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Optimization of Big Data Parallel Scheduling Based on Dynamic Clustering Scheduling Algorithm

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Abstract

In today’s data age, the big data processing analysis framework plays an important role in mass information processing, along with the increasing of massive data. “Sharing Data” is proposed to enhance the performance of data processing through structured data scheduling. However, such approach makes the higher communication cost and buffer cost for the extra data copy and buffering. Hence, in the big data analysis environment, this paper uses based on the correlation of data, Dynamic Cluster Scheduling Algorithm(DCSA) is proposed for parallel optimization of big data tasks. Firstly, a dynamic data queue based on the server’s request database is generated. The priority of data item and size of data item are as the considerations of dynamic data queue for data clustering association. And then the weights are introduced, the dynamic data item is made equalization to provide the basis for the multi-channel optimal scheduling. Secondly, according to the relevance of the data items, the mechanism of data optimized placement is used to make the data which are aggregated in the same frame. After the placement is completed, the dynamic data is uniformly scheduled to minimize the cost at the time of migration, with the local characteristics of the data item as constraints. Through the target iteration, the optimal scheduling scheme is adjusted, and finally to achieve multi-channel optimal scheduling. Experiments show that the proposed method enables dynamic data to achieve optimal scheduling.

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China under Grant No. 61972293.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Bayi Road, Wuhan, 430072, Hubei, China

    Fang Liu & Yanxiang He

  2. Information Engineering Department, Wuhan Institute of City, Huangjia Dawan Road, Wuhan, 430083, Hubei, China

    Fang Liu

  3. College of Computer Science, Wuhan University of Science and Technology, Huangjiahu West Road, Wuhan, 430065, Hubei, China

    Feihu Huang

  4. Department of Computer Science, Kennesaw State University, 1100 South Parkway, Marietta, 30060, Georgia, USA

    Jing He

  5. Admission Office, Wuhan Institute of City, Huangjia Dawan Road, Wuhan, 430083, Hubei, China

    Xing Gao

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  1. Fang Liu
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Correspondence to Yanxiang He.

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This article is part of the Topical Collection on: Big Data Security Track

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Liu, F., He, Y., He, J. et al. Optimization of Big Data Parallel Scheduling Based on Dynamic Clustering Scheduling Algorithm. J Sign Process Syst 94, 1243–1251 (2022). https://doi.org/10.1007/s11265-022-01765-4

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  • DOI: https://doi.org/10.1007/s11265-022-01765-4

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