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EM_WOA: A budget-constrained energy consumption optimization approach for workflow scheduling in clouds

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Abstract

Cloud computing brings convenience to users worldwide in the era of green computing. However, massive data computation requires equally large amounts of energy, thereby introducing challenges to workflow scheduling in the cloud under budget constraints. On the basis of whale optimization algorithm, this study proposes a new metaheuristic workflow scheduling algorithm called energy minimization whale optimization algorithm (EM_WOA), which reduces energy consumption in the cloud. EM_WOA applies two strategies, namely, service instance selection and energy consumption reduction. In the first phase, candidate virtual machines are selected based on the budget constraints given by the user. In the second phase, the optimal scheduling solution is identified by changing the position of the whale. Extensive experiments are performed in real workflow applications, and results show that EM_WOA is more efficient and competitive than state-of-the-art meta-heuristic algorithms.

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Acknowledgements

The authors would like to thank three anonymous reviewers for their suggestions to improve the manuscript. This work was partially funded by the National Key R&D Program of China (Grant No. 2018YFB1003401), the National Natural Science Foundation of China (Grant Nos. 61702178, 61871432, 62072172), the Natural Science Foundation of Hunan Province (Grant Nos. 2019JJ50123, 2019JJ60054), the Research Foundation of Education Bureau of Hunan Province (Grant Nos. 20C0625, 18C0528), and in part by China Scholarship Council (Grant No. 201808430297).

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  1. School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, China

    Longxin Zhang, Lan Wang, Mansheng Xiao, Zhicheng Wen & Cheng Peng

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  1. Longxin Zhang
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  2. Lan Wang
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  3. Mansheng Xiao
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  4. Zhicheng Wen
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  5. Cheng Peng
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Correspondence to Longxin Zhang or Lan Wang.

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This article is part of the Topical Collection: Special Issue on Green Edge Computing.

Guest Editors: Zhiyong Yu, Liming Chen, Sumi Helal, and Zhiwen Yu

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Zhang, L., Wang, L., Xiao, M. et al. EM_WOA: A budget-constrained energy consumption optimization approach for workflow scheduling in clouds. Peer-to-Peer Netw. Appl. 15, 973–987 (2022). https://doi.org/10.1007/s12083-021-01267-3

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