Abstract
Optimizing energy consumption in cloud computing is yet a challenge despite the diversity of the proposed energy management strategies. Indeed, and during our related work study we have observed that the different elements or components which should be considered in order to be able to properly manage energy consumption in a cloud computing context are not well defined and/or discussed in terms of importance. This makes the proper classification and/or comparison of the different proposed strategies or techniques very difficult. Consequently, this paper aims, on the one hand, at defining and discussing properly such components in order to create a guideline and, on the other hand, to ease both the classification and the comparison of these proposed strategies and techniques. Second and after discussing some common weaknesses related to the current energy consumption optimization techniques and methods, this paper proposes energy-saving technique which uses a novel load detecting policy. This policy is based on the median absolute deviation method which uses the median and the standard deviation to calculate upper and lower thresholds which aim to classify hosts into either overloaded or under-loaded state. Simulation results have shown better results of the proposed technique compared to the existing ones especially in reducing energy consumption and the number of virtual machine migrations in addition to better active host time. Indeed, we found that the average of saved energy is around 40% compared to the built in techniques in cloudSim.
Similar content being viewed by others
Notes
The Open Compute project. http://opencompute.org.
References
Zhu X, Young D, Watson BJ et al (2009) 1000 islands: an integrated approach to resource management for virtualized data centers. Clust Comput 12(1):45–57
Greenberg A, Hamilton J, Maltz DA, Patel P (2009) The cost of a cloud: research problems in data center networks. ACM SIGCOMM Comput Commun Rev 39(1):68–73
Beloglazov A, Buyya R (2012) Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr Comput Pract Exp 24(13):1397–1420
Barroso LA, Hlzle U (2007) The case for energy-proportional computing. Computer 40(12):33–37. doi:10.1109/MC.2007.443
Fan X, Weber WD, Barroso LA (2007) Power provisioning for a warehouse-sized computer. ACM SIGARCH Comput Archit News 35(12):13–23
Lee YC, Zomaya AY (2012) Energy efficient utilization of resources in cloud computing systems. J Supercomput 60(2):268–280
Buyya R, Beloglazov A, Abawajy J (2010) Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv:1006.0308
Buyya R, Garg SK, Calheiros RN (2011) SLA-oriented resource provisioning for cloud computing: challenges, architecture, and solutions. In: Cloud and Service Computing (CSC) IEEE International Conference, pp 1–10. doi:10.1109/CSC.2011.6138522
Beloglazov A, Buyya R (2010) Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. In: Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science, p 4
Lim HC, Babu S, Chase JS, Parekh SS (2009) Automated control in cloud computing: challenges and opportunities. In: Proceedings of the 1st Workshop on Automated Control for Datacenters and Clouds, pp 13–18
Verma A, Ahuja P, Neogi A (2008) pMapper: power and migration cost aware application placement in virtualized systems. In: ACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing, pp 243–264
Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28(5):755–768
Chowdhury MR, Mahmud MR, Rahman RM (2015) Implementation and performance analysis of various VM placement strategies in CloudSim. J Cloud Comput 4(1):1
Graubner P, Schmidt M, Freisleben B (2011) Energy-efficient management of virtual machines in eucalyptus. In: Cloud Computing (CLOUD) IEEE International Conference, pp 243–250
Lin C, Liu P, Wu J (2011) Energy-aware virtual machine dynamic provision and scheduling for cloud computing. In: Cloud Computing (CLOUD) IEEE International Conference, pp 736–737
Nurmi D, Wolski R, Grzegorczyk C et al (2009) The eucalyptus open-source cloud-computing system. In: Cluster Computing and the Grid (CCGRID’09) 9th IEEE/ACM International Symposium on IEEE, pp 124–131
Zhao W, Peng Y, Xie F, Dai Z (2012) Modeling and simulation of cloud computing: a review. In: IEEE Asia Pacific Cloud Computing Congress (APCloudCC), pp 20–24
Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50
Tian W, Xu M, Chen A, Lia G, Wanga X, Chena Y (2015) Open-source simulators for cloud computing: comparative study and challenging issues. Simul Model Pract Theory 58(2):239–254
Shojafar M, Cordeschi N, Baccarelli E (2016) Energy-efficient resource management for real-time vehicular cloud services. IEEE Trans Cloud Comput. doi:10.1109/TCC.2016.2551747
Beloglazov A, Buyya R (2010) Energy efficient allocation of virtual machines in cloud data centers. In: Cluster, Cloud and Grid Computing (CCGrid) 10th IEEE/ACM International Conference, pp 577–578
Zhu X, Young D, Watson, BJ et al (2008) 1000 islands: integrated capacity and workload management for the next generation data center. In: International Conference on Autonomic Computing (ICAC’08), pp 172–181
Gmach D, Rolia J, Cherkasova L, Belrose G, Turicchi T, Kemper A (2008) An integrated approach to resource pool management: policies, efficiency and quality metrics. In: IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN), pp 326–335
Al-haidari F, Sqalli M, Salah K (2013) Impact of CPU utilization thresholds and scaling size on autoscaling cloud resources. In: Cloud Computing Technology and Science (CloudCom 2013) IEEE, pp 256–261
Xiao Z, Song W, Chen Q (2013) Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans Parallel Distrib Syst 24(6):1107–1117
Alboaneen DA, Pranggono B, Tianfield H (2014) Energy-aware virtual machine consolidation for cloud data centers. In: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing. IEEE Computer Society, pp 1010–1015
Yang Q, Peng C, Zhao H et al (2014) A new method based on PSR and EA-GMDH for host load prediction in cloud computing system. J Supercomput 68(3):1402–1417
Di S, Kondo D, Cirne W (2012) Host load prediction in a Google compute cloud with a Bayesian model. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis IEEE Computer Society Press, p 21
Wang L, Lu Y (2008) Efficient power management of heterogeneous soft real-time clusters. In: Real-Time Systems Symposium IEEE, pp 323–332
Cao Z, Dong S (2012) Dynamic VM consolidation for energy-aware and SLA violation reduction in cloud Computing. In: Parallel and Distributed Computing, Applications and Technologies (PDCAT) 13th IEEE International Conference, pp 363–369
Fu X, Zhou C (2015) Virtual machine selection and placement for dynamic consolidation in Cloud computing environment. Front Comput Sci 9(2):322–330
Goudarzi H, Pedram M (2012) Energy-efficient virtual machine replication and placement in a cloud computing system. In: Cloud Computing (CLOUD) 5th IEEE International Conference, pp 750–757
Singh NA, Hemalatha M (2013) Reduce energy consumption through virtual machine placement in cloud data centre. Min Intell Knowl Explor 8284:466–474
Huang J, Wu K, Moh M (2014) Dynamic virtual machine migration algorithms using enhanced energy consumption model for green cloud data centers. In: High Performance Computing & Simulation (HPCS) IEEE International Conference, pp 902–910
Huang Q, Gao F, Wang R, Qi Z (2011) Power consumption of virtual machine live migration in clouds. In: IEEE Third International Conference on Communications and Mobile Computing, pp 122–125
Kapil D, Pilli ES, Joshi RC (2013) Live virtual machine migration techniques: Survey and research challenges. In: 3rd IEEE International Advance Computing Conference (IACC), pp 963–969
Strunk A (2012) Costs of virtual machine live migration: a survey. In: IEEE Eighth World Congress on Services, pp 323–329
Orgerie AC, Lefevre L, Gelas JP (2010) Demystifying energy consumption in grids and clouds. In: Green Computing Conference, pp 335–342
Baccarelli E, Amendola D, Cordeschi N (2015) Minimum-energy bandwidth management for QoS live migration of virtual machines. Comput Netw 93(1):1–22
Liu H, Xu CZ, Jin H, Gong J, Liao X (2011) Performance and energy modeling for live migration of virtual machines. In: Proceedings of the 20th International Symposium on High Performance Distributed Computing, pp 171–182
Carpen-Amarie A, Orgerie AC, Morin C (2013) Experimental study on the energy consumption in IaaS cloud environments. In: Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, pp 42–49
Strunk A (2013) A lightweight model for estimating energy cost of live migration of virtual machines. In: IEEE Sixth International Conference on Cloud Computing, pp 510–517
Chase JS, Anderson DC, Thakar PN, Vahdat AM, Doyle RP (2001) Managing energy and server resources in hosting centers. ACM SIGOPS Oper Syst Rev 35(5):103–116
Cordeschi N, Shojafar M, Amendola D, Baccarelli E (2015) Energy-efficient adaptive networked datacenters for the QoS support of real-time applications. J Supercomput 71(2):448–478
Rajamani K, Lefurgy C (2003) On evaluating request-distribution schemes for saving energy in server clusters. In: Performance Analysis of Systems and Software IEEE International Symposium, pp 111–122
Orgerie A C, Lefvre L, Gelas J P (2008) Chasing gaps between bursts: towards energy efficient large scale experimental grids. In: Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies, pp 381–389
Bobroff N, Kochut A, Beaty K (2007) Dynamic placement of virtual machines for managing SLA violations. In: Integrated Network Management (IM’07) 10th IFIP/IEEE International Symposium, pp 119–128
Zheng Q, Veeravalli B (2012) Utilization-based pricing for power management and profit optimization in data centers. J Parallel Distrib Comput 72(1):27–34
Srikantaiah S, Kansal A, Zhao F (2008) Energy aware consolidation for cloud computing. In: Proceedings of the 2008 Conference on Power Aware Computing and Systems, pp 1–5
Park K, Pai VS (2006) CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Oper Syst Rev 40(1):65–74
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chaabouni, T., Khemakhem, M. Energy management strategy in cloud computing: a perspective study. J Supercomput 74, 6569–6597 (2018). https://doi.org/10.1007/s11227-017-2154-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-017-2154-z