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Load Balancing Oriented Predictive Routing Algorithm for Data Center Networks

Author

Listed:
  • Yazhi Liu

    (Department of Computer Science and Technology, North China University of Science and Technology, Tangshan 063000, China)

  • Jiye Zhang

    (Department of Computer Science and Technology, North China University of Science and Technology, Tangshan 063000, China)

  • Wei Li

    (Department of Computer Science and Technology, North China University of Science and Technology, Tangshan 063000, China)

  • Qianqian Wu

    (Department of Computer Science and Technology, North China University of Science and Technology, Tangshan 063000, China)

  • Pengmiao Li

    (Department of Computer Science and Technology, North China University of Science and Technology, Tangshan 063000, China)

Abstract

A data center undertakes increasing background services of various applications, and the data flows transmitted between the nodes in data center networks (DCNs) are consequently increased. At the same time, the traffic of each link in a DCN changes dynamically over time. Flow scheduling algorithms can improve the distribution of data flows among the network links so as to improve the balance of link loads in a DCN. However, most current load balancing works achieve flow scheduling decisions to the current links on the basis of past link flow conditions. This situation impedes the existing link scheduling methods from implementing optimal decisions for scheduling data flows among the network links in a DCN. This paper proposes a predictive link load balance routing algorithm for a DCN based on residual networks (ResNet), i.e., the link load balance route (LLBR) algorithm. The LLBR algorithm predicts the occupancy of the network links in the next duty cycle, according to the ResNet architecture, and then the optimal traffic route is selected according to the predictive network environment. The LLBR algorithm, round-robin scheduling (RRS), and weighted round-robin scheduling (WRRS) are used in the same experimental environment. Experimental results show that compared with the WRRS and RRS, the LLBR algorithm can reduce the transmission time by approximately 50%, reduce the packet loss rate from 0.05% to 0.02%, and improve the bandwidth utilization by 30%.

Suggested Citation

  • Yazhi Liu & Jiye Zhang & Wei Li & Qianqian Wu & Pengmiao Li, 2021. "Load Balancing Oriented Predictive Routing Algorithm for Data Center Networks," Future Internet, MDPI, vol. 13(2), pages 1-13, February.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:2:p:54-:d:503702
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    References listed on IDEAS

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    1. Hadar Sufiev & Yoram Haddad & Leonid Barenboim & José Soler, 2019. "Dynamic SDN Controller Load Balancing," Future Internet, MDPI, vol. 11(3), pages 1-21, March.
    2. Babangida Isyaku & Mohd Soperi Mohd Zahid & Maznah Bte Kamat & Kamalrulnizam Abu Bakar & Fuad A. Ghaleb, 2020. "Software Defined Networking Flow Table Management of OpenFlow Switches Performance and Security Challenges: A Survey," Future Internet, MDPI, vol. 12(9), pages 1-30, August.
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    Cited by:

    1. Mahbod, Muhammad Haiqal Bin & Chng, Chin Boon & Lee, Poh Seng & Chui, Chee Kong, 2022. "Energy saving evaluation of an energy efficient data center using a model-free reinforcement learning approach," Applied Energy, Elsevier, vol. 322(C).

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