe Web services and business processes. It enables the specification of processes in terms of high level users' goals that are expressed based on the concepts of such common domain ontology, thus independently on the syntax of the service provided operations. WebFlowAH provides a unique environment for specifying and executing goal-oriented business processes, allowing services discovery, composition and invocation on the fly.

This paper presents a novel approach, Fuzz-SSVS, to improve the secondary structure prediction of proteins. In this work, a Sugeno based Fuzzy System is trained to act as a voting system to combine results of several secondary structure prediction techniques and produce superior answers. Fuzz-SSVS is tested with three of the well-known benchmarks in this field. The results demonstrate the superiority of the proposed technique even in the case of formidable sequences.

Single nucleotide polymorphisms (SNPs) studies have been an active topic of research in the life sciences in recent years. Because SNPs are abundant, stable and sometimes can be related to specific diseases, they have been widely selected as biomarkers for multi-purpose research. As traditional methods for identifying SNPs are time-consuming and expensive, discovering SNPs from expressed sequence tags (ESTs) has became an alternative efficient way. As most EST databases do not store quality/trace files together with EST reads, several methods, like Phard, which requires corresponding sequences quality files, will not be suitable for further research purpose. Thus, computational methods that are able to obtain reliable SNPs without the need for trace/quality information are still essential. We have developed a pipeline framework, called PFSNP, to reveal reliable SNPs from EST data sets without the association of trace/quality files. PFSNP deploys several strategies, like modified neighborhood quality standard measurement and fuzzy logic, in this framework. Also, it automatically adjusts the slide window to efficiently fit different conditions of data sets. PFSNP is demonstrated by identifying SNPs from two subgroups of Oryza sativa with two different strategies as well as zebrafish. Based on our experimental results, PFSNP can obtain higher reliable results when compared to existing methods.

The study on protein-protein interactions is rapidly increasing; one of the most important findings of such study is the observation of hub proteins that play vital roles in all organisms. Identifying hub proteins may provide more information on essential proteins and lead to more efficient methods for their prediction. Here, we proposed a new network topological-based method for prediction of hub proteins in Saccharomyces cerevisiae (baker's yeast). The method, HP3NN (Hub Protein Prediction using Probabilistic Neural Network), has successfully predicts the hub proteins with accuracy of 95% (sensitivity of 1.0 and specificity of 0.89).

In this paper, we study CPU utilization time patterns of several MapReduce applications. After extracting running patterns of several applications, they are saved in a reference database to be later used to tweak system parameters to efficiently execute unknown applications in future. To achieve this goal, CPU utilization patterns of new applications are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different patterns lengths, the Dynamic Time Warping (DTW)is utilized for such comparison, a correlation analysis is then applied to DTWs' outcomes to produce feasible similarity patterns. Three real applications (Word Count, Exim Mainlogparsing and Terasort) are used to evaluate our hypothesis in tweaking system parameters in executing similar applications. Results were very promising and showed effectiveness of our approach on pseudo-distributed MapReduce platforms.

The problem of further improving the accuracy of GPS-provided coordinates of vehicles within the same VANET (Vehicular Ad-Hoc Network) is addressed. VANETs can be used to increase the accuracy of each vehicle's computed location by allowing vehicles to share information wth each other. This paper looks at improving the coordinates provided by the GPS devices, given that erroneous measurements are present in the system. The algorithm VLOCI2 is an extension of the VLOCI algorithm by providing location improvement on multi-laned roads. VLOCI2 is shown to perform efficiently when erroneous distance measurements are present in the environment/computations.

The introduction of real time communication has created additional challenges in the wireless networks area with different communication constraints. Sensor nodes spend most of their lifetime functioning as a small router to deliver packets from one node to another until the packet reaches the sink. Since sensor networks represent a new generation of time-critical applications, it is often necessary for communication to meet real time constraints as well as other constrains. Nevertheless, research dealing with providing QoS guarantees for real time traffic in sensor networks is still in its infancy. This paper presents a novel packet delivery mechanism, namely Multiple Level Stateless Protocol (MLSP), as a real time protocol for sensor networks to guarantee the quality of traffic in wireless Sensor Networks. MLSP improves the packet loss rate and handles holes in sensor networks. This paper also introduces the k-limited polling model. This model is used in a sensor network by the implementation of two queues served according to a 2-limited polling model in a sensor node. Here, two different classes of traffic are considered and the exact packet delay for each corresponding class is calculated. The analytical results are validated through an extensive simulation study.

This paper presents a novel approach for solving the Multiple Sequence Alignment (MSA) problem. K-Means clustering is combined with the Rubber Band Technique (RBT) to introduce an iterative optimization algorithm, namely RBT-Km, to find the optimal alignment for a set of input protein sequences. In this technique, the MSA problem is modeled as a Rubber Band, while the solution space is modeled as plate with several poles corresponding locations in the input sequences that are most likely to be correlated and/or biologically related. K-Means clustering is then used to discriminate biologically related locations from those that may appear by chance. RBT-Km is tested with one of the well-known benchmarks in this field (BALiBASE 2.0). The results demonstrate the superiority of the proposed technique even in the case of formidable sequences.

This paper presents a novel approach, namely SSVS, to improve the secondary structure prediction of proteins. In this work, a Radial Basis Function Neural Network is trained to combine different answers found by different secondary structure prediction techniques to produce superior answers. SSVS is tested with three of the well-known benchmarks in this field. The results demonstrate the superiority of the proposed technique even in the case of formidable sequences.

The energy consumption issue in distributed computing systems has become quite critical due to environmental concerns. In response to this, many energy-aware scheduling algorithms have been developed primarily by using the dynamic voltage-frequency scaling (DVFS) capability incorporated in recent commodity processors. The majority of these algorithms involve two passes: schedule generation and slack reclamation. The latter is typically achieved by lowering processor frequency for tasks with slacks. In this paper, we revisit this energy reduction technique from a different perspective and propose a new slack reclamation algorithm which uses a linear combination of the maximum and minimum processor frequencies to decrease energy consumption. This algorithm has been evaluated based on results obtained from experiments with three different sets of task graphs: 1,500 randomly generated task graphs, and 300 task graphs of each of two real-world applications (Gauss-Jordan and LU decomposition). The results show that the amount of energy saved in the proposed algorithm is 13.5%, 25.5% and 0.11% for random, LU decomposition and Gauss-Jordan task graphs, respectively, these percentages for the reference DVFSbased algorithm are 12.4%, 24.6% and 0.1%, respectively.

In this paper, a novel technique, namely SSPT, is introduced to predict the secondary structure (SS) of a protein just based on its primary structure. In the training phase of this technique, a novel training tool (secondary structure triangle) is trained to reflect the tendency of overlapping small amino acid windows of a sequence toward three SS formation of H/E/L. These tendencies are then augmented to form three SS signals to reflect the neighboring properties of different sections of the sequence. These signals are then used to determine the protein's class (alpha, beta, or alpha + beta) for better prediction of its structure. SSPT is tested using three well-known benchmarks (RS126, CB396, and CB513). Results are promising and authenticate the hypothesis behind this work.

In sensor network applications, data gathering mechanisms, which are based on multi-hop forwarding, can be expensive in terms of energy. This limitation challenges the use of sensor networks for applications that demand a predefined operational-lifetime. To avoid this problem, using of mobile element (ME) as a mechanical data carrier has emerged as a promising approach. However, practical considerations such as the ME speed and route planning, sensor buffer size and data frequency generation constraints impose limits on this approach. To address these issues, we propose a natural hybrid approach that combines two approaches of ME and multi-hop forwarding. We consider the problem of determining the path of the ME, in which the length of this path is bounded by pre-determined length. This path will visit a subset of the nodes. These selected nodes will work as caching points and will aggregate the other nodes' data. The caching point nodes are selected with the aim of reducing the energy expenditures due to multi-hop forwarding. To address this problem, we present a heuristic-based solution and compare its performance against the optimal solution. We obtain the optimal solution by providing an integer linear program for this problem.

Recent advances in miniaturization and low power design have led to a flurry of activity in wireless sensor networks. However, the introduction of real time communication has created additional challenges in this area. The sensor node spends most of its life in routing packets from one node to another until the packet reaches the sink In other words, we can say that it is functioning as a small router most of the time. Since sensor networks deal with time-critical applications, it is often necessary for communication to meet real time constraints. However, research dealing with providing QoS guarantees for real time traffic in sensor networks is still in its infancy. In this paper, an analytical model for implementing Priority Queueing (PQ) in a sensor node to calculate the queueing delay is presented. The model is based on M/D/l queueing system (a special class of M/G/l queueing systems). Here, two different classes of traffic are considered. The exact packet delay for corresponding classes is calculated. Further, the analytical results are validated through an extensive simulation study.

Time Series are ubiquitous, hence, similarity search is one of the biggest challenges in the area of mining time series data. This is due to the vast data size, number of sequences and number of dimensions that lead to a very costly querying process. In this paper, we demonstrate, for the first time, the use of three dimensionality reduction techniques (random projection (RP), Down sampling (DS) and Averaging (Avg)) in time series similarity searches. Two different similarity measurements are used for this investigation; dynamic time warping (DTW) and Euclidean distance. A thorough study has been conducted in this paper based on very exhaustive experiments. Results show the individual performance of Avg, RP, and DS in the two similarity measurements in different dimensions. Simulation shows that a high similarity matching accuracy can still be achieved after a significant dimension reduction onto lower dimensions.

This paper presents a novel approach to solve the Multiple Sequence Alignment (MSA) problem. The Rubber Band Technique: Index Base (RBT-I) introduced in this paper, is inspired by the elastic behavior of a Rubber Band (RB) on a plate with poles. RBT-I is an iterative optimization algorithm designed and implemented to find the optimal alignment for a set of input Protein sequences. In this technique, the alignment answer of the MSA problem is modeled as a RB, while the answer space is modeled as the plate with several poles resembling locations in the input sequences that are most likely to be correlated and/or biologically related. Fixing the head and tail of the RB at two corners of this plate, the RB is free to bend and finds its best configuration, yielding the best answer for the MSA problem. RBT-I is tested with one of the well-known benchmarks (BALiBASE 2.0) in this field. The obtained results show the superiority of the proposed technique even in the case of formidable sequences.

Abstract This paper presents a new approach to solving the mobile location management problem using the Paging Cells (PCs) scheme. In this approach (HNN‐BDT‐PC), a combination of the Hopfield Neural Network (HNN) and the author's Ball Dropping Technique (BDT) is used to solve the problem. To this end, the location management cost of a network is embedded in the HNN parameters, and by iteration, the mobile network gradually moves toward an optimal state. The approach is inspired by the phenomenon that results from the natural movement of balls when they are dropped onto a non‐even plate (a plate with troughs and crests). Each trough of the plate corresponds to a PC, and the network corresponds to the whole plate. The aim is to find optimal PC configuration (i.e., the troughs) of the network. Three main procedures are used in the optimization process; in each optimization cycle, the HNN is launched to move the balls around the plate, and, by analogy, to move the PCs around the network to find the best configuration. Copyright © 2007 John Wiley & Sons, Ltd.

This work presents a new approach to solve the location management problem by using the location areas approach. A combination of a genetic algorithm and the Hopfield neural network is used to find the optimal configuration of location areas in a mobile network. Toward this end, the location areas configuration of the network is modeled so that the general condition of all the chromosomes of each population improves rapidly by the help of a Hopfield neural network. The Hopfield neural network is incorporated into the genetic algorithm optimization process, to expedite its convergence, since the generic genetic algorithm is not fast enough. Simulation results are very promising and they lead to network configurations that are unexpected but very efficient

This work presents a new approach to solve the location management problem by using the location areas approach. A modified genetic algorithm is used to find the optimal configuration of location areas in a mobile network. The location areas configuration of the network is modeled so that the general condition of all the chromosomes of each population improves rapidly. Since a generic genetic algorithm will not be so efficient in solving this problem, several modifications have been made to the genetic optimizer to improve its performance. These modifications deal with the mutation operation where three types of mutation are considered after the crossover operation of the genetic algorithm. Simulation results are very promising and they lead to network configurations that are unanticipated

In this paper, two new approaches based on artificial neural networks for solving the puzzle problem are presented. To do this, a Hopfield neural network (HNN) is used in a certain constraint satisfaction problem of the puzzle so that the energy of a state can be interpreted as the extent to which a hypothesis fits the underlying neural formulation model. Thus, low energy values indicate a good level of constraint satisfaction. Then, inspired by the way a human being, as an intelligent system, solves a puzzle, two new hierarchical schemes are proposed. In these approaches, some intermediate stage puzzles are designed to guarantee reaching the answer. In addition, to increase the performance of the proposed algorithms and make them much more powerful, another criterion based on the Tree Search Algorithm is combined with them.

In this paper, a new approach based on artificial neural networks for solving the puzzle problem in conjunction with the tree search algorithm, is presented. For this purpose, a Hopfield neural network is used in a certain constraint satisfaction problem of the puzzle so that the energy values indicate a good level of constraint satisfaction of the puzzle problem. Also, another criterion known as "tree search algorithm", is used to solved the puzzle problem. At the end, based on the appropriate behaviors of each of the presented algorithms, these two algorithms are combined so that they generate a much more powerful algorithm than each of them individually. Finally, a comparison is made for the actual performance of the proposed algorithm and the Hopfield neural network optimizer formerly presented in [N. Sadati, J. Tahri, Australia, 1999].

In this paper, a new approach based on artificial neural networks to solve the robot motion planning problem is presented. For this purpose, a Hopfield neural network is used in a certain constraint satisfaction problem of the robot motion planning in conjunction with fuzzy modeling of the real robot's environment so that the energy of a state can be interpreted as the extent to which a hypothesis fit the underlying neural formulation model. Thus, low energy values indicate a good level of constraint satisfaction of the problem. Finally, since the obtained answer by the Hopfield neural network is not optimal, some algorithms are designed to optimize and generate the final answer.