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Task scheduling in cloud computing using particle swarm optimization with time varying inertia weight strategies

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Abstract

Cloud computing is an efficient technology to serve the requirement of big data applications. Minimizing the makespan of the cloud system while increasing resource utilization is important to reduce costs. In this case, task scheduling is a challenging task to meet the requirement because it requires both effectiveness and efficiency. This article proposes a task scheduler with several discrete variants of the particle swarm optimization (PSO) algorithm for task scheduling in cloud computing. In order to evaluate the performance, these approaches were compared with three well-known heuristic algorithms on task scheduling problems. Experiment results demonstrate the efficiency and effectiveness of the proposed approaches. For the proposed PSO-based scheduler, an appropriate choice is to use the logarithm decreasing strategy to provide an optimal scheduling scheme. The average makespan of the proposed PSO-based scheduler that adopts logarithm decreasing strategy is reduced by 19.12%, 21.42% and 15.14% relative to the compared gravitational search algorithm, artificial bee colony algorithm and dragonfly algorithm respectively.

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Acknowledgements

Project supported by the Scientific Research Foundation of Jimei University, China under Grant ZQ2019006. And the authors would like to thank the anonymous referees for their valuable comments and suggestions.

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Authors and Affiliations

  1. Computer Engineering College, Jimei University, 185 Yinjiang Rd., Jimei District, Xiamen, 361021, China

    Xingwang Huang & Hefeng Chen

  2. Information Engineering College, Jimei University, 185 Yinjiang Rd., Jimei District, Xiamen, 361021, China

    Chaopeng Li

  3. Search Science Department, JD.com, Building A, Beichen Century Center, 8 Beichen West Rd., Chaoyang District, Beijing, 100101, China

    Dong An

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  1. Xingwang Huang
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  3. Hefeng Chen
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  4. Dong An
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Correspondence to Xingwang Huang.

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Huang, X., Li, C., Chen, H. et al. Task scheduling in cloud computing using particle swarm optimization with time varying inertia weight strategies. Cluster Comput 23, 1137–1147 (2020). https://doi.org/10.1007/s10586-019-02983-5

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  • DOI: https://doi.org/10.1007/s10586-019-02983-5

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