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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Output statusPublished
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Print28 Apr 2022
Publication process dates
Accepted29 Mar 2022
Deposited17 Jan 2023
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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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Exact algorithms for energy-efficient virtual machine placement in data centers
Wei, Chen, Hu, Zhi-Hua and Wang, You-Gan. (2020). Exact algorithms for energy-efficient virtual machine placement in data centers. Future Generation Computer Systems. 106, pp. 77-91. https://doi.org/10.1016/j.future.2019.12.043
A working likelihood approach for robust regression
Fu, Liya, Wang, You-Gan and Cai, Fengjing. (2020). A working likelihood approach for robust regression. Statistical Methods in Medical Research. 29(12), pp. 3641-3652. https://doi.org/10.1177/0962280220936310
Maritime convection and fluctuation between Vietnam and China : A data-driven study
Hu, Zhi-Hua, Liu, Chan-Juan, Chen, Wanting, Wang, You-Gan and Wei, Chen. (2020). Maritime convection and fluctuation between Vietnam and China : A data-driven study. Research in Transportation Business and Management. 34, pp. 1-15. https://doi.org/10.1016/j.rtbm.2019.100414
Identifying barley pan-genome sequence anchors using genetic mapping and machine learning
Gao, Shang, Wu, Ryan, Stiller, Jiri, Zheng, Zhi, Zhou, Meixue, Wang, You-Gan and Liu, Chunji. (2020). Identifying barley pan-genome sequence anchors using genetic mapping and machine learning. Theoretical and Applied Genetics. 133(9), pp. 2535-2544. https://doi.org/10.1007/s00122-020-03615-y
Natural mortality estimation using tree-based ensemble learning models
Liu, Chanjuan, Zhou, Shijie, Wang, You-Gan and Hu, Zhi-Hua. (2020). Natural mortality estimation using tree-based ensemble learning models. ICES Journal of Marine Science. 77(4), pp. 1414-1426. https://doi.org/10.1093/icesjms/fsaa058
Profile-guided three-phase virtual resource management for energy efficiency of data centers
Ding, Zhe, Tian, Yu-Chu, Tang, Maolin, Li, Yuefeng, Wang, You-Gan and Zhou, Chunjie. (2020). Profile-guided three-phase virtual resource management for energy efficiency of data centers. IEEE Transactions on Industrial Electronics. 67(3), pp. 2460-2468. https://doi.org/10.1109/TIE.2019.2902786
Response of water quality to land use and sewage outfalls in different seasons
Xu, Jing, Jin, Guangqiu, Tang, Hongwu, Mo, Yuming, Wang, You-Gan and Li, Ling. (2019). Response of water quality to land use and sewage outfalls in different seasons. Science of the Total Environment. 696, p. Article 134014. https://doi.org/10.1016/j.scitotenv.2019.134014
A new hybrid model to predict the electrical load in five states of Australia
Wu, Jinran, Cui, Zhesen, Chen, Yanyan, Kong, Demeng and Wang, You-Gan. (2019). A new hybrid model to predict the electrical load in five states of Australia. Energy. 166, pp. 598-609. https://doi.org/10.1016/j.energy.2018.10.076
Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere
Jones, Andy, Lavery, Shane D., Le Port, Agnès, Wang, You-Gan, Blower, Dean and Ovenden, Jennifer. (2019). Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere. Molecular Ecology. 28(12), pp. 2986-2995. https://doi.org/10.1111/mec.15130
A review of the Behrens–Fisher problem and some of its analogs : Does the same size fit all?
Paul, Sudhir, Wang, You-Gan and Ullah, Insha. (2019). A review of the Behrens–Fisher problem and some of its analogs : Does the same size fit all? Revstat Statistical Journal. 17(4), p. 563–597.
Incorporating social objectives in evaluating sustainable fisheries harvest strategy
Wu, Jiafeng, Wang, Na, Hu, Zhi-Hua, Hong, Zhenjie and Wang, You-Gan. (2019). Incorporating social objectives in evaluating sustainable fisheries harvest strategy. Environmental Modeling and Assessment. 24(4), pp. 381-386. https://doi.org/10.1007/s10666-019-9651-9
Significance tests for analyzing gene expression data with small sample sizes
Ullah, Insha, Paul, Sudhir, Hong, Zhenjie and Wang, You-Gan. (2019). Significance tests for analyzing gene expression data with small sample sizes. Bioinformatics. 35(20), pp. 3996-4003. https://doi.org/10.1093/bioinformatics/btz189
Robust Estimation Using Modified Huber’s Functions With New Tails
Jiang, Yunlu, Wang, You-Gan, Fu, Liya and Wang, Xueqin. (2019). Robust Estimation Using Modified Huber’s Functions With New Tails. Technometrics. 61(1), pp. 111-122. https://doi.org/10.1080/00401706.2018.1470037
Variable selection in rank regression for analyzing longitudinal data
Fu, Liya and Wang, You-Gan. (2018). Variable selection in rank regression for analyzing longitudinal data. Statistical Methods in Medical Research. 27(8), pp. 2447-2458. https://doi.org/10.1177/0962280216681347
Assessing temporal variations of Ammonia Nitrogen concentrations and loads in the Huaihe River Basin in relation to policies on pollution source control
Xu, Jing, Jin, Guangqiu, Tang, Hongwu, Zhang, Pei, Wang, Shen, Wang, You-Gan and Li, Ling. (2018). Assessing temporal variations of Ammonia Nitrogen concentrations and loads in the Huaihe River Basin in relation to policies on pollution source control. Science of the Total Environment. 642, pp. 1386-1395. https://doi.org/10.1016/j.scitotenv.2018.05.395
Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods
Li, Bo, Zhang, Nanxi, Wang, You-Gan, George, Andrew W., Reverter, Antonio and Li, Yutao. (2018). Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Frontiers in Genetics. 9, p. Article 237. https://doi.org/10.3389/fgene.2018.00237
Dividend growth and equity premium predictability
Zhu, Min, Chen, Rui, Du, Ke and Wang, You-Gan. (2018). Dividend growth and equity premium predictability. International Review of Economics and Finance. 56, pp. 125-137. https://doi.org/10.1016/j.iref.2017.10.020
Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations
Wang, Na, Wang, You-Gan, Hu, Shuwen, Hu, Zhi-Hua, Xu, Jing, Tang, Hongwu and Jin, Guangqiu. (2018). Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations. Environmental Modeling and Assessment. 23(6), pp. 779-786. https://doi.org/10.1007/s10666-018-9605-7
Analysis of spatial data with a nested correlation structure
Adegboye, Oyelola, Leung, Denis and Wang, You-Gan. (2018). Analysis of spatial data with a nested correlation structure. Journal of the Royal Statistical Society Series C: Applied Statistics. 67(2), pp. 329-354. https://doi.org/10.1111/rssc.12230
Working correlation structure selection in generalized estimating equations
Fu, Liya, Hao, Yangyang and Wang, You-Gan. (2018). Working correlation structure selection in generalized estimating equations. Computational Statistics. 33(2), pp. 983-996. https://doi.org/10.1007/s00180-018-0800-4
Selection of working correlation structure in generalized estimating equations
Wang, You-Gan and Fu, Liya. (2017). Selection of working correlation structure in generalized estimating equations. Statistics in Medicine. 36(14), pp. 2206-2219. https://doi.org/10.1002/sim.7262
Blockwise AICc for model selection in generalized linear models
Song, Guofeng, Dong, Xiaogang, Wu, Jiafeng and Wang, You-Gan. (2017). Blockwise AICc for model selection in generalized linear models. Environmental Modeling and Assessment. 22(6), pp. 523-533. https://doi.org/10.1007/s10666-017-9552-8
A comment on Koh’s “The optimal design of fallible organizations : Invariance of optimal decision threshold and uniqueness of hierarchy and polyarchy structures”
Zhu, Min, Liu, Chang and Wang, You-Gan. (2017). A comment on Koh’s “The optimal design of fallible organizations : Invariance of optimal decision threshold and uniqueness of hierarchy and polyarchy structures”. Social Choice and Welfare. 48(2), pp. 385-392. https://doi.org/10.1007/s00355-016-1009-5
The Buckley–James estimator and induced smoothing
Wang, You-Gan, Zhao, Yudong and Fu, Liya. (2016). The Buckley–James estimator and induced smoothing. Australian and New Zealand Journal of Statistics. 58(2), pp. 211-225. https://doi.org/10.1111/anzs.12155
Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data
Kienzle, Marco, Sterling, David, Zhou, Shijie and Wang, You-Gan. (2016). Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data. Ecological Modelling. 322, pp. 1-9. https://doi.org/10.1016/j.ecolmodel.2015.11.008
Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment
Zischke, Mitchell T., Litherland, Lenore, Tilyard, Benjamin R., Stratford, Nicholas J., Jones, Ebony L. and Wang, You-Gan. (2016). Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment. Fisheries Research. 176, pp. 39-47. https://doi.org/10.1016/j.fishres.2015.12.003
Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data
Fu, Liya and Wang, You-Gan. (2016). Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data. Journal of Multivariate Analysis. 143, pp. 492-502. https://doi.org/10.1016/j.jmva.2015.07.004
Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data
Lloyd-Jones, Luke R., Nguyen, Hien D., McLachlan, Geoffrey J., Sumpton, Wayne and Wang, You-Gan. (2016). Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics. 72(4), pp. 1255-1265. https://doi.org/10.1111/biom.12531
Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas
Purcell, Steven W., Piddocke, Toby P., Dalton, Steven J. and Wang, You-Gan. (2016). Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas. Marine Ecology Progress Series. 551, pp. 201-214. https://doi.org/10.3354/meps11720
Improved confidence intervals for the linkage disequilibrium method for estimating effective population size
Jones, A. T., Ovenden, J. R. and Wang, Y.-G.. (2016). Improved confidence intervals for the linkage disequilibrium method for estimating effective population size. Heredity. 117(4), pp. 217-223. https://doi.org/10.1038/hdy.2016.19