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A novel cloud workflow scheduling algorithm based on stable matching game theory

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Abstract

Workflow scheduling is one of the most popular and challenging problems in cloud computing. However, among the studies on cloud workflow scheduling, very few consider the fairness among workflow tasks which could significantly delay the workflows and hence deteriorates user satisfaction. In this paper, we propose a workflow scheduling algorithm based on stable matching game theory to minimize workflow makespan and ensure the fairness among the tasks. The local optimization methods based on critical path and task duplication are developed to improve the performance of the algorithm. In addition, a novel evaluation metric is proposed to measure the fairness among workflow tasks. Comprehensive experiments are conducted to compare the performance of the proposed algorithm with other four representative algorithms. Experimental results demonstrate that our algorithm outperforms the other compared algorithms in terms of all three performance metrics under different workflow applications.

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Acknowledgements

This work is supported by the National Natural Science Foundation under Grants 71971002 and 61872002, the Humanity and Social Science Youth Foundation of Ministry of Education of China under Grant 15YJC630041 and the Natural Science Foundation of Anhui Provincial Department of Education under Grant KJ2015A062.

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Author notes

  1. Zhao-hong Jia and Lei Pan have contributed equally to this work.

Authors and Affiliations

  1. Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, Hefei, China

    Zhao-hong Jia & Lei Pan

  2. School of Computer Science and Technology, Anhui University, Hefei, 230039, China

    Zhao-hong Jia, Lei Pan & Xue-jun Li

  3. School of Information Technology, Deakin University, Geelong, Australia

    Xiao Liu

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  1. Zhao-hong Jia
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  2. Lei Pan
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Correspondence to Xiao Liu.

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Jia, Zh., Pan, L., Liu, X. et al. A novel cloud workflow scheduling algorithm based on stable matching game theory. J Supercomput 77, 11597–11624 (2021). https://doi.org/10.1007/s11227-021-03742-3

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