A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Xuan Chen, Long Cheng*, Cong Liu, Qingzhi Liu, Jinwei Liu, Ying Mao, John Murphy

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number8961103
Pages (from-to)3117-3128
Number of pages12
JournalIEEE Systems Journal
Issue number3
Publication statusPublished - Sep 2020


  • Cloud computing
  • metaheuristics
  • multiobjective optimization
  • task scheduling
  • whale optimization algorithm

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