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Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation

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  • Hormozi, Elham
  • Hu, Shuwen
  • Ding, Zhe
  • Tian, Yu-Chu
  • Wang, You-Gan
  • Yu, Zu-Guo
  • Zhang, Weizhe

Abstract

Energy efficiency is a critical issue in data centre management, which is the foundation for cloud computing. The VM placement has a considerable impact on a data centre's energy efficiency and resource utilisation. The assignment of VMs to PMs is an NP-hard problem without an easy way to find an optimal solution, particularly in large-scale data centres. In this study, the VM placement problem is formulated as a constrained optimisation problem. The Genetic Algorithm (GA) is a suitable method for solving this problem in terms of the quality of the solution. However, GA is time-consuming to obtain an optimal solution in the large scale optimisation problem. Therefore, this paper focuses on accelerated GA for energy-efficient VM placement. As the most time-consuming element of the GA is the calculation of its fitness function, this paper simplifies this calculation through a new fitness function in GA. Simulation results of small-, medium-, and large-scale data centres demonstrate that our accelerated GA is faster than the standard GA and gives better quality of solution than the First Fit Decreasing (FFD) algorithm, respectively. The findings of our GA with the new fitness function reveal an 8% energy saving for our GA compared to FFD and a 66% reduction in our GA execution time compared to the standard GA with standard energy formula as a fitness function. The number of generations in our GA is reduced by about 50% in comparison with the standard GA. Moreover, we started with 3000 PMs in the large-scale dataset, and only 1086 PMs were actually used after running our GA. Therefore, we may switch off far more PMs for energy savings from our GA results than those from the standard GA.

Suggested Citation

  • Hormozi, Elham & Hu, Shuwen & Ding, Zhe & Tian, Yu-Chu & Wang, You-Gan & Yu, Zu-Guo & Zhang, Weizhe, 2022. "Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation," Energy, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:energy:v:252:y:2022:i:c:s0360544222007873
    DOI: 10.1016/j.energy.2022.123884
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    References listed on IDEAS

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    1. Weiguo Fan & Edward A. Fox & Praveen Pathak & Harris Wu, 2004. "The effects of fitness functions on genetic programming‐based ranking discovery for Web search," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(7), pages 628-636, May.
    2. Sajid, Mohammad & Raza, Zahid, 2017. "Energy-aware stochastic scheduler for batch of precedence-constrained jobs on heterogeneous computing system," Energy, Elsevier, vol. 125(C), pages 258-274.
    3. Fu, Yangyang & Han, Xu & Baker, Kyri & Zuo, Wangda, 2020. "Assessments of data centers for provision of frequency regulation," Applied Energy, Elsevier, vol. 277(C).
    4. Mohd Nadhir Ab Wahab & Samia Nefti-Meziani & Adham Atyabi, 2015. "A Comprehensive Review of Swarm Optimization Algorithms," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-36, May.
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    1. Sanjoy Choudhury & Ashish Kumar Luhach & Joel J. P. C. Rodrigues & Mohammed AL-Numay & Uttam Ghosh & Diptendu Sinha Roy, 2023. "A Residual Resource Fitness-Based Genetic Algorithm for a Fog-Level Virtual Machine Placement for Green Smart City Services," Sustainability, MDPI, vol. 15(11), pages 1-21, June.

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