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Hypergraph-partitioning-based online joint scheduling of tasks and data

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Abstract

Recently, wide-area distributed computing environments have become popular owing to their huge resource capability. In a wide-area distributed computing environment, joint scheduling of tasks and data is the main strategy to improve system performance. However, the geographically distributed diverse resources exhibit high variations, making it challenging to design efficient joint scheduling of tasks and data. To accurately adapt to the dynamic variations of geographically distributed diverse resources and achieve a high system performance, this study proposes a hypergraph-partitioning-based online joint scheduling method. The proposed method constructs a hypergraph of geographically distributed tasks, data, and diverse resources to clearly describe the correlation among the three elements and quantitatively reflect the time cost of different process in the environment. The hypergraph is dynamically updated according to the generated scheduling scheme and the collected information to reflect the dynamic variations of resource states. Then, a hypergraph partition optimization mechanism is proposed to generate efficient joint scheduling schemes, thus reducing the overall completion time in the system. The experimental results indicate that compared with the state-of-the-art joint scheduling methods, the proposed method reduces the overall completion time by up to 25.67% and significantly reduces the task waiting time, although it makes a concession in the data migration time.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 61772053 and Grant No. 62104014, and the fund of the State Key Laboratory of Software Development Environment under Grant No. SKLSDE-2020ZX-15. This work was also supported by Natural Science Foundation of Shaanxi Province of China(2021JM-344) and Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data(No.IPBED7).

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Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China

    Yao Song, Liang Wang, Limin Xiao & Jinquan Wang

  2. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Yao Song, Liang Wang, Limin Xiao & Jinquan Wang

  3. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Wei Wei

  4. Institute of Computational Intelligence, Czestochowa University of Technology, Czestochowa, 42-200, Poland

    Rafał Scherer

  5. Smart City College, Beijing Union University, Beijing, 100101, China

    Guangjun Qin

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  1. Yao Song
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Correspondence to Liang Wang or Limin Xiao.

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Song, Y., Wang, L., Xiao, L. et al. Hypergraph-partitioning-based online joint scheduling of tasks and data. J Supercomput 78, 16088–16117 (2022). https://doi.org/10.1007/s11227-022-04460-0

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