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


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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  1. 1.

    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

  2. 2.

    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

  3. 3.

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

  4. 4.

    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

  5. 5.

    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 

  6. 6.

    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

    Article  Google Scholar 

  7. 7.

    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

    Article  Google Scholar 

  8. 8.

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

    Google Scholar 

  9. 9.

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

    Article  Google Scholar 

  10. 10.

    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.

    Article  Google Scholar 

  11. 11.

    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..

  12. 12.

    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

  13. 13.

    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

  14. 14.

    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

  15. 15.

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

  16. 16.

    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

  17. 17.

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

  18. 18.

    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

  19. 19.

    Ferrer M: Measuring overhead introduced by vmware workstation hosted virtual machine monitor network subsystem. Technical University of Catalonia. Accessed 2 Oct 2019

  20. 20.

    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

  21. 21.

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

  22. 22.

    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

  23. 23.

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

    Google Scholar 

  24. 24.

    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

  25. 25.

    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

  26. 26.

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

  27. 27.

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

  28. 28.

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

  29. 29.

    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

    Article  Google Scholar 

  30. 30.

    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

  31. 31.

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

    Article  Google Scholar 

  32. 32.

    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

  33. 33.

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

    Google Scholar 

  34. 34.

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

    Article  Google Scholar 

  35. 35.

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

  36. 36.

    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

  37. 37.

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

    Google Scholar 

  38. 38.

    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

  39. 39.

    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

    Article  Google Scholar 

  40. 40.

    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

  41. 41.

    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

  42. 42.

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

  43. 43.

    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)

  44. 44.

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

    MathSciNet  Article  Google Scholar 

  45. 45.

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

    Google Scholar 

  46. 46.

    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

    Google Scholar 

  47. 47.

    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

  48. 48.

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

    Google Scholar 

  49. 49.

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

  50. 50.

    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

    Article  Google Scholar 

  51. 51.

    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

    Google Scholar 

  52. 52.

    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

  53. 53.

    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

  54. 54.

    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

  55. 55.

    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

  56. 56.

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

    Article  Google Scholar 

  57. 57.

    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

    Google Scholar 

  58. 58.

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

    Article  Google Scholar 

  59. 59.

    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

    Article  Google Scholar 

  60. 60.

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

    MathSciNet  Google Scholar 

  61. 61.

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

  62. 62.

    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

    Google Scholar 

  63. 63.

    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).

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  • Virtual machine consolidation
  • Performance
  • Overhead