Telecommunications Science ›› 2022, Vol. 38 ›› Issue (12): 1-10.doi: 10.11959/j.issn.1000-0801.2022281

• Research and Development •     Next Articles

Virtual machine scheduling model based on Shapley value modified genetic algorithm in cloud computing

Kun MA1,2, Lingyu XU3, Xiaoping SHEN1,2, Zhicheng GONG1,2, Jianping LAN1,2, Shuangxi CHEN1,2,4, Jun QIAN5   

  1. 1 Jiaxing Vocational Technical College, Jiaxing 314036, China
    2 Jiaxing Key Laboratory of Industrial Internet Security, Jiaxing 314036, China
    3 Jiaxing Nanyang Polytechnic Institute, Jiaxing 314031, China
    4 Zhejiang University, Hangzhou 310058, China
    5 Jiaxing Branch of China Telecom Co., Ltd., Jiaxing 314011, China
  • Revised:2022-11-07 Online:2022-12-20 Published:2022-12-01
  • Supported by:
    Zhejiang Province “Top Soldiers” and “Leading Geese” Research and Development Projects(2022C01243);Key Research and Development Programs of Science Technology Department of Zhejiang Province(2021C01036);General Research and Development Programs Department of Education of Zhejiang Province(Y202044105);Public Welfare Research Program of Kexueju(2022AY10009);Public Welfare Research Program of Kexueju(2021AD10003);Public Welfare Research Program of Kexueju(2019AD32029)


Cloud computing system has the characteristics of large-scale servers and a wide range of users.However, it also consumes a huge number of energy, resulting in high operating costs of cloud providers and high carbon emissions issue.Cloud computing is highly virtualized.How to allocate and manage the virtual resources to ensure efficient physical resource utilization and energy consumption control is a multi-parameter game problem, and it is also a research hotspot in this field.A virtual machine scheduling model and the corresponding SV-GA were proposed, which could calculate the contribution value of the physical machine participating in the work through the Shapley value, and modify the probability parameter of the mutation step in the genetic algorithm through the contribution value, so as to complete the task of virtual machine scheduling.The experimental results show that during the comparison with Max-Min, LrMmt and DE, the SV-GA shows its excellent performance in the multi-parameter game including migration time, times, SLA violation rate and energy consumption in the virtual machine scheduling process.

Key words: cloud computing, multi-parameter game, virtual machine scheduling, Shapley value, genetic algorithm

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