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Cluster equilibrium scheduling method based on backpressure flow control in railway power supply systems

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  • Zhijian Qu
  • Hanxin Liu
  • Hanlin Wang
  • Xinqiang Chen
  • Rui Chi
  • Zixiao Wang

Abstract

The purpose of the study is to solve problems, i.e., increasingly significant processing delay of massive monitoring data and imbalanced tasks in the scheduling and monitoring center for a railway network. To tackle these problems, a method by using a smooth weighted round-robin scheduling based on backpressure flow control (BF-SWRR) is proposed. The method is developed based on a model for message queues and real-time streaming computing. By using telemetry data flow as input data sources, the fields of data sources are segmented into different sets by using a distributed model of stream computing parallel processing. Moreover, the round-robin (RR) scheduling method for the distributed server is improved. The parallelism, memory occupancy, and system delay are tested by taking a high-speed train section of a certain line as an example. The result showed that the BF-SWRR method for clusters can control the delay to within 1 s. When the parallelism of distributed clusters is set to 8, occupancy rates of the CPU and memory can be decreased by about 15%. In this way, the overall load of the cluster during stream computing is more balanced.

Suggested Citation

  • Zhijian Qu & Hanxin Liu & Hanlin Wang & Xinqiang Chen & Rui Chi & Zixiao Wang, 2020. "Cluster equilibrium scheduling method based on backpressure flow control in railway power supply systems," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-23, December.
    • Handle: RePEc:plo:pone00:0243543
    DOI: 10.1371/journal.pone.0243543
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    References listed on IDEAS

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    1. Juan Wen & Yanghong Tan & Lin Jiang, 2016. "A Reconfiguration Strategy of Distribution Networks Considering Node Importance," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-19, December.
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    Cited by:

    1. Zhengyu Hu & Wenrui Liu & Shengchen Ling & Kuan Fan, 2021. "Research on multi-objective optimal scheduling considering the balance of labor workload distribution," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-15, August.

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