• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Cloud Task Scheduling Based on Proximal Policy Optimization Algorithm for Lowering Energy Consumption of Data Center


Abstract

As a part of cloud computing technology, algorithms for cloud task scheduling place an important influence on the area of cloud computing in data centers. In our earlier work, we proposed DeepEnergyJS, which was designed based on the original version of the policy gradient and reinforcement learning algorithm. We verified its effectiveness through simulation experiments. In this study, we used the Proximal Policy Optimization (PPO) algorithm to update DeepEnergyJS to DeepEnergyJSV2.0. First, we verify the convergence of the PPO algorithm on the dataset of Alibaba Cluster Data V2018. Then we contrast it with reinforcement learning algorithm in terms of convergence rate, converged value, and stability. The results indicate that PPO performed better in training and test data sets compared with reinforcement learning algorithm, as well as other general heuristic algorithms, such as First Fit, Random, and Tetris. DeepEnergyJSV2.0 achieves better energy efficiency than DeepEnergyJS by about 7.814%.


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Cite this article

[IEEE Style]
Y. Yang, C. He, B. Yin, Z. Wei and B. Hong, "Cloud Task Scheduling Based on Proximal Policy Optimization Algorithm for Lowering Energy Consumption of Data Center," KSII Transactions on Internet and Information Systems, vol. 16, no. 6, pp. 1877-1891, 2022. DOI: 10.3837/tiis.2022.06.006.

[ACM Style]
Yongquan Yang, Cuihua He, Bo Yin, Zhiqiang Wei, and Bowei Hong. 2022. Cloud Task Scheduling Based on Proximal Policy Optimization Algorithm for Lowering Energy Consumption of Data Center. KSII Transactions on Internet and Information Systems, 16, 6, (2022), 1877-1891. DOI: 10.3837/tiis.2022.06.006.

[BibTeX Style]
@article{tiis:25759, title="Cloud Task Scheduling Based on Proximal Policy Optimization Algorithm for Lowering Energy Consumption of Data Center", author="Yongquan Yang and Cuihua He and Bo Yin and Zhiqiang Wei and Bowei Hong and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.06.006}, volume={16}, number={6}, year="2022", month={June}, pages={1877-1891}}