A Particle Grey Wolf Hybrid Algorithm for Workflow Scheduling in Cloud Computing

被引：13

作者：

Arora, Neeraj ^{[1
]}

Banyal, Rohitash Kumar ^{[2
]}

机构：

[1] Vardhman Mahaveer Open Univ, Kota, Rajasthan, India

[2] Rajasthan Tech Univ, Kota, Rajasthan, India

来源：

WIRELESS PERSONAL COMMUNICATIONS | 2022年 / 122卷 / 04期

基金：

英国科研创新办公室;

关键词：

Workflow scheduling; Meta-heuristic algorithms; Particle swarm optimization; Grey wolf optimization; Cloud Computing;

D O I：

10.1007/s11277-021-09065-z

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

A workflow consists of a set of tasks that are dependent on each other and scheduling these dependent tasks to the virtual machines is one of the complex problems in cloud computing. Moreover, workflow scheduling becomes more complex with the increasing number of tasks and virtual machines and considered to be an NP-hard problem. Therefore, the meta-heuristic approaches have been used to find out optimal scheduling of workflow schedules. The proposed algorithm named PSO-GWO is the combination of two well-known meta-heuristic algorithms Particle Swarm Optimization and Grey Wolf Optimization. The experiment result shows that the PSO-GWO algorithm decreases the average total execution cost and average total execution time in comparison to standard Particle Swarm Optimization and Grey Wolf Optimization algorithm.

引用

页码：3313 / 3345

页数：33

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