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Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation
Hormozi, Elham Hu, Shuwen Ding, Zhe Tian, Yu-Chu Wang, You-Gan Yu, Zu-Guo Zhang, Weizhe
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.
Keywords
Cloud computing, Data centre, Energy efficiency, Fitness function, Genetic algorithm, Virtual machine placement
Date
2022
Type
Journal article
Journal
Book
Volume
252
Issue
Page Range
1-15
Article Number
ACU Department
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Faculty of Education and Arts
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Notes
This work was supported in part by Queensland University of Technology under the Edge Grant scheme.
© 2022 Published by Elsevier Ltd. All rights reserved.
© 2022 Published by Elsevier Ltd. All rights reserved.