Energy-efficient virtual machine consolidation algorithm in cloud data centers

Abstract

Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine (VM) consolidation algorithm named PVDE (prediction-based VM deployment algorithm for energy efficiency) is presented. The proposed algorithm uses linear weighted method to predict the load of a host and classifies the hosts in the data center, based on the predicted host load, into four classes for the purpose of VMs migration. We also propose four types of VM selection algorithms for the purpose of determining potential VMs to be migrated. We performed extensive performance analysis of the proposed algorithms. Experimental results show that, in contrast to other energy-saving algorithms, the algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement (SLA) violations.

Keywords

cloud computing / energy consumption / linear weighted method / virtual machine consolidation / virtual machine selection algorithm

References

[1] PuthalD, SahooB P S, MishraS, SwainS. Cloud computing features, issues, and challenges: A big picture [C]//. 2015 International Conference on Computational Intelligence and Networks (CINE), 2015116123

[2] MagalhãesD, CalheirosR N, BuyyaR, DanieloG G. Workload modeling for resource usage analysis and simulation in cloud computing [J]. Computers & Electrical Engineering, 2015, 47(1): 69-81

[3] RicciardiS, CareglioD, Santos-BoadaG, Solé-ParetaJ, FioreU, PalmieriF. Saving energy in data center infrastructures [C]//. 2011 First International Conference on Data Compression, Communications and Processing (CCP), 2011265270

[4] BarrosoL A, HolzleU. The case for energy-proportional computing [J]. Computer, 2007, 40(12): 33-37

[5] BohrerP, ElnozahyE N, KellerT, KistlerM, LefurgyC, McdowellC, RajamonyRThe case for power management in web servers [M], 2002261289

[6] ClarkC, FraserK, HandS, HansenJ G, JulE, LimpachC, PrattI, WarfieldA. Live migration of virtual machines [C]//. Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation, 2005273286

[7] HermenierF, LorcaX, MenaudJ M, MullerG, LawallJ. Entropy: A consolidation manager for clusters [C]//. Proceedings of the 2009 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, 20094150

[8] BeloglazovA, BuyyaR. Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers [C]//. Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science, 201016

[9] HansonH, KecklerS W, GhiasiS, RajamaniK, RawsonF, RubioJ. Thermal response to DVFS: Analysis with an Intel Pentium M [C]//. Proceedings of the 2007 International Symposium on Low Power Electronics and Design, 2007219224

[10] BeloglazovA, BuyyaR. Energy efficient resource management in virtualized cloud data centers [C]//. Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, 2010826831

[11] CalheirosR N, RanjanR, BeloglazovA, DeroseC A, BuyyaR. CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms [J]. Software: Practice and Experience, 2011, 41(1): 23-50

[12] BeloglazovA, AbawajyJ, BuyyaR. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing [J]. Future Generation Computer Systems, 2012, 28(5): 755-768

[13] VanH N, TranF D, MenaudJ M. Performance and power management for cloud infrastructures [C]//. 2010 IEEE 3rd International Conference on Cloud Computing, 2010329336

[14] KangJ, RankaS. Dynamic slack allocation algorithms for energy minimization on parallel machines [J]. Journal of Parallel and Distributed Computing, 2010, 70(5): 417-430

[15] BuyyaR, RanjanR, CalheirosR N. Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit: Challenges and opportunities [C]//. International Conference on High Performance Computing & Simulation, 2009111

[16] ZhouZ, HuZ, SongT, YuJ. A novel virtual machine deployment algorithm with energy efficiency in cloud computing [J]. Journal of Central South University, 2015, 22(5): 974-983

[17] KusicD, KephartJ O, HansonJ E, KandasamyN, JiangG. Power and performance management of virtualized computing environments via look ahead control [J]. Cluster Computing, 2009, 12(1): 1-15

[18] VoorsluysW, BrobergJ, VenugopalS, BuyyaRCost of virtual machine live migration in clouds: A performance evaluation [M], 2009254265