Paper Info
Title
A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems
Abstract
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 <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> mproved <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</b> OA for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> loud 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.
Year
DOI
Venue
2020
10.1109/JSYST.2019.2960088
IEEE Systems Journal
Keywords
DocType
Volume
Cloud computing,metaheuristics,multiobjective optimization,task scheduling,whale optimization algorithm
Journal
14
Issue
ISSN
Citations 
3
1932-8184
6
PageRank 
References 
Authors
0.40
0
7
Name
Order
Citations
PageRank
Xuan Chen160.40
Long Cheng29116.99
Cong Liu360.40
Qingzhi Liu460.40
Jinwei Liu5147.78
Ying Mao6308.54
John Murphy759752.43