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

Journal article

Hormozi, Elham, Hu, Shuwen, Ding, Zhe, Tian, Yu-Chu, Wang, You-Gan, Yu, Zu-Guo and Zhang, Weizhe. (2022). Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation. Energy. 252, pp. 1-15. https://doi.org/10.1016/j.energy.2022.123884
AuthorsHormozi, Elham, Hu, Shuwen, Ding, Zhe, Tian, Yu-Chu, Wang, You-Gan, Yu, Zu-Guo and 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.

KeywordsCloud computing; Data centre; Energy efficiency; Fitness function; Genetic algorithm; Virtual machine placement
Year01 Jan 2022
JournalEnergy
Journal citation252, pp. 1-15
PublisherElsevier
ISSN0360-5442
Digital Object Identifier (DOI)https://doi.org/10.1016/j.energy.2022.123884
Scopus EID2-s2.0-85129305256
Web address (URL)https://www.sciencedirect.com/science/article/pii/S0360544222007873
Open accessPublished as non-open access
Research or scholarlyResearch
Page range1-15
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File Access Level
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Output statusPublished
Publication dates
Print28 Apr 2022
Publication process dates
Accepted29 Mar 2022
Deposited17 Jan 2023
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Additional information

This work was supported in part by Queensland University of Technology under the Edge Grant scheme.

© 2022 Published by Elsevier Ltd. All rights reserved.

Place of publicationUnited Kingdom
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