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An Improved Cluster Load Balancing Scheduling Algorithm

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Neural Computing for Advanced Applications (NCAA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1449))

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Abstract

People’s lives and work are closely connected to the network, and network activities take up an increasingly large portion of their daily lives, generating large amounts of data. This large amount of data puts tremendous pressure on server clusters, which leads to resource allocation problems. Existing load-balancing algorithms take simple factors into account and do not take into account the server load and the resource consumption of the request. This paper proposes a PSO-GA (Particle Swarm Optimization-Genetic Algorithm) based LVS (Linux Virtual Server) cluster load-balancing scheduling algorithm to quantify the different scheduling options by constructing a resource balance model and an adaptation function. The PSO-GA algorithm is used to solve the adaptation function to obtain the optimal weights. The load balancer schedules requests according to the weights to achieve Linux virtual server cluster load balancing.

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Author information

Authors and Affiliations

  1. Chongqing University of Posts and Telecommunications, Chongwen Road.2, CHN, Chongqing, China

    Wei Fu & Xunhang Cui

Authors
  1. Wei Fu
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  2. Xunhang Cui
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Corresponding author

Correspondence to Wei Fu .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Nanfang College of Sun Yat-sen University, Guangzhou, China

    Zhi Yang

  3. Hefei University of Technology, Hefei, China

    Zhao Zhang

  4. Chongqing University, Chongqing, China

    Zhou Wu

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

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Fu, W., Cui, X. (2021). An Improved Cluster Load Balancing Scheduling Algorithm. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_28

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  • DOI: https://doi.org/10.1007/978-981-16-5188-5_28

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5187-8

  • Online ISBN: 978-981-16-5188-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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