Reliability-aware and Deadline-constrained workflow scheduling in Mobile Edge Computing

Peng, Qinglan; Jiang, Honghua; Chen, Mujie; Liang, Jinghua; Xia, Yunni

doi:10.1109/icnsc.2019.8743291

Cited by 25 publications

(7 citation statements)

References 16 publications

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“…CloudSim‐based simulations on famous five workflows were executed, and the obtained consequences were presented to show the efficiency of the algorithm on an average number of established replicas, costs resulting from abandoned replicas, makespans, deadline violation rates, and resource utilization rates. Latterly, a workflow scheduling algorithm was proposed and formulated for evaluating resource reliability in a mobile edge computing (MEC) environment 28 . It was developed by a Krill‐based algorithm to solve workflow scheduling as an optimization problem.…”

Section: Related Workmentioning

confidence: 99%

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A new reliability‐based task scheduling algorithm in cloud computing

Motlagh

Movaghar

Rahmani

2021

Int J Communication

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Summary In the last decade, the scale of heterogeneous computing (HC) systems such as heterogeneous cloud computing environments was growing like never before. So network failures are unavoidable in such systems, which affect system reliability. Since the task scheduling algorithm in HC is challenging, we investigate a new reliability‐aware task scheduling algorithm (RATSA) in this paper. RATSA is designed to schedule tasks on directed acyclic graphs (DAGs) by using the shuffled frog‐leaping algorithm (SFLA) and genetic algorithm (GA) as evolutionary algorithms. The population‐based SFLA‐GA is applied to optimize makespan in the RATSA as an NP‐complete problem. Moreover, the proposed algorithm exploits a new heuristic‐based earliest finish time technique for task mapping to virtual machines (VMs) section to decrease the failure rate. Experimental results on random DAGs indicate that RATSA improves some current algorithms in terms of reliability and has acceptable performance in makespan. The results reveal that the RATSA decreases the overall failure rate by 43% compared to some current task scheduling algorithms.

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Section: Related Workmentioning

confidence: 99%

“…Latterly, a workflow scheduling algorithm was proposed and formulated for evaluating resource reliability in a mobile edge computing (MEC) environment. 28 It was developed by a Krill-based algorithm to solve workflow scheduling as an optimization problem. Experiments were performed on real-world scientific applications.…”

Section: Related Workmentioning

confidence: 99%

A new reliability‐based task scheduling algorithm in cloud computing

Motlagh

Movaghar

Rahmani

2021

Int J Communication

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“…Experimental results demonstrated that DNCPSO could achieve better performance than other classic algorithms. Peng et al [26] proposed a node reliability model to evaluate resource reliability in Mobile Edge Computing (MEC) environments, defining workflow scheduling as an optimization problem and solving it by an algorithm based on Krill-Herd [27]. Through experiments based on real workflow applications and mobile user contract tracking, it had proven that the performance of this method was significantly better than the traditional methods in terms of success rate and makespan.…”

Section: Related Workmentioning

confidence: 99%

A Fuzzy Scheduling Strategy for Intelligent Workflow Decision Making in Uncertain Edge-Cloud Environments

Li¹,

Lin²,

Chen³

2021

Preprint

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Workflow decision making is critical to performing many practical workflow applications. Scheduling in edgecloud environments can address the high complexity of workflow applications, while decreasing the data transmission delay between the cloud and end devices. However, due to the heterogeneous resources in edge-cloud environments and the complicated data dependencies between the tasks in a workflow, significant challenges for workflow scheduling remain, including the selection of an optimal tasks-servers solution from the possible numerous combinations. Existing studies are mainly done subject to rigorous conditions without fluctuations, ignoring the fact that workflow scheduling is typically present in uncertain environments. In this study, we focus on reducing the execution cost of workflow applications mainly caused by task computation and data transmission, while satisfying the workflow deadline in uncertain edge-cloud environments. The Triangular Fuzzy Numbers (TFNs) are adopted to represent the task processing time and data transferring time. A cost-driven fuzzy scheduling strategy based on an Adaptive Discrete Particle Swarm Optimization (ADPSO) algorithm is proposed, which employs the operators of Genetic Algorithm (GA). This strategy introduces the randomly two-point crossover operator, neighborhood mutation operator, and adaptive multipoint mutation operator of GA to effectively avoid converging on local optima. The experimental results show that our strategy can effectively reduce the workflow execution cost in uncertain edge-cloud environments, compared with other benchmark solutions.

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“…To minimize the overall error probability in a multiserver mobile edge computing (MEC) network, where the wireless data transmission/offloading was carried by finite blocklength (FBL) codes, Zhu et al [20] characterized the FBL reliability of the transmission phase and investigated the extreme event of queue length violation in the computation phase by applying extreme value theory and provided an optimal framework for deciding time allocation and server selection. Peng et al [8] proposed a novel method to evaluate the resource reliability in mobile edge computing environment and addressed the workflow scheduling problem by using a Krill-based algorithm. Kouloumpris et al [21] considered an architecture consisting of an edge node, an intermediate node (hub), and the cloud infrastructure and then used a mathematical programming-based framework to derive an applicationreliability-optimal task allocation based on multiple operational constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, the edge computing paradigm has evolved as an increasingly popular force for supporting and enabling business process and scientific workflow execution [3][4][5]. A workflow is a set of dependent or independent tasks illustrated as a directed acyclic graph (DAG) [6][7][8], in which the nodes indicate the tasks and a directed arch represents the interdependency among the corresponding tasks. Workflow scheduling involves mapping workflow tasks to computational resources for execution, and the resulting optimization problem is well acknowledged to be NP-hard.…”

Section: Introductionmentioning

confidence: 99%

A Novel Coevolutionary Approach to Reliability Guaranteed Multi-Workflow Scheduling upon Edge Computing Infrastructures

Wang

Zheng

Chen

et al. 2020

Security and Communication Networks

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Recently, mobile edge computing (MEC) is widely believed to be a promising and powerful paradigm for bringing enterprise applications closer to data sources such as IoT devices or local edge servers. It is capable of energizing novel mobile applications, especially the ultra-latency-sensitive ones, by providing powerful local computing capabilities and lower end-to-end delays. Nevertheless, various challenges, especially the reliability-guaranteed scheduling of multitask business processes in terms of, e.g., workflows, upon distributed edge resources and servers, are yet to be carefully addressed. In this paper, we propose a novel edge-environment-based multi-workflow scheduling method, which incorporates a reliability estimation model for edge-workflows and a coevolutionary algorithm for yielding scheduling decisions. The proposed approach aims at maximizing the reliability, in terms of success rates, of services deployed upon edge infrastructures while minimizing service invocation cost for users. We conduct simulative experimental case studies based on multiple well-known scientific workflow templates and a well-known dataset of edge resource locations as well. Simulative results clearly suggest that our proposed approach outperforms traditional ones in terms of workflow success rate and monetary cost.

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Reliability-aware and Deadline-constrained workflow scheduling in Mobile Edge Computing

Cited by 25 publications

References 16 publications

A new reliability‐based task scheduling algorithm in cloud computing

A new reliability‐based task scheduling algorithm in cloud computing

A Fuzzy Scheduling Strategy for Intelligent Workflow Decision Making in Uncertain Edge-Cloud Environments

A Novel Coevolutionary Approach to Reliability Guaranteed Multi-Workflow Scheduling upon Edge Computing Infrastructures

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