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A low-power task scheduling algorithm for heterogeneous cloud computing

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Abstract

As a new type of computing, cloud computing has led to a major computational change. Among many technologies in cloud computing, task scheduling has always been studied as a core issue by industry and academia. In the existing research, the main goal is completion time or load balancing. However, as the expansion of cluster size, energy consumption becomes a problem that must be faced. In this paper, the first of maximum loss scheduling algorithm is proposed. The algorithm is a low-power algorithm that can greatly reduce the energy consumption of cloud computing clusters through loss comparison rule. The effect of this method is more obvious as the cluster size and the number of tasks increase. Experimental simulation results show that the proposed method is significantly better than the Max–Min, Min–Min, Sufferage and E-HEFT algorithms. Compared to Min–Min, Max–Min, Sufferage and E-HEFT algorithms, average completion time of the algorithm reduces 16%, 12%, 8% and 14%, respectively. At the same time, the load balancing effect is also better than Min–Min and Sufferage algorithms.

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Acknowledgements

This work was supported by the National Key Research and Development Program [No. 2016YFB0200902 to X. Zhang] and the National Natural Science Foundation of China [No. 61572394 to X. Dong].

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China

    Bin Liang, Xiaoshe Dong, Yufei Wang & Xingjun Zhang

  2. School of Computer Science, Xi’an Shiyou University, Xi’an, China

    Bin Liang

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  1. Bin Liang
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  2. Xiaoshe Dong
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  3. Yufei Wang
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  4. Xingjun Zhang
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Correspondence to Bin Liang.

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Liang, B., Dong, X., Wang, Y. et al. A low-power task scheduling algorithm for heterogeneous cloud computing. J Supercomput 76, 7290–7314 (2020). https://doi.org/10.1007/s11227-020-03163-8

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