标题:A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems
作者:Chen, Xuan; Cheng, Long; Liu, Cong; Liu, Qingzhi; Liu, Jinwei; Mao, Ying; Murphy, John
作者机构:[Chen, Xuan] Zhejiang Ind Polytech Coll, Design & Art, Shaoxing 312000, Peoples R China.; [Cheng, Long] Dublin City Univ, Sch Comp, Dublin D09 NA55, 更多
通讯作者:Cheng, Long
通讯作者地址:Cheng, L (corresponding author), Dublin City Univ, Sch Comp, Dublin D09 NA55, Ireland.
来源:IEEE SYSTEMS JOURNAL
出版年:2020
卷:14
期:3
页码:3117-3128
DOI:10.1109/JSYST.2019.2960088
关键词:Cloud computing; metaheuristics; multiobjective optimization; task; scheduling; whale optimization algorithm
摘要:Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this article, for the first time, we apply the latest metaheuristics whale optimization algorithm (WOA) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called Improved WOA for Cloud task scheduling (IWC) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks.
收录类别:EI;SCOPUS;SCIE
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090950666&doi=10.1109%2fJSYST.2019.2960088&partnerID=40&md5=75f5f938fe2243a44f5095c0a12e69b1
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