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A gradient-based optimization approach for task scheduling problem in cloud computing

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Abstract

Task scheduling in cloud computing is a key component that affects the resource usage and operating costs of the system. In order to promote the efficiency of task executions in the cloud system, many heuristic algorithms and their variants have been used to optimize scheduling. Since makespan is the vital metric of cloud computing system, most of the relevant research focuses on improving this performance. The gradient-based optimization (GBO) has a faster convergence rate, and can avoid prematurely falling into the local optimum. In this work, we propose a task scheduling based on the GBO in the cloud to improve the makespan performance. Since the GBO is proposed for continuous optimization, rounding-off method is used to convert the real “vector” value of the GBO to the nearest integer value, thereby representing the solution of the task scheduling problem. To evaluate the performance of the proposed GBO-based scheduling method, two experimental cases are performed. The results of the two experimental cases show that compared with current heuristic algorithms, the GBO has better convergence speed and accuracy in searching for the optimal task scheduling solution, especially in the presence of large-scale tasks.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the anonymous referees for their valuable comments and suggestions.

Funding

This research is supported in part by the National Natural Science Foundation of China (62006096), in part by the Natural Science Foundation of Fujian Province of China (2020J01699, 2020J01700, 2020J05146), in part by the Education, the Scientific Research Project of Middle-aged and Young Teachers in Fujian Province (JAT190320, JAT200244) and in part by the National Foundation Cultivation Program of Jimei University (ZP2022007) and in part by the Innovation Strategy Research Project of Fujian Provincial Department of Science and Technology (2020R0066).

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Authors and Affiliations

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

    Xingwang Huang, Yangbin Lin, Zongliang Zhang, Xiaoxi Guo & Shubin Su

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  1. Xingwang Huang
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  2. Yangbin Lin
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  3. Zongliang Zhang
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  4. Xiaoxi Guo
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  5. Shubin Su
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Contributions

XH contributed to the modeling, conducted the experiments, performed the data analysis and wrote the manuscript; YL, ZZ and XG contributed to analysis through constructive discussions. SS contributed to the conceptualization, writing review and visualization.

Corresponding author

Correspondence to Shubin Su.

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The work is a novel work and has not been published elsewhere nor is it currently under review for publication elsewhere.

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Huang, X., Lin, Y., Zhang, Z. et al. A gradient-based optimization approach for task scheduling problem in cloud computing. Cluster Comput 25, 3481–3497 (2022). https://doi.org/10.1007/s10586-022-03580-9

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