Progressive-fidelity computation of the genetic algorithm for energy-efficient virtual machine placement in cloud data centers
Journal article
Ding, Zhe, Tian, Yu-Chu, Wang, You-Gan, Zhang, Weizhe and Yu, Zu-Guo. (2023). Progressive-fidelity computation of the genetic algorithm for energy-efficient virtual machine placement in cloud data centers. Applied Soft Computing. 146, pp. 1-13. https://doi.org/10.1016/j.asoc.2023.110681
Authors | Ding, Zhe, Tian, Yu-Chu, Wang, You-Gan, Zhang, Weizhe and Yu, Zu-Guo |
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Abstract | Energy efficiency is a critical issue in the management and operation of data centers, which form the backbone of cloud computing. Virtual machine placement has a significant impact on the energy efficiency of virtualized data centers. Among various methods to solve the virtual-machine placement problem, the genetic algorithm has been well accepted for its quality of solution. However, it is computationally demanding largely due to the complex form of fitness function, limiting its applications in data centers. To enhance the computational efficiency of the genetic algorithm while maintaining its quality of solution, a progressive-fidelity approach is developed in this paper for genetic-algorithm computation. It starts with a low-fidelity genetic algorithm with a simple fitness function. Then, for solution refinement, it switches to a medium-fidelity genetic algorithm with a more complicated fitness function. Finally, it progresses to the fine tuning of solution through a high-fidelity genetic algorithm with the energy consumption of data centers as fitness function. Heuristics are presented for the adaptive switching of genetic-algorithm computation from low fidelity to medium fidelity and finally to high fidelity. Experiments show that compared with the standard genetic algorithm of high fidelity, our progressive-fidelity approach of genetic algorithm computation is 50% faster for large-scale data centers while maintaining similar quality of solution in terms of the energy consumption of data centers. |
Keywords | Virtual machine placement ; Genetic algorithm ; Fitness function ; Progressive fidelity ; Data center |
Year | 01 Jan 2023 |
Journal | Applied Soft Computing |
Journal citation | 146, pp. 1-13 |
Publisher | Elsevier B.V. (Netherlands) |
ISSN | 1568-4946 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.asoc.2023.110681 |
Web address (URL) | https://www.sciencedirect.com/science/article/pii/S1568494623006993?via%3Dihub |
Open access | Open access |
Research or scholarly | Research |
Page range | 1-13 |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
25 Jul 2023 | |
Publication process dates | |
Accepted | 21 Jul 2021 |
Deposited | 13 Aug 2024 |
Additional information | © 2023 The Authors. Published by Elsevier B.V. T |
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). | |
Place of publication | Netherlands |
https://acuresearchbank.acu.edu.au/item/90w89/progressive-fidelity-computation-of-the-genetic-algorithm-for-energy-efficient-virtual-machine-placement-in-cloud-data-centers
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Publisher's version
OA_Wang_2023_Progressive_fidelity_computation_of_the_genetic.pdf | |
License: CC BY-NC-ND 4.0 | |
File access level: Open |
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