a systematic literature review on virtual machine consolidation

Virtual machine consolidation: a systematic review of its overhead influencing factors

• Published: 22 October 2019
• Volume 76 , pages 324–361, ( 2020 )

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• Belen Bermejo   ORCID: orcid.org/0000-0002-9283-2378 1 &
• Carlos Juiz 1  

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This survey is an up-to-date account of the research on virtual machine consolidation overhead. The overhead influencing factors are analyzed throughout this work. Based on these factors, we propose a categorization that classifies the most important research works on virtualization and virtual machine consolidation overhead. We have analyzed and summarized 46 selected research works from an initial set of 428, attempting to update the state of the art with the most recent papers in this field.

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Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance

Perspective of virtual machine consolidation in cloud computing: a systematic survey

A Meta-Analysis on the Algorithms for Virtual Machine Consolidation

Apparao P, Iyer R, Zhang X, Newell D, Adelmeyer T (2008) Characterization and analysis of a server consolidation benchmark. In: Proceedings of the Fourth ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments. ACM, pp 21–30

Barham P, Dragovic B, Fraser K, Hand S, Harris T, Ho A, Neugebauer R, Pratt I, Warfield A (2003) Xen and the art of virtualization. In: ACM SIGOPS Operating Systems Review, vol 37. ACM, pp 164–177

Bastoni A, Bovet DP, Cesati M, Palana P (2010) Discovering hypervisor overheads using micro and macrobenchmarks

Bermejo B, Filiposka S, Juiz C, Gómez B, Guerrero C (2017) Improving the energy efficiency in cloud computing data centres through resource allocation techniques. In: Sanjay C, Gaurav S, Rajkumar B (eds) Research advances in cloud computing. Springer, Berlin, pp 211–236

Bermejo B, Juiz C, Guerrero C (2019) Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance. J Supercomput 75(2):808–836

Article   Google Scholar  

Bhukya DP, Ramachandram S (2009) Performance evaluation of virtualization and non virtualization on different workloads using doe methodology. Int J Eng Technol 1(5):404

Bratanov S, Belenov R, Manovich N (2009) Virtual machines: a whole new world for performance analysis. ACM SIGOPS Oper Syst Rev 43(2):46–55

Buyya R, Vecchiola C, Selvi ST (2013) Mastering cloud computing: foundations and applications programming. Newnes, Lithgow

Google Scholar  

Casazza JP, Greenfield M, Shi K (2006) Redefining server performance characterization for virtualization benchmarking. Intel Technol J 10(3):243–251

Chae M, Lee H, Lee K (2019) A performance comparison of linux containers and virtual machines using Docker and KVM. Cluster Comput 22(1):1765–1775. https://doi.org/10.1007/s10586-017-1511-2

Charalambous M (2010) Application performance overhead and scalability for execution on virtual machines over multicore processors. Master’s thesis, \(\varPi \alpha \nu \varepsilon \pi \iota \sigma \tau \acute{\eta }\mu \iota \text{o}\, \text{ K }\acute{\nu }\pi \rho \text{ o }\upsilon ,\, \Sigma \chi \text{ o }\lambda \acute{\eta }\, \varTheta \varepsilon \tau \iota \kappa \acute{\omega }\nu \, \kappa \alpha \iota \, \text{ E }\varphi \alpha \rho \mu \text{ o }\sigma \mu \acute{\varepsilon }\nu \omega \nu \, \text{ E }\pi \iota \sigma \tau \eta \mu \acute{\omega }\nu\) /University of..

Che J, Shi C, Yu Y, Lin W (2010) A synthetical performance evaluation of OpenVZ, XEN and KVM. In: 2010 IEEE Asia-Pacific Services Computing Conference. IEEE, pp 587–594

Chen L, Patel S, Shen H, Zhou Z (2015) Profiling and understanding virtualization overhead in cloud. In: 2015 44th International Conference on Parallel Processing. IEEE, pp 31–40

Cherkasova L, Gardner R (2005) Measuring CPU overhead for I/O processing in the Xen virtual machine monitor. In: USENIX Annual Technical Conference, General Track, vol 50

Chiueh SNTC, Brook S (2005) A survey on virtualization technologies. Rpe Report 142

Clark B, Deshane T, Dow EM, Evanchik S, Finlayson M, Herne J, Matthews JN (2004) Xen and the art of repeated research. In: USENIX Annual Technical Conference, FREENIX Track, pp 135–144

Devanathan Nandhagopal NM, Ravichandran S, Malpani S: VMware and Xen hypervisor performance comparisons in thick and thin provisioned environments

Felter W, Ferreira A, Rajamony R, Rubio J (2015) An updated performance comparison of virtual machines and linux containers. In: 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). IEEE, pp 171–172

Ferrer M: Measuring overhead introduced by vmware workstation hosted virtual machine monitor network subsystem. Technical University of Catalonia. http://studies.ac.upc.edu/doctorat/ENGRAP/Miquel.pdf . Accessed 2 Oct 2019

Ganesan R, Murarka Y, Sarkar S, Frey K (2013) Empirical study of performance benefits of hardware assisted virtualization. In: Proceedings of the 6th ACM India Computing Convention. ACM, p 1

Gordon A, Ben-Yehuda M, Filimonov D, Dahan M (2011) Vamos: virtualization aware middleware. In: Proceedings of the 3rd Workshop on I/O Virtualization

Gottschlag M, Hillenbrand M, Kehne J, Stoess J, Bellosa F (2013) Logv: Low-overhead GPGPU virtualization. In: 2013 IEEE 10th International Conference on High Performance Computing and Communications and 2013 IEEE International Conference on Embedded and Ubiquitous Computing. IEEE, pp 1721–1726

Gregg B (2013) Systems performance: enterprise and the cloud. Pearson Education, London

Huang W, Liu J, Abali B, Panda DK (2006) A case for high performance computing with virtual machines. In: Proceedings of the 20th Annual International Conference on Supercomputing. ACM, pp 125–134

Huber N, von Quast M, Brosig F, Hauck M, Kounev S (2011) A method for experimental analysis and modeling of virtualization performance overhead. In: International Conference on Cloud Computing and Services Science. Springer, Berlin, pp 353–370

Huber N, von Quast M, Hauck M, Kounev S (2011) Evaluating and modeling virtualization performance overhead for cloud environments. In: CLOSER, pp 563–573

Hwang D, George EI, Barnes RD (2009) SMP virtualization performance evaluation

Juiz C (2001) Performance modelling of asynchronous data transfer components in soft real-time systems. Ph.D. thesis, Universitat de les Illes Balears

Kitchenham B, Brereton OP, Budgen D, Turner M, Bailey J, Linkman S (2009) Systematic literature reviews in software engineering—a systematic literature review. Inf Softw Technol 51(1):7–15

Li J, Wang Q, Jayasinghe D, Park J, Zhu T, Pu C (2013) Performance overhead among three hypervisors: an experimental study using hadoop benchmarks. In: 2013 IEEE International Congress on Big Data. IEEE, pp 9–16

Lovász G, Niedermeier F, De Meer H (2013) Performance tradeoffs of energy-aware virtual machine consolidation. Cluster Comput 16(3):481–496

Macdonell C, Lu P (2007) Pragmatics of virtual machines for high-performance computing: a quantitative study of basic overheads. In: Proceedings of the 2007 High Performance Computing and Simulation Conference. Citeseer

Marinescu DC (2017) Cloud computing: theory and practice. Morgan Kaufmann, Burlington

McDougall R, Anderson J (2010) Virtualization performance: perspectives and challenges ahead. ACM SIGOPS Oper Syst Rev 44(4):40–56

Menascé DA (2005) Virtualization: concepts, applications, and performance modeling. In: International CMG Conference, pp 407–414

Menon A, Santos JR, Turner Y, Janakiraman GJ, Zwaenepoel W (2005) Diagnosing performance overheads in the Xen virtual machine environment. In: Proceedings of the 1st ACM/USENIX International Conference on Virtual Execution Environments. ACM, pp 13–23

Molero X, Juiz C, Rodeño MJ (2004) Evaluación y modelado del rendimiento de los sistemas informáticos. Prentice Hall, London

Morabito R, Kjällman J, Komu M (2015) Hypervisors versus lightweight virtualization: a performance comparison. In: 2015 IEEE International Conference on Cloud Engineering. IEEE, pp 386–393

Neiger G, Santony A, Leung F, Rogers D, Uhlig R (2006) Virtualization technology: hardware support for efficient processor virtualization. Intel Technol J 10(3):167–178

Ongaro D, Cox AL, Rixner S (2008) Scheduling i/o in virtual machine monitors. In: Proceedings of the Fourth ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments. ACM, pp 1–10

Padala P, Zhu X, Wang Z, Singhal S, Shin KG et al (2007) Performance evaluation of virtualization technologies for server consolidation. HP Labs Tec. Report 137

Padala PR (2018) Virtualization of data centers: study on server energy consumption performance

Pedretti K, Bridges PG, Lange JR, Dinda P, Bae C, Soltero P, Merritt A (2011) Minimal-overhead virtualization of a large scale supercomputer. Tech. rep., Sandia National Lab.(SNL-NM), Albuquerque, NM (United States)

Popek GJ, Goldberg RP (1974) Formal requirements for virtualizable third generation architectures. Commun ACM 17(7):412–421

Article   MathSciNet   Google Scholar  

Portnoy M (2012) Virtualization essentials, vol 19. Wiley, New York

Pousa D, Rufino J (2017) Evaluation of type-1 hypervisors on desktop-class virtualization hosts. IADIS J Comput Sci Inf Syst 12(2):86–101

ur Rahman H, Wang G, Chen J, Jiang H (2018) Performance evaluation of hypervisors and the effect of virtual CPU on performance. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, pp 772–779

Revelle D (2011) Hypervisors and virtual machines: implementation insights on the x86 architecture. Usenix Adv Comput Syst Assoc 36(5):17–22

Shea RW (2016) Performance and energy efficiency of virtual machine based clouds. Ph.D. thesis, Applied Sciences: School of Computing Science

Shetty J, Upadhaya S, Rajarajeshwari H, Shobha G, Chandra J (2017) An empirical performance evaluation of docker container, openstack virtual machine and bare metal server. Indones J Electr Eng Comput Sci 7(1):205–213

Sivaraman E, Manickachezian R (2016) Research and performance evaluation of open source and commercial virtualization hypervisors. Commercial virtualization hypervisors. Int J Sci Adv Res Technol (IJSART) 2(10):368–374

Soundararajan V, Agrawal B, Herndon B, Sethuraman P, Taheri R (2014) Benchmarking a virtualization platform. In: 2014 IEEE International Symposium on Workload Characterization (IISWC). IEEE, pp 99–109

Tikotekar A, Vallée G, Naughton T, Ong H, Engelmann C, Scott SL (2008) An analysis of HPC benchmarks in virtual machine environments. In: European Conference on Parallel Processing. Springer, Berlin, pp 63–71

Tong G, Jin H, Xie X, Cao W, Yuan P (2011) Measuring and analyzing CPU overhead of virtualization system. In: 2011 IEEE Asia-Pacific Services Computing Conference. IEEE, pp 243–250

Vasilas D, Gerangelos S, Koziris N (2016) VGVM: Efficient GPU capabilities in virtual machines. In: 2016 International Conference on High Performance Computing and Simulation (HPCS). IEEE, pp 637–644

Waldspurger CA (2002) Memory resource management in VMware ESX server. ACM SIGOPS Oper Syst Rev 36(SI):181–194

Wang B, Song Y, Sun Y, Liu J (2018) Analysis model for server consolidation of virtualized heterogeneous data centers providing internet services. Cluster Comput 22(3):1–18

Whitaker A, Shaw M, Gribble SD (2002) Scale and performance in the Denali isolation kernel. ACM SIGOPS Oper Syst Rev 36(SI):195–209

Xu F, Liu F, Jin H, Vasilakos AV (2014) Managing performance overhead of virtual machines in cloud computing: a survey, state of the art, and future directions. Proc IEEE 102(1):11–31

Yamamoto VYOVT (2008) Server virtualization technology and its latest trends. Fujitsu Sci Tech J 44(1):46–52

MathSciNet   Google Scholar  

Yaqub N (2012) Comparison of virtualization performance: VMware and KVM. Master’s thesis

Ye K, Che J, He Q, Huang D, Jiang X (2012) Performance combinative evaluation from single virtual machine to multiple virtual machine systems. Int J Numer Anal Model 9(2):351–370

Younge AJ, Henschel R, Brown JT, Von Laszewski G, Qiu J, Fox GC (2011) Analysis of virtualization technologies for high performance computing environments. In: 2011 IEEE 4th International Conference on Cloud Computing. IEEE, pp 9–16

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Bermejo, B., Juiz, C. Virtual machine consolidation: a systematic review of its overhead influencing factors. J Supercomput 76 , 324–361 (2020). https://doi.org/10.1007/s11227-019-03025-y

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Published : 22 October 2019

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DOI : https://doi.org/10.1007/s11227-019-03025-y

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• DOI: 10.1145/3470972
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A Systematic Literature Review on Virtual Machine Consolidation

• Alexandre H. T. Dias , L. H. A. Correia , N. Malheiros
• Published in ACM Computing Surveys 4 October 2021
• Computer Science, Engineering

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Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance

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• He X Shen J Liu F Wang B Zhong G Jiang J (2022) A two-stage scheduling method for deadline-constrained task in cloud computing Cluster Computing 10.1007/s10586-022-03561-y 25 :5 (3265-3281) Online publication date: 1-Oct-2022 https://dl.acm.org/doi/10.1007/s10586-022-03561-y
• Bermejo B Juiz C (2021) On the classification and quantification of server consolidation overheads The Journal of Supercomputing 10.1007/s11227-020-03258-2 77 :1 (23-43) Online publication date: 1-Jan-2021 https://dl.acm.org/doi/10.1007/s11227-020-03258-2
• Pourghebleh B Aghaei Anvigh A Ramtin A Mohammadi B (2021) The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments Cluster Computing 10.1007/s10586-021-03294-4 24 :3 (2673-2696) Online publication date: 1-Sep-2021 https://dl.acm.org/doi/10.1007/s10586-021-03294-4
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