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

Abstract

This survey is an up-to-date account of the research on the performance–energy trade-off in virtualized environments, specifically in virtual machine consolidation. The factors that influence the performance and energy in consolidated data centres and the performance–energy trade-off itself are analysed. Based on these factors, we propose a categorization that classifies the most important research on performance and energy in consolidated systems. We have analysed and summarized 91 selected research works from an initial set of 1030. This article summarizes all previous surveys on the subject of virtual machine consolidation and updates them with the most recent papers in the field.

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References

  1. 1.

    Adeleye O (2015) Energy efficient virtual machine management for cloud computing: a survey. Int J Sci Eng Res 6(11):1065–1071

    Google Scholar 

  2. 2.

    Ahmad RW, Gani A, Hamid SHA, Shiraz M, Yousafzai A, Xia F (2015) A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J Netw Comput Appl 52(C):11–25. https://doi.org/10.1016/j.jnca.2015.02.002

    Article  Google Scholar 

  3. 3.

    Ahmad RW, Gani A, Hamid SHA, Shiraz M, Yousafzai A, Xia F (2015) A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J Netw Comput Appl 52:11–25. https://doi.org/10.1016/j.jnca.2015.02.002

    Article  Google Scholar 

  4. 4.

    Ahmadi MR, Maleki D (2010) Performance evaluation of server virtualization in data center applications. In: 2010 5th International symposium on telecommunications, pp 638–644. https://doi.org/10.1109/ISTEL.2010.5734102

  5. 5.

    Al-Dulaimy A, Itani W, Zekri A, Zantout R (2016) Power management in virtualized data centers: state of the art. J Cloud Comput 5(1):6. https://doi.org/10.1186/s13677-016-0055-y

    Article  Google Scholar 

  6. 6.

    Alboaneen DA, Pranggono B, Tianfield H (2014) Energy-aware virtual machine consolidation for cloud data centers. In: 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp 1010–1015. https://doi.org/10.1109/UCC.2014.166

  7. 7.

    Alzamil I, Djemame K, Armstrong D, Kavanagh R (2015) Energy-aware profiling for cloud computing environments. Electron Notes Theor Comput Sci 318:91–108. Twenty-ninth and thirtieth annual UK performance engineering workshops (UKPEW). https://doi.org/10.1016/j.entcs.2015.10.021. http://www.sciencedirect.com/science/article/pii/S1571066115000626

  8. 8.

    Amannejad Y, Krishnamurthy D, Far B (2015) Detecting performance interference in cloud-based web services. In: 2015 IFIP/IEEE international symposium on integrated network management (IM), pp 423–431. https://doi.org/10.1109/INM.2015.7140319

  9. 9.

    Arianyan E, Taheri H, Sharifian S (2015) Novel energy and sla efficient resource management heuristics for consolidation of virtual machines in cloud data centers. Comput Electr Eng 47:222–240. https://doi.org/10.1016/j.compeleceng.2015.05.006

    Article  Google Scholar 

  10. 10.

    Aroca JA, Anta AF, Mosteiro MA, Thraves C, Wang L (2016) Power-efficient assignment of virtual machines to physical machines. Future Gener Comput Syst 54:82–94. https://doi.org/10.1016/j.future.2015.01.006

    Article  Google Scholar 

  11. 11.

    Arockia Ranjini A, Sahayadhas A (2017) A comparison study of various virtual machine consolidation algorithms in cloud datacenter. ARPN J Eng Appl Sci 12:125–129

    Google Scholar 

  12. 12.

    Aryania A, Aghdasi HS, Khanli LM (2018) Energy-aware virtual machine consolidation algorithm based on ant colony system. J Grid Comput. https://doi.org/10.1007/s10723-018-9428-4

    Article  Google Scholar 

  13. 13.

    Barroso LA, Clidaras J, Hoelzle U (2013) The datacenter as a computer: an introduction to the design of warehouse-scale machines. Morgan & Claypool, San Rafael. https://doi.org/10.2200/S00516ED2V01Y201306CAC024

    Google Scholar 

  14. 14.

    Barroso LA, Hlzle U (2007) The case for energy-proportional computing. Computer 40(12):33–37. https://doi.org/10.1109/MC.2007.443

    Article  Google Scholar 

  15. 15.

    Belen Bermejo CJ, Guerrero C (2018) On the linearity of performance and energy at virtual machine consolidation: the cis2 index for cpu workload in server saturation. In: Proceedings of the IEEE 20th International Conference on High Performance Computing and Communications. Exeter, pp 928–933

  16. 16.

    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. https://doi.org/10.1002/cpe.1867

    Article  Google Scholar 

  17. 17.

    Bermejo B, Filiposka S, Juiz C, Gómez B, Guerrero C (2017) Improving the energy efficiency in cloud computing data centres through resource allocation techniques. Springer, Singapore, pp 211–236. https://doi.org/10.1007/978-981-10-5026-8_9

    Google Scholar 

  18. 18.

    Bratanov S, Belenov R, Manovich N (2009) Virtual machines: a whole new world for performance analysis. SIGOPS Oper Syst Rev 43(2):46–55. https://doi.org/10.1145/1531793.1531802

    Article  Google Scholar 

  19. 19.

    Buyya R, Vecchiola C, Selvi ST (2013) Mastering cloud computing: foundations and applications programming, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco

    Google Scholar 

  20. 20.

    Bn D, Ferenc R, Siket I, Kiss (2015) Prediction models for performance, power, and energy efficiency of software executed on heterogeneous hardware. In: 2015 IEEE trustcom/BigDataSE/ISPA, vol 3, pp 178–183. https://doi.org/10.1109/Trustcom.2015.629

  21. 21.

    Caglar F, Shekhar S, Gokhale A (2013) A performance interference-aware virtual machine placement strategy for supporting soft real-time applications in the cloud. Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA, Tech. Rep. ISIS-13-105

  22. 22.

    Cao Z, Dong S (2012) Dynamic vm consolidation for energy-aware and sla violation reduction in cloud computing. In: 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies, pp 363–369. https://doi.org/10.1109/PDCAT.2012.68

  23. 23.

    Cao Z, Dong S (2013) Energy-aware framework for virtual machine consolidation in cloud computing. In: 2013 IEEE 10th International Conference on High Performance Computing and Communications 2013 IEEE International Conference on Embedded and Ubiquitous Computing, pp 1890–1895. https://doi.org/10.1109/HPCC.and.EUC.2013.271

  24. 24.

    Cao Z, Dong S (2014) An energy-aware heuristic framework for virtual machine consolidation in cloud computing. J Supercomput 69(1):429–451. https://doi.org/10.1007/s11227-014-1172-3

    Article  Google Scholar 

  25. 25.

    Chaabouni T, Khemakhem M (2017) Energy management strategy in cloud computing: a perspective study. J Supercomput. https://doi.org/10.1007/s11227-017-2154-z

    Article  Google Scholar 

  26. 26.

    Jiang C, Wang Y, Ou D, Li Y, Zhang J, Wan J, Luo B, Shi W (2017) Energy efficiency comparison of hypervisors. Sustain Comput Inf Syst. https://doi.org/10.1016/j.suscom.2017.09.005

    Article  Google Scholar 

  27. 27.

    Cui L, Cziva R, Tso FP, Pezaros DP (2016) Synergistic policy and virtual machine consolidation in cloud data centers. In: IEEE INFOCOM 2016—The 35th Annual IEEE International Conference on Computer Communications, pp 1–9. https://doi.org/10.1109/INFOCOM.2016.7524354

  28. 28.

    Dong Y, Zhou L, Jin Y, Wen Y (2015) Improving energy efficiency for mobile media cloud via virtual machine consolidation. Mobile Netw Appl 20(3):370–379. https://doi.org/10.1007/s11036-015-0595-2

    Article  Google Scholar 

  29. 29.

    Esfandiarpoor S, Pahlavan A, Goudarzi M (2013) Virtual machine consolidation for datacenter energy improvement. CoRR abs/1302.2227. http://arxiv.org/abs/1302.2227

  30. 30.

    Esfandiarpoor S, Pahlavan A, Goudarzi M (2015) Structure-aware online virtual machine consolidation for datacenter energy improvement in cloud computing. Comput Electr Eng 42(C):74–89. https://doi.org/10.1016/j.compeleceng.2014.09.005

    Article  Google Scholar 

  31. 31.

    Farahnakian F, Pahikkala T, Liljeberg P, Plosila J (2013) Energy aware consolidation algorithm based on k-nearest neighbor regression for cloud data centers. In: 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, pp 256–259. https://doi.org/10.1109/UCC.2013.51

  32. 32.

    Feller E, Morin C, Esnault A (2012) A case for fully decentralized dynamic vm consolidation in clouds. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pp 26–33. https://doi.org/10.1109/CloudCom.2012.6427585

  33. 33.

    Ferdaus MH, Murshed M (2014) Energy-aware virtual machine consolidation in iaaS cloud computing. Springer, Cham, pp 179–208. https://doi.org/10.1007/978-3-319-10530-7_8

    Google Scholar 

  34. 34.

    Ferdaus MH, Murshed M, Calheiros RN, Buyya R (2014) Virtual machine consolidation in cloud data centers using aco metaheuristic. In: Silva F, Dutra I, Santos Costa V (eds) Euro-Par 2014 parallel processing. Springer, Cham, pp 306–317

    Google Scholar 

  35. 35.

    Gilly K, Juiz C, Puigjaner R (2011) An up-to-date survey in web load balancing. World Wide Web 14(2):105–131. https://doi.org/10.1007/s11280-010-0101-5

    Article  Google Scholar 

  36. 36.

    Gondhi NK, Kailu P (2015) Prediction based energy efficient virtual machine consolidation in cloud computing. In: 2015 Second International Conference on Advances in Computing and Communication Engineering, pp 437–441. https://doi.org/10.1109/ICACCE.2015.148

  37. 37.

    Graubner P, Schmidt M, Freisleben B (2013) Energy-efficient virtual machine consolidation. IT Prof 15(2):28–34. https://doi.org/10.1109/MITP.2012.48

    Article  Google Scholar 

  38. 38.

    Han G, Que W, Jia G, Shu L (2016) An efficient virtual machine consolidation scheme for multimedia cloud computing. Sensors. https://doi.org/10.3390/s16020246

    Article  Google Scholar 

  39. 39.

    Hasan S, En Huh (2013) Heuristic based energy-aware resource allocation by dynamic consolidation of virtual machines in cloud data center. KSII Trans Internet Inf Syst 7:1825–1842

    Article  Google Scholar 

  40. 40.

    Horri A, Rahmanian A, Dastghaibyfard G (2015) Energy and performance-aware virtual machine consolidation in cloud computing a two dimensional approach. Turk J Eng 1:20–35

    Google Scholar 

  41. 41.

    Hu Y, Li T (2016) Towards efficient server architecture for virtualized network function deployment: implications and implementations. In: 2016 49th annual IEEE/ACM international symposium on microarchitecture (MICRO), pp 1–12. https://doi.org/10.1109/MICRO.2016.7783711

  42. 42.

    Huang Z, Tsang DHK (2012) Sla guaranteed virtual machine consolidation for computing clouds. In: 2012 IEEE International Conference on Communications (ICC), pp 1314–1319. https://doi.org/10.1109/ICC.2012.6363970

  43. 43.

    Huang Z, Tsang DHK, She J (212) A virtual machine consolidation framework for mapreduce enabled computing clouds. In: Proceedings of the 24th international teletraffic congress, ITC ’12, International Teletraffic Congress, pp 26:1–26:8. http://dl.acm.org/citation.cfm?id=2414276.2414308

  44. 44.

    Huber N, von Quast M, Brosig F, Hauck M, Kounev S (2012) A method for experimental analysis and modeling of virtualization performance overhead. Springer, New York, pp 353–370. https://doi.org/10.1007/978-1-4614-2326-3_19

    Google Scholar 

  45. 45.

    Janpan T, Visoottiviseth V, Takano R (2014) A virtual machine consolidation framework for cloudstack platforms. In: The International Conference on Information Networking 2014 (ICOIN2014), pp 28–33. https://doi.org/10.1109/ICOIN.2014.6799494

  46. 46.

    Joshi S, Kaur S (2015) Cuckoo search approach for virtual machine consolidation in cloud data centre. In: International Conference on Computing, Communication Automation, pp 683–686. https://doi.org/10.1109/CCAA.2015.7148461

  47. 47.

    Kakadia D, Kopri N, Varma V (2013) Network-aware virtual machine consolidation for large data centers. In: Proceedings of the third international workshop on network-aware data management, NDM ’13. ACM, New York, pp 6:1–6:8. https://doi.org/10.1145/2534695.2534702

  48. 48.

    Kang S, Kim Sg, Eom H, Yeom HY (2012) Towards workload-aware virtual machine consolidation on cloud platforms. In: Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication, ICUIMC ’12. ACM, New York, pp 45:1–45:4. https://doi.org/10.1145/2184751.2184806

  49. 49.

    Khan MA, Paplinski A, Khan AM, Murshed M, Buyya R (2018) Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management: a review. Springer, Cham, pp 135–165. https://doi.org/10.1007/978-3-319-62238-5_6

    Google Scholar 

  50. 50.

    Kharat V, Shelar M, Sane S, Jadhav R (2014) Efficient virtual machine placement with energy saving in cloud data center. J Cloud Comput Super Comput 1:15–26

    Google Scholar 

  51. 51.

    Kim S, Eom H, Yeom HY (2013) Virtual machine consolidation based on interference modeling. J Supercomput 66(3):1489–1506. https://doi.org/10.1007/s11227-013-0939-2

    Article  Google Scholar 

  52. 52.

    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. https://doi.org/10.1016/j.infsof.2008.09.009

    Article  Google Scholar 

  53. 53.

    Kolhe S, Dhage S (2012) Comparative study on virtual machine monitors for cloud. In: 2012 World congress on information and communication technologies, pp 425–430. https://doi.org/10.1109/WICT.2012.6409115

  54. 54.

    Kousiouris G, Cucinotta T, Varvarigou T (2011) The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks. J Syst Softw 84(8):1270–1291. https://doi.org/10.1016/j.jss.2011.04.013

    Article  Google Scholar 

  55. 55.

    Kumar A, Sathasivam C, Periyasamy P (2016) Virtual machine placement in cloud computing. Indian J Sci Technol 9(29). http://www.indjst.org/index.php/indjst/article/view/79768

  56. 56.

    Laili Y, Tao F, Wang F, Zhang L, Lin T (2018) An iterative budget algorithm for dynamic virtual machine consolidation under cloud computing environment. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2018.2793209

    Article  Google Scholar 

  57. 57.

    Langer SG, French T (2011) Virtual machine performance benchmarking. J Digital Imaging 24(5):883–889. https://doi.org/10.1007/s10278-010-9358-6

    Article  Google Scholar 

  58. 58.

    Leite D, Peixoto M, Santana M, Santana R (2012) Performance evaluation of virtual machine monitors for cloud computing. In: 2012 13th symposium on computer systems, pp 65–71. https://doi.org/10.1109/WSCAD-SSC.2012.22

  59. 59.

    Lent R (2011) Evaluating the performance and power consumption of systems with virtual machines. In: 2011 IEEE Third International Conference on Cloud Computing Technology and Science, pp 778–783. https://doi.org/10.1109/CloudCom.2011.120

  60. 60.

    Li H, Zhu G, Cui C, Tang H, Dou Y, He C (2016) Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing. Computing 98(3):303–317. https://doi.org/10.1007/s00607-015-0467-4

    MathSciNet  MATH  Article  Google Scholar 

  61. 61.

    Li M, Bi J, Li Z (2016) Improving consolidation of virtual machine based on virtual switching overhead estimation. J Netw Comput Appl 59:158–167. https://doi.org/10.1016/j.jnca.2015.07.008

    Article  Google Scholar 

  62. 62.

    Li X, Ventresque A, Iglesias JO, Murphy J (2015) Scalable correlation-aware virtual machine consolidation using two-phase clustering. In: 2015 International Conference on High Performance Computing Simulation (HPCS), pp 237–245. https://doi.org/10.1109/HPCSim.2015.7237045

  63. 63.

    Lin CC, Liu P, Wu JJ (2011) Energy-aware virtual machine dynamic provision and scheduling for cloud computing. In: 2011 IEEE 4th International Conference on Cloud Computing, pp 736–737. https://doi.org/10.1109/CLOUD.2011.94

  64. 64.

    Lin CC, Liu P, Wu JJ (2011) Energy-efficient virtual machine provision algorithms for cloud systems. In: Proceedings of the 2011 Fourth IEEE International Conference on Utility and Cloud Computing, UCC ’11, pp. 81–88. IEEE Computer Society, Washington. https://doi.org/10.1109/UCC.2011.21

  65. 65.

    Liu M, Li T (2014) Optimizing virtual machine consolidation performance on numa server architecture for cloud workloads. In: 2014 ACM/IEEE 41st international symposium on computer architecture (ISCA), pp 325–336. https://doi.org/10.1109/ISCA.2014.6853224

  66. 66.

    Lovász G, Niedermeier F, de Meer H (2013) Performance tradeoffs of energy-aware virtual machine consolidation. Cluster Comput 16(3):481–496. https://doi.org/10.1007/s10586-012-0214-y

    Article  Google Scholar 

  67. 67.

    Luo G, Qian Z, Dong M, Ota K, Lu S (2017) Improving performance by network-aware virtual machine clustering and consolidation. J Supercomput. https://doi.org/10.1007/s11227-017-2104-9

    Article  Google Scholar 

  68. 68.

    Masane MS, Kulkarni NP (2016) A survey on energy-aware dynamic virtual machine consolidation in cloud data centers. Int J Sci Res Dev 3(11):0613–2321

    Google Scholar 

  69. 69.

    Masoumzadeh SS, Hlavacs H (2015) Dynamic virtual machine consolidation: a multi agent learning approach. In: 2015 IEEE International Conference on Autonomic Computing, pp 161–162. https://doi.org/10.1109/ICAC.2015.17

  70. 70.

    Mastelic T, Oleksiak A, Claussen H, Brandic I, Pierson JM, Vasilakos AV (2015) Cloud computing: survey on energy efficiency. ACM Comput Surv 47(2):1–36. https://doi.org/10.1145/2656204

    Article  Google Scholar 

  71. 71.

    Molero X, Juiz C, Rodeo M (2004) Evaluacin y Modelado del Rendimiento del os Sistemas Informticos. Pearson. https://doi.org/10.2200/S00516ED2V01Y201306CAC024

  72. 72.

    Monil MAH, Qasim R, Rahman RM (2014) Energy-aware vm consolidation approach using combination of heuristics and migration control. In: Ninth International Conference on Digital Information Management (ICDIM 2014), pp 74–79. https://doi.org/10.1109/ICDIM.2014.6991413

  73. 73.

    Monil MAH, Rahman RM (2016) Vm consolidation approach based on heuristics, fuzzy logic, and migration control. J Cloud Comput 5(1):59:1–59:18. https://doi.org/10.1186/s13677-016-0059-7

    Article  Google Scholar 

  74. 74.

    Motwani A, Patel V, Patil VM (2015) Power and qos aware virtual machine consolidation in green cloud data center. Int J Electr Electron Comput Eng 4(1):93

    Google Scholar 

  75. 75.

    Najari A, Alavi SE, Noorimehr MR (2016) Optimization of dynamic virtual machine consolidation in cloud computing data centers. Optimization 7(9)

  76. 76.

    Nema P, Choudhary S, Nema T (2015) Vm consolidation technique for green cloud computing. Int J Comput Sci Inf Technol 6:4620–4624

    Google Scholar 

  77. 77.

    Nguyen TH, Francesco MD, Yla-Jaaski A (2017) Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans Serv Computing. https://doi.org/10.1109/TSC.2017.2648791

    Article  Google Scholar 

  78. 78.

    Nguyen TH, Francesco MD, Yl-Jski A (2014) A multi-resource selection scheme for virtual machine consolidation in cloud data centers. In: 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, pp 234–239. https://doi.org/10.1109/CloudCom.2014.130

  79. 79.

    Pires FL, Barán B (2015) Virtual machine placement literature review. CoRR abs/1506.01509. http://arxiv.org/abs/1506.01509

  80. 80.

    Popek GJ, Goldberg RP (1974) Formal requirements for virtualizable third generation architectures. Commun ACM 17(7):412–421. https://doi.org/10.1145/361011.361073

    MathSciNet  MATH  Article  Google Scholar 

  81. 81.

    Ribas BC, Suguimoto RM, Montaño RANR, Silva F, de Bona L, Castilho MA (2012) On modelling virtual machine consolidation to pseudo-boolean constraints. In: Pavón J, Duque-Méndez ND, Fuentes-Fernández R (eds) Adv Artif Intell IBERAMIA 2012. Springer, Berlin, pp 361–370

    Google Scholar 

  82. 82.

    Ribas BC, Suguimoto RM, Montao RANR, Silva F, Castilho M (2013) Pbfvmc: a new pseudo-boolean formulation to virtual-machine consolidation. In: 2013 Brazilian Conference on Intelligent Systems, pp 201–206. https://doi.org/10.1109/BRACIS.2013.41

  83. 83.

    Roytman A, Kansal A, Govindan S, Liu J, Nath S (2013) Pacman: performance aware virtual machine consolidation. In: Proceedings of the 10th International Conference on Autonomic Computing (ICAC 13). USENIX, San Jose, pp 83–94. https://www.usenix.org/conference/icac13/technical-sessions/presentation/roytman

  84. 84.

    Selim GEI, El-Rashidy MA, El-Fishawy NA (2016) An efficient resource utilization technique for consolidation of virtual machines in cloud computing environments. In: 2016 33rd National Radio Science Conference (NRSC), pp 316–324. https://doi.org/10.1109/NRSC.2016.7450844

  85. 85.

    Selome Kostentinos CK, Tordsson J (2018) Virtualization techniques compared: Performance, resources, and power usage overheads in clouds. In: ICPE, pp 145–156. Berlin

  86. 86.

    SPEC (2014) Power and performance benchmark methodology. Standard Performance Evaluation Corporation, Gainesville

    Google Scholar 

  87. 87.

    Sun X, Wu Q, Tan Y, Wu F (2014) Mvei: an interference prediction model for cpu-intensive application in cloud environment. In: 2014 13th international symposium on distributed computing and applications to business, engineering and science, pp 83–87. https://doi.org/10.1109/DCABES.2014.21

  88. 88.

    Takahashi S, Nakada H, Takefusa A, Kudoh T, Shigeno M, Yoshise A (2012) Virtual machine packing algorithms for lower power consumption. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pp 161–168. https://doi.org/10.1109/CloudCom.2012.6427493

  89. 89.

    Takouna I, Alzaghoul E, Meinel C (2014) Robust virtual machine consolidation for efficient energy and performance in virtualized data centers. In: 2014 IEEE International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom), pp 470–477. https://doi.org/10.1109/iThings.2014.84

  90. 90.

    Telenyk S, Zharikov E, Rolik O (2016) An approach to virtual machine placement in cloud data centers. In: 2016 International Conference Radio Electronics Info Communications (UkrMiCo), pp 1–6. https://doi.org/10.1109/UkrMiCo.2016.7739645

  91. 91.

    Teng F, Yu L, Li T, Deng D, Magoulès F (2017) Energy efficiency of vm consolidation in iaas clouds. J Supercomput 73(2):782–809. https://doi.org/10.1007/s11227-016-1797-5

    Article  Google Scholar 

  92. 92.

    Tickoo O, Iyer R, Illikkal R, Newell D (2010) Modeling virtual machine performance: challenges and approaches. SIGMETRICS Perform Eval Rev 37(3):55–60. https://doi.org/10.1145/1710115.1710126

    Article  Google Scholar 

  93. 93.

    Urul G (2018) Energy-efficient dynamic virtual machine allocation with CPU usage prediction in cloud datacenters. Bilkent University

  94. 94.

    Varasteh A, Goudarzi M (2017) Server consolidation techniques in virtualized data centers: a survey. IEEE Syst J 11(2):772–783. https://doi.org/10.1109/JSYST.2015.2458273

    Article  Google Scholar 

  95. 95.

    Verma A, Bagrodia J, Jaiswal V (2014) Virtual machine consolidation in the wild. In: Proceedings of the 15th International Middleware Conference, Middleware ’14. ACM, New York, pp 313–324. https://doi.org/10.1145/2663165.2663316

  96. 96.

    Wu Q, Ishikawa F (2015) Heterogeneous virtual machine consolidation using an improved grouping genetic algorithm. In: 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, pp 397–404. https://doi.org/10.1109/HPCC-CSS-ICESS.2015.92

  97. 97.

    Wu Y, Tang M, Fraser W (2012) A simulated annealing algorithm for energy efficient virtual machine placement. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 1245–1250. https://doi.org/10.1109/ICSMC.2012.6377903

  98. 98.

    Yang JS, Liu P, Wu JJ (2012) Workload characteristics-aware virtual machine consolidation algorithms. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pp 42–49. https://doi.org/10.1109/CloudCom.2012.6427540

  99. 99.

    Yang Z, Fang H, Wu Y, Li C, Zhao B, Huang HH (2012) Understanding the effects of hypervisor i/o scheduling for virtual machine performance interference. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pp 34–41. https://doi.org/10.1109/CloudCom.2012.6427495

  100. 100.

    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 

  101. 101.

    Ye K, Jiang X, Chen S, Huang D, Wang B (2010) Analyzing and modeling the performance in xen-based virtual cluster environment. In: 2010 IEEE 12th International Conference on High Performance Computing and Communications (HPCC), pp 273–280. https://doi.org/10.1109/HPCC.2010.79

  102. 102.

    Yuan P, Ding C, Cheng L, Li S, Jin H, Cao W (2010) Vits test suit: a micro-benchmark for evaluating performance isolation of virtualization systems. In: 2010 IEEE 7th International Conference on E-Business Engineering, pp 132–139. https://doi.org/10.1109/ICEBE.2010.71

  103. 103.

    Zhang W, Liu J, Liu C, Zheng Q, Zhang W (2015) Workload modeling for virtual machine-hosted application. Expert Syst Appl 42(4):1835–1844. https://doi.org/10.1016/j.eswa.2014.10.012

    Article  Google Scholar 

  104. 104.

    Zhang Y, Ansari N (2013) Heterogeneity aware dominant resource assistant heuristics for virtual machine consolidation. In: 2013 IEEE Global Communications Conference (GLOBECOM), pp 1297–1302. https://doi.org/10.1109/GLOCOM.2013.6831253

  105. 105.

    Zhao C, Liu J (2015) A virtual machine dynamic consolidation algorithm based dynamic complementation and ffd algorithm. In: 2015 Fifth International Conference on Communication Systems and Network Technologies, pp 333–338. https://doi.org/10.1109/CSNT.2015.38

  106. 106.

    Zheng Q, Li J, Dong B, Li R, Shah N, Tian F (2015) Multi-objective optimization algorithm based on bbo for virtual machine consolidation problem. In: 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS), pp 414–421. https://doi.org/10.1109/ICPADS.2015.59

  107. 107.

    Zhou Z, Hu Z, Yu J, Abawajy J, Chowdhury M (2017) Energy-efficient virtual machine consolidation algorithm in cloud data centers. J Central South Univ 24(10):2331–2341. https://doi.org/10.1007/s11771-017-3645-z

    Article  Google Scholar 

  108. 108.

    Zhu Q, Tung T (2012) A performance interference model for managing consolidated workloads in qos-aware clouds. In: 2012 IEEE Fifth International Conference on Cloud Computing, pp 170–179. https://doi.org/10.1109/CLOUD.2012.25

  109. 109.

    Zola E, Kassler AJ (2015) Energy efficient virtual machine consolidation under uncertain input parameters for green data centers. In: 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), pp 436–439. https://doi.org/10.1109/CloudCom.2015.15

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Acknowledgements

This research was supported by the Spanish Government (Agencia Estatal de Investigación) and the European Commission (Fondo Europeo de Desarrollo Regional) through Grant No. TIN2017-88547-P (MINECO/AEI/FEDER, UE).

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Correspondence to Belen Bermejo.

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Bermejo, B., Juiz, C. & Guerrero, C. Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance. J Supercomput 75, 808–836 (2019). https://doi.org/10.1007/s11227-018-2613-1

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Keywords

  • Virtualization
  • Virtual machine consolidation
  • Performance degradation
  • Energy efficiency
  • Performance–energy trade-off