Abstract
Virtual machine (VM) consolidation is one of the key mechanisms of designing an energy-efficient dynamic Cloud resource management system. It is based on the premise that migrating VMs into fewer number of Physical Machines (PMs) can achieve both optimization objectives, increasing the utilization of Cloud servers while concomitantly reducing the energy consumption of the Cloud data center. However, packing more VMs into a single server may lead to poor Quality of Service (QoS), since VMs share the underlying physical resources of the PM. To address this, VM Consolidation (VMC) algorithms are designed to dynamically select VMs for migration by considering the impact on QoS in addition to the above-mentioned optimization objectives. VMC is a NP-hard problem and hence, a wide range of heuristic and meta-heuristic VMC algorithms have been proposed that aim to achieve near-optimality. Since, VMC is highly popular research topic and plethora of researchers are presently working in this area, the related literature is extremely broad. Hence, it is a non-trivial research work to cover such extensive literature and find strong distinguishing aspects based on which VMC algorithms can be classified and critically compared, as it is missing in existing surveys. In this chapter, we have classified and critically reviewed VMC algorithms from multitude of viewpoints so that the readers can be truly benefitted. Finally, we have concluded with valuable future directions so that it would pave the way of fellow researchers to further contribute in this area.
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References
Kleinrock L. A vision for the internet. ST J Res. 2005;2(1):4–5.
Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I. Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst. 2009;25(6):599–616.
Kaplan JM, Forrest W, Kindler N. Revolutionizing data center energy efficiency. Technical report, McKinsey & Company; 2008.
Beloglazov A. Energy-efficient management of virtual machines in data centers for cloud computing [dissertation]. Melbourne, AU: The University of Melbourne; 2013.
Koomey J. Growth in data center electricity use 2005 to 2010. A report by Analytical Press, completed at the request of The New York Times. 2011;9.
Gartner Estimates I. Industry accounts for 2 percent of global CO2 emissions. Press release; 2007.
Buyya R, Beloglazov A, Abawajy J. Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges; 2010. arXiv preprint arXiv:10060308.
Koomey JG. Estimating total power consumption by servers in the US and the world. Feb 2007.
Hopkin J. VMware ESX Server [Image on internet]. OStatic; © 2015. Available from: http://ostatic.com/vmware-esx-server/screenshot/1.
Mann ZÁ. Allocation of virtual machines in cloud data centers—a survey of problem models and optimization algorithms. ACM Comput Surv (CSUR). 2015;48(1):11.
Pietri I, Sakellariou R. Mapping virtual machines onto physical machines in cloud computing: a survey. ACM Comput Surv (CSUR). 2016;49(3):49.
Kaur S, Bawa S, editors. A review on energy aware VM placement and consolidation techniques. In: International conference on inventive computation technologies (ICICT). IEEE; 2016.
Madhan ES, Srinivasan S, editors. Energy aware data center using dynamic consolidation techniques: a survey. In: Proceedings of IEEE international conference on computer communication and systems ICCCS14. 20–21 Feb 2014.
Pires FL, Barán B, editors. A Virtual machine placement taxonomy. In: 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and grid computing. 4–7 May 2015.
Ranjana R, Raja J, editors. A survey on power aware virtual machine placement strategies in a cloud data center. In: 2013 international conference on green computing, communication and conservation of energy (ICGCE). IEEE; 2013.
Varasteh A, Goudarzi M. Server consolidation techniques in virtualized data centers: a survey. IEEE Syst J. 2015;11(2):772–83.
Ahmad RW, Gani A, Hamid SHA, Shiraz M, Yousafzai A, Xia F. A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J Netw Comput Appl. 2015;52:11–25.
Choudhary A, Rana S, Matahai KJ. A critical analysis of energy efficient virtual machine placement techniques and its optimization in a cloud computing environment. Procedia Comput Sci. 2016;78:132–8.
Masdari M, Nabavi SS, Ahmadi V. An overview of virtual machine placement schemes in cloud computing. J Netw Comput Appl. 2016;66:106–27.
Usmani Z, Singh S. A survey of virtual machine placement techniques in a cloud data center. Procedia Comput Sci. 2016;78:491–8.
Beloglazov A, Buyya R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr Comput. 2012;24(13):1397–420.
Ferdaus MH, Murshed M. Energy-aware virtual machine consolidation in IaaS cloud computing. In: Cloud computing. Berlin: Springer; 2014. p. 179–208.
Jersak LC, Ferreto T. Performance-aware server consolidation with adjustable interference levels. In: Proceedings of the 31st annual ACM symposium on applied computing. Pisa, Italy. 2851625: ACM; 2016. p. 420–5.
Hwang I, Pedram M. Hierarchical, portfolio theory-based virtual machine consolidation in a compute cloud. IEEE Trans Serv Comput. 2016;PP(99):1.
Nasim R, Taheri J, Kassler AJ, editors. Optimizing virtual machine consolidation in virtualized datacenters using resource sensitivity. In: 2016 IEEE international conference on cloud computing technology and science (CloudCom). 12–15 Dec 2016.
Chen L, Shen H, Platt S, editors. Cache contention aware virtual machine placement and migration in cloud datacenters. In: 2016 IEEE 24th international conference on network protocols (ICNP). IEEE; 2016.
Ferdaus MH. Multi-objective virtual machine management in cloud data centers. Melbourne: Monash University; 2016.
Ahamed F, Shahrestani S, Javadi B, editors. Security aware and energy-efficient virtual machine consolidation in cloud computing systems. In: 2016 IEEE Trustcom/BigDataSE/ISPA. 23–26 Aug 2016.
Deng D, He K, Chen Y, editors. Dynamic virtual machine consolidation for improving energy efficiency in cloud data centers. In: 2016 4th international conference on cloud computing and intelligence systems (CCIS). 17–19 Aug 2016.
Fioccola GB, Donadio P, Canonico R, Ventre G, editors. Dynamic routing and virtual machine consolidation in green clouds. In: 2016 IEEE international conference on cloud computing technology and science (CloudCom). 12–15 Dec 2016.
Masoumzadeh SS, Hlavacs H. A gossip-based dynamic virtual machine consolidation strategy for large-scale cloud data centers. In: Proceedings of the third international workshop on adaptive resource management and scheduling for cloud computing, Chicago, IL, USA. 2962565: ACM; 2016. p. 28–34.
Khelghatdoust M, Gramoli V, Sun D, editors. GLAP: distributed dynamic workload consolidation through gossip-based learning. In: 2016 IEEE international conference on cluster computing (CLUSTER). IEEE; 2016.
Beloglazov A, Abawajy J, Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst. 2012;28(5):755–68.
Marcel A, Cristian P, Eugen P, Claudia P, Cioara T, Anghel I, et al., editors. Thermal aware workload consolidation in cloud data centers. In: 2016 IEEE 12th international conference on intelligent computer communication and processing (ICCP). 8–10 Sept 2016.
Marotta A, Avallone S, editors. A Simulated annealing based approach for power efficient virtual machines consolidation. In: 2015 IEEE 8th international conference on cloud computing. June 27–July 2 2015.
Farahnakian F, Ashraf A, Pahikkala T, Liljeberg P, Plosila J, Porres I, et al. Using ant colony system to consolidate vms for green cloud computing. IEEE Trans Serv Comput. 2015;8(2):187–98.
Farahnakian F, Bahsoon R, Liljeberg P, Pahikkala T, editors. Self-adaptive resource management system in IaaS clouds. In: 2016 IEEE 9th international conference on cloud computing (CLOUD). June 27–July 2 2016.
Farahnakian F, Liljeberg P, Plosila J, editors. LiRCUP: Linear regression based CPU usage prediction algorithm for live migration of virtual machines in data centers. In: 2013 39th euromicro conference on software engineering and advanced applications. IEEE; 2013.
Pascual JA, Lorido-Botrán T, Miguel-Alonso J, Lozano JA. Towards a greener cloud infrastructure management using optimized placement policies. J Grid Comput. 2015;13(3):375–89.
Selim GEI, El-Rashidy MA, El-Fishawy NA, editors. An efficient resource utilization technique for consolidation of virtual machines in cloud computing environments. In: 2016 33rd national radio science conference (NRSC). 22–25 Feb 2016.
Abdi H. Multiple correlation coefficient. Richardson: The University of Texas at Dallas; 2007.
Yan C, Li Z, Yu X, Yu N, editors. Bayesian networks-based selection algorithm for virtual machine to be migrated. In: 2016 IEEE international conferences on big data and cloud computing (BDCloud), Social computing and networking (SocialCom), Sustainable computing and communications (SustainCom)(BDCloud-SocialCom-SustainCom). IEEE; 2016.
Tanenbaum AS. Distributed operating systems. New Delhi: Pearson Education India; 1995.
Ferdaus MH, Murshed M, Calheiros RN, Buyya R, editors. Virtual machine consolidation in cloud data centers using ACO metaheuristic. In: European conference on parallel processing. Springer; 2014.
Ma L, Liu H, Leung YW, Chu X, editors. Joint VM-switch consolidation for energy efficiency in data centers. In: 2016 IEEE global communications conference (GLOBECOM). 4–8 Dec 2016.
Varasteh A, Goudarzi M. Server consolidation techniques in virtualized data centers: a survey. IEEE Syst J. 2015;11(2):772–83.
Dabbagh M, Hamdaoui B, Guizani M, Rayes A. Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment. IEEE Netw. 2015;29(2):56–61.
Jobava A, Yazidi A, Oommen BJ, Begnum K, editors. Achieving intelligent traffic-aware consolidation of virtual machines in a data center using learning automata. In: 2016 8th IFIP international conference on new technologies, mobility and security (NTMS). IEEE; 2016.
Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp. 2011;41(1):23–50.
Nguyen TH, Francesco MD, Yla-Jaaski A. Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans Serv Comput. 2017;PP(99):1.
Wu Q, Ishikawa F, Zhu Q, Xia Y. Energy and migration cost-aware dynamic virtual machine consolidation in heterogeneous cloud datacenters. IEEE Trans Serv Comput. 2016;PP(99):1.
Choudhary A, Govil MC, Singh G, Awasthi LK, Pilli ES, Kumar N, editors. Improved virtual machine migration approaches in cloud environment. In: 2016 IEEE international conference on cloud computing in emerging markets (CCEM). 19–21 Oct 2016.
Grimes D, Mehta D, O’Sullivan B, Birke R, Chen L, Scherer T, et al., editors. Robust server consolidation: coping with peak demand underestimation. In: 2016 IEEE 24th international Symposium on modeling, analysis and simulation of computer and telecommunication systems (MASCOTS). IEEE; 2016.
Montresor A, Jelasity M, editors. PeerSim: a scalable P2P simulator. In: IEEE ninth international conference on Peer-to-Peer computing, 2009 P2P'09. IEEE; 2009.
Kaur A, Kalra M, editors. Energy optimized VM placement in cloud environment. In: 2016 6th international conference cloud system and big data engineering (confluence). IEEE; 2016.
Liu Y, editor A consolidation strategy supporting resources oversubscription in cloud computing. In: 2016 IEEE 3rd international conference on cyber security and cloud computing (CSCloud). IEEE; 2016.
Li H, Zhu G, Cui C, Tang H, Dou Y, He C. Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing. Computing. 2016;98(3):303–17.
Li X, Ventresque A, Murphy J, Thorburn J. SOC: Satisfaction-Oriented Virtual Machine Consolidation in Enterprise Data Centers. Int J Parallel Program. 2016;44(1):130–50.
Dong J-k, Wang H-b, Li Y-Y, Cheng S-d. Virtual machine placement optimizing to improve network performance in cloud data centers. J China Univ Posts Telecommun. 2014;21(3): 62–70.
Zheng Q, Li R, Li X, Shah N, Zhang J, Tian F, et al. Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Future Gener Comput Syst. 2016;54:95–122.
Cao Z, Dong S. An energy-aware heuristic framework for virtual machine consolidation in Cloud computing. J Supercomput. 2014;69(1):429–51.
Kang D-K, Alhazemi F, Kim S-H, Youn C-H. Dynamic virtual machine consolidation for energy efficient cloud data centers. In: Zhang Y, Peng L, Youn C-H, editors. Cloud computing: 6th international conference, CloudComp 2015, Daejeon, South Korea, October 28–29, 2015, Revised selected papers. Cham: Springer International Publishing; 2016. p. 70–80.
Liu L, Zheng S, Yu H, Anand V, Xu D. Correlation-based virtual machine migration in dynamic cloud environments. Photonic Netw Commun. 2016;31(2):206–16.
Monil MAH, Rahman RM. VM consolidation approach based on heuristics, fuzzy logic, and migration control. J Cloud Computing. 2016;5(1):8.
Patel CA, Shah JS. Server consolidation with minimal SLA violations. In: Behera HS, Mohapatra DP, editors. Computational intelligence in data mining—volume 2. Proceedings of the international conference on CIDM, 5–6 Dec 2015. New Delhi: Springer India; 2016. p. 455–62.
Shackleford D. Virtualization and cloud: Orchestration, automation and security gaps [video on the Internet]. 2DeCipher; 2014. [Available from: https://www.youtube.com/watch?v=mjOwQlr1LIk].
Pinheiro E, Bianchini R, Carrera EV, Heath T, editors. Load balancing and unbalancing for power and performance in cluster-based systems. In: Workshop on compilers and operating systems for low power. Barcelona, Spain; 2001.
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Khan, M.A., Paplinski, A., Khan, A.M., Murshed, M., Buyya, R. (2018). Dynamic Virtual Machine Consolidation Algorithms for Energy-Efficient Cloud Resource Management: A Review. In: Rivera, W. (eds) Sustainable Cloud and Energy Services. Springer, Cham. https://doi.org/10.1007/978-3-319-62238-5_6
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