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Task-Driven Virtual Machine Optimization Placement Model and Algorithm

Author

Listed:
  • Ran Yang

    (School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China)

  • Zhaonan Li

    (School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China)

  • Junhao Qian

    (School of Internet of Things Engineering, Jiangnan University, Wuxi 214000, China)

  • Zhihua Li

    (School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China)

Abstract

In cloud data centers, determining how to balance the interests of the user and the cloud service provider is a challenging issue. In this study, a task-loading-oriented virtual machine (VM) optimization placement model and algorithm is proposed integrating consideration of both VM placement and the user’s computing requirements. First, the VM placement is modeled as a multi-objective optimization problem to minimize the makespan of the loading tasks, user rental costs, and energy consumption of cloud data centers; then, an improved chaos-elite NSGA-III (CE-NSGAIII) algorithm is presented by casting the logistic mapping-based population initialization (LMPI) and the elite-guided algorithm in NSGA-III; finally, the presented CE-NSGAIII is employed to solve the aforementioned optimization model, and further, through combination of the above sub-algorithms, a CE-NSGAIII-based VM placement method is developed. The experiment results show that the Pareto solution set obtained using the CE-NSGAIII exhibits better convergence and diversity than those of the compared algorithms and yields an optimized VM placement scheme with shorter makespan, less user rental costs, and lower energy consumption.

Suggested Citation

  • Ran Yang & Zhaonan Li & Junhao Qian & Zhihua Li, 2025. "Task-Driven Virtual Machine Optimization Placement Model and Algorithm," Future Internet, MDPI, vol. 17(2), pages 1-30, February.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:2:p:73-:d:1586153
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    References listed on IDEAS

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    1. Jiahao Fan & Ying Li & Tan Wang, 2021. "An improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-52, November.
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