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Challenges of server consolidation in virtualized data centers and open research issues: a systematic literature review

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A Correction to this article was published on 16 December 2019

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Abstract

With the increasing demands for cloud computing services, the development of technologies based on virtualization in data centers was noticed. In the virtualized data center, the efficient mapping of virtual machines to physical machines is done using the consolidation technique. Due to the advantages of the server consolidation technique, a large body of research has been done in this field. A comprehensive study on the different server consolidation solutions has not been done yet, though. In this study, a systematic review has been done on a set of researches related to server consolidation. After investigating the considered researches, their proposed solutions were categorized into three groups based on the type of decision making for running the consolidation process. Groups involve static method, dynamic method (including threshold-based and periodic-based adaptation) and prediction-based dynamic method. Thereafter, we discussed handling the challenges presented in each research by investigating the proposed approach for developing consolidation technique. Then, the open issues in each study were expressed. Finally, the objectives, evaluation parameters, optimization methods and the affecting parameters of server consolidation in all studies were investigated and analyzed.

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  • 16 December 2019

    The wording of Sasan Hossein Alizadeh’s name was incorrect. The correct wording is given here. The original article has been corrected.

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References

  1. Abdelmaboud A, Jawawi DN, Ghani I, Elsafi A, Kitchenham B (2015) Quality of service approaches in cloud computing: a systematic mapping study. J Syst Softw 101:159–179

    Google Scholar 

  2. Manvi SS, Shyam GK (2014) Resource management for Infrastructure as a Service (IaaS) in cloud computing: a survey. J Netw Comput Appl 41:424–440

    Google Scholar 

  3. Armburst M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2015) Above the clouds: A view of cloud computing. Berkeley reliable adaptive distributed systems laboratory (RADLab)

  4. Varasteh A, Goudarzi M (2017) Server consolidation techniques in virtualized datacenters: a survey. IEEE Syst J 11(2):772–783

    Google Scholar 

  5. Hameed A, Khoshkbarforoushha A, Ranjan R, Jayaraman PP, Kolodziej J, Balaji P, Zeadally S, Malluhi QM, Tziritas N, Vishnu A, Khan SU (2016) A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7):751–774

    MathSciNet  Google Scholar 

  6. Barroso LA, Hölzle U (2007) The case for energy-proportional computing. Computer 40(12):33–37

    Google Scholar 

  7. Cao J, Hwang K, Li K, Zomaya AY (2013) Optimal multiserver configuration for profit maximization in cloud computing. IEEE Trans Parallel Distrib Syst 24(6):1087–1096

    Google Scholar 

  8. 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

    Google Scholar 

  9. Le D, Wang H (2011) An effective memory optimization for virtual machine-based systems. IEEE Trans Parallel Distrib Syst 22(10):1705–1713

    Google Scholar 

  10. Jung G, Joshi KR, Hiltunen MA, Schlichting RD, Pu C (2008) Generating adaptation policies for multi-tier applications in consolidated server environments. In: International Conference on Autonomic Computing, 2008. ICAC’08. IEEE, pp 23–32

  11. Beloglazov A, Buyya R (2012) Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud datacenters. Concurr Comput Pract Exp 24(13):1397–1420

    Google Scholar 

  12. Setzer T, Bichler M (2013) Using matrix approximation for high-dimensional discrete optimization problems: server consolidation based on cyclic time-series data. Eur J Oper Res 227(1):62–75

    MathSciNet  MATH  Google Scholar 

  13. da Silva RA, da Fonseca NL (2016) Topology-aware virtual machine placement in datacenters. J Grid Comput 14(1):75–90

    Google Scholar 

  14. Garg SK, Toosi AN, Gopalaiyengar SK, Buyya R (2014) SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. J Netw Comput Appl 45:108–120

    Google Scholar 

  15. Corradi A, Fanelli M, Foschini L (2014) VM consolidation: a real case based on OpenStack Cloud. Future Gener Comput Syst 32:118–127

    Google Scholar 

  16. Hankendi C, Coskun AK (2017) Scale 8 cap: scaling-aware resource management for consolidated multi-threaded applications. ACM Trans Des Autom Electron Syst 22(2):30

    Google Scholar 

  17. Bila N, Wright EJ, Lara ED, Joshi K, Lagar-Cavilla HA, Park E, Goel A, Hiltunen M, Satyanarayanan M (2015) Energy-oriented partial desktop virtual machine migration. ACM Trans Comput Syst 33(1):2

    Google Scholar 

  18. Hieu NT, Di Francesco M, Ylä-Jääski A (2015) Virtual machine consolidation with usage prediction for energy-efficient cloud datacenters. In: IEEE 8th International Conference on Cloud Computing (CLOUD), 2015. IEEE, pp 750–757

  19. Li X, Qian Z, Lu S, Wu J (2013) Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a datacenter. Math Comput Model 58(5):1222–1235

    Google Scholar 

  20. Han G, Que W, Jia G, Zhang W (2018) Resource-utilization-aware energy efficient server consolidation algorithm for green computing in IIOT. J Netw Comput Appl 103:205–214

    Google Scholar 

  21. Deng W, Liu F, Jin H, Liao X, Liu H, Chen L (2012) Lifetime or energy: consolidating servers with reliability control in virtualized cloud datacenters. In: IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom), 2012. IEEE, pp 18–25

  22. Deng W, Liu F, Jin H, Liao X, Liu H (2014) Reliability-aware server consolidation for balancing energy-lifetime tradeoff in virtualized cloud datacenters. Int J Commun Syst 27(4):623–642

    Google Scholar 

  23. Fard SYZ, Ahmadi MR, Adabi S (2017) A dynamic VM consolidation technique for QoS and energy consumption in cloud environment. The J Supercomput 73(10):4347–4368

    Google Scholar 

  24. Kim SG, Eom H, Yeom HY (2013) Virtual machine consolidation based on interference modeling. J Supercomput 66(3):1489–1506

    Google Scholar 

  25. Gupta D, Cherkasova L, Gardner R, Vahdat A (2006) Enforcing performance isolation across virtual machines in Xen. In: Proceedings of the ACM/IFIP/USENIX 2006 International Conference on Middleware. Springer, New York, pp 342–362

  26. Luo G, Qian Z, Dong M, Ota K, Lu S (2017) Improving performance by network-aware virtual machine clustering and consolidation. J Supercomput 74:1–19

    Google Scholar 

  27. Mohamadi Bahram Abadi R, Rahmani AM, Alizadeh SH (2018) Server consolidation techniques in virtualized data centers of cloud environments: A systematic literature review. Softw Pract Exp 48(9):1688–1726

    Google Scholar 

  28. Kitchenham B (2004) Procedures for performing systematic reviews, vol 33. Keele University, Keele, pp 1–26

    Google Scholar 

  29. Li Z, Zhang H, O’Brien L, Cai R, Flint S (2013) On evaluating commercial cloud services: a systematic review. J Syst Softw 86(9):2371–2393

    Google Scholar 

  30. Procaccianti G, Lago P, Bevini S (2015) A systematic literature review on energy efficiency in cloud software architectures. Sustain Comput Inform Syst 7:2–10

    Google Scholar 

  31. Aznoli F, Navimipour NJ (2017) Cloud services recommendation: reviewing the recent advances and suggesting the future research directions. J Netw Comput Appl 77:73–86

    Google Scholar 

  32. Jula A, Sundararajan E, Othman Z (2014) Cloud computing service composition: a systematic literature review. Expert Syst Appl 41(8):3809–3824

    Google Scholar 

  33. Zhang H, Babar MA, Tell P (2011) Identifying relevant studies in software engineering. Inf Softw Technol 53(6):625–637

    Google Scholar 

  34. Milani AS, Navimipour NJ (2016) Load balancing mechanisms and techniques in the cloud environments: systematic literature review and future trends. J Netw Comput Appl 71:86–98

    Google Scholar 

  35. Tang Z, Mo Y, Li K, Li K (2014) Dynamic forecast scheduling algorithm for virtual machine placement in cloud computing environment. J Supercomput 70(3):1279–1296

    Google Scholar 

  36. Li H, Zhu G, Cui C, Tang H, Dou Y, He C (2016) Energy-efficient migration and consolidation algorithm of virtual machines in datacenters for cloud computing. Computing 98(3):303–317

    MathSciNet  MATH  Google Scholar 

  37. Mastroianni C, Meo M, Papuzzo G (2013) Probabilistic consolidation of virtual machines in self-organizing cloud datacenters. IEEE Trans Cloud Comput 1(2):215–228

    Google Scholar 

  38. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of datacenters for cloud computing. Future Gener Comput Syst 28(5):755–768

    Google Scholar 

  39. Ferreto TC, Netto MA, Calheiros RN, De Rose CA (2011) Server consolidation with migration control for virtualized datacenters. Future Gener Comput Syst 27(8):1027–1034

    Google Scholar 

  40. Alicherry M, Lakshman TV (2012) Network aware resource allocation in distributed clouds. In: Infocom, 2012 proceedings IEEE. IEEE, pp 963–971

  41. Steiner M, Gaglianello BG, Gurbani V, Hilt V, Roome WD, Scharf M, Voith T (2012) Network-aware service placement in a distributed cloud environment. ACM SIGCOMM Comput Commun Rev 42(4):73–74

    Google Scholar 

  42. Stoer M, Wagner F (1997) A simple min-cut algorithm. J ACM 44(4):585–591

    MathSciNet  MATH  Google Scholar 

  43. Sedaghat M, Hernández-Rodriguez F, Elmroth E (2016) Decentralized cloud datacenter reconsolidation through emergent and topology-aware behavior. Future Gener Comput Syst 56:51–63

    Google Scholar 

  44. Li W, Tordsson J, Elmroth E (2011) Virtual machine placement for predictable and time-constrained peak loads. In: International Workshop on Grid Economics and Business Models. Springer, Berlin, pp 120–134

  45. Perumal V, Subbiah S (2014) Power-conservative server consolidation based resource management in cloud. Int J Netw Manag 24(6):415–432

    Google Scholar 

  46. Shahdi-Pashaki S, Teymourian E, Tavakkoli-Moghaddam R (2018) New approach based on group technology for the consolidation problem in cloud computing-mathematical model and genetic algorithm. Comput Appl Math 37(1):693–718

    MathSciNet  MATH  Google Scholar 

  47. Berral García JL, Gavaldà Mestre R, Torres Viñals J (2010) An integer linear programming representation for data-center power-aware management

  48. Ghribi C, Hadji M, Zeghlache D (2013) Energy efficient vm scheduling for cloud datacenters: Exact allocation and migration algorithms. In: 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2013. IEEE, pp 671–678

  49. Speitkamp B, Bichler M (2010) A mathematical programming approach for server consolidation problems in virtualized datacenters. IEEE Trans Serv Comput 3(4):266–278

    Google Scholar 

  50. Schwarzkopf M, Konwinski A, Abd-El-Malek M, Wilkes J (2013) Omega: flexible, scalable schedulers for large compute clusters. In: Proceedings of the 8th ACM European Conference on Computer Systems. ACM, pp 351–364

  51. Yousefipour A, Rahmani AM, Jahanshahi M (2018) Energy and cost-aware virtual machine consolidation in cloud computing. Softw Pract Exp 48(10):1758–1774

    Google Scholar 

  52. Yesodha R, Amudha T (2012) A comparative study on heuristic procedures to solve bin packing problems. Int J Found Comput Sci Technol 2(6):37–49

    Google Scholar 

  53. Stillwell M, Schanzenbach D, Vivien F, Casanova H (2010) Resource allocation algorithms for virtualized service hosting platforms. J Parallel Distrib Comput 70(9):962–974

    MATH  Google Scholar 

  54. Cao Z, Dong S (2012) Dynamic VM consolidation for energy-aware and SLA violation reduction in cloud computing. In: 13th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2012. IEEE, pp 363–369

  55. Tchana A, De Palma N, Safieddine I, Hagimont D (2016) Software consolidation as an efficient energy and cost saving solution. Future Gener Comput Syst 58:1–12

    Google Scholar 

  56. Asyabi E, Azhdari A, Dehsangi M, Khan MG, Sharifi M, Azhari SV (2016) Kani: a QoS-aware hypervisor-level scheduler for cloud computing environments. Clust Comput 19(2):567–583

    Google Scholar 

  57. Abdelsamea A, El-Moursy AA, Hemayed EE, Eldeeb H (2017) Virtual machine consolidation enhancement using hybrid regression algorithms. Egypt Inform J 18(3):161–170

    Google Scholar 

  58. Witanto JN, Lim H, Atiquzzaman M (2018) Adaptive selection of dynamic VM consolidation algorithm using neural network for cloud resource management. Future Gener Comput Syst 87:35–42

    Google Scholar 

  59. Park K, Pai VS (2006) CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Oper Syst Rev 40(1):65–74

    Google Scholar 

  60. Koomey J (2011) Growth in datacenter electricity use 2005 to 2010. A report by Analytical Press, completed at the request of The New York Times, 9

  61. 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

    Google Scholar 

  62. Arroba P, Moya JM, Ayala JL, Buyya R (2017) Dynamic voltage and frequency scaling-aware dynamic consolidation of virtual machines for energy efficient cloud datacenters. Concurr Comput Pract Exp 29(10):e4067

    Google Scholar 

  63. Lee EK, Viswanathan H, Pompili D (2012) Vmap: proactive thermal-aware virtual machine allocation in hpc cloud datacenters. In: 19th International Conference on High Performance Computing (HiPC), 2012. IEEE, pp 1–10

  64. Mukherjee T, Banerjee A, Varsamopoulos G, Gupta SK, Rungta S (2009) Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous datacenters. Comput Netw 53(17):2888–2904

    MATH  Google Scholar 

  65. Rodero I, Jaramillo J, Quiroz A, Parashar M, Guim F, Poole S (2010) Energy-efficient application-aware online provisioning for virtualized clouds and datacenters. In: Green Computing Conference, 2010 International. IEEE, pp 31–45

  66. Tang Q, Gupta SKS, Varsamopoulos G (2008) Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing datacenters: a cyber-physical approach. IEEE Trans Parallel Distrib Syst 19(11):1458–1472

    Google Scholar 

  67. Lee EK, Viswanathan H, Pompili D (2015) Proactive thermal-aware resource management in virtualized HPC cloud datacenters. IEEE Trans Cloud Comput 5(2):234–248

    Google Scholar 

  68. Meng X, Pappas V, Zhang L (2010) Improving the scalability of datacenter networks with traffic-aware virtual machine placement. In: INFOCOM, 2010 Proceedings IEEE. IEEE, pp 1–9

  69. Huang Z, Tsang DH (2012) SLA guaranteed virtual machine consolidation for computing clouds. In: IEEE International Conference on Communications (ICC), 2012. IEEE, pp 1314–1319

  70. Huang Z, Tsang DH (2016) M-convex VM consolidation: towards a better VM workload consolidation. IEEE Trans Cloud Comput 4(4):415–428

    MathSciNet  Google Scholar 

  71. Singh R, Sharma U, Cecchet E, Shenoy P (2010) Autonomic mix-aware provisioning for non-stationary datacenter workloads. In: Proceedings of the 7th International Conference on Autonomic Computing. ACM, pp 21–30

  72. Lama P, Guo Y, Zhou X (2013) Autonomic performance and power control for co-located web applications on virtualized servers. In: IEEE/ACM 21st International Symposium on Quality of Service (IWQoS), 2013. IEEE, pp 1–10

  73. Xiao Z, Chen Q, Luo H (2014) Automatic scaling of internet applications for cloud computing services. IEEE Trans Comput 63(5):1111–1123

    MathSciNet  MATH  Google Scholar 

  74. Anglano C, Canonico M, Guazzone M (2017) FCMS: a fuzzy controller for CPU and memory consolidation under SLA constraints. Concurr Comput Pract Exp 29(5):e3968

    Google Scholar 

  75. Prevost JJ, Nagothu K, Kelley B, Jamshidi M (2013) Optimal update frequency model for physical machine state change and virtual machine placement in the cloud. In: 8th International Conference on System of Systems Engineering (SoSE), 2013. IEEE, pp 159–164

  76. Abadi RMB, Rahmani AM, Alizadeh SH (2018) Self-adaptive architecture for virtual machines consolidation based on probabilistic model evaluation of data centers in Cloud computing. Clust Comput 21(3):1711–1733

    Google Scholar 

  77. Jiang J, Feng Y, Zhao J, Li K (2017) Dataabc: a fast abc based energy-efficient live vm consolidation policy with data-intensive energy evaluation model. Future Gener Comput Syst 74:132–141

    Google Scholar 

  78. Mashaly M, Kuehn PJ (2016) Modeling and analysis of virtualized multi-service cloud datacenters with automatic server consolidation and prescribed service level agreements. In: IEEE 41st Conference on Local Computer Networks Workshops (LCN Workshops), 2016. IEEE, pp 9–16

  79. Zhang S, Qian Z, Luo Z, Wu J, Lu S (2016) Burstiness-aware resource reservation for server consolidation in computing clouds. IEEE Trans Parallel Distrib Syst 27(4):964–977

    Google Scholar 

  80. Mazumdar S, Pranzo M (2017) Power efficient server consolidation for Cloud datacenter. Future Gener Comput Syst 70:4–16

    Google Scholar 

  81. Zhou Z, Hu ZG, Song T, Yu JY (2015) A novel virtual machine deployment algorithm with energy efficiency in cloud computing. J Cent South Univ 22(3):974–983

    Google Scholar 

  82. Zhu F, Li H, Lu J (2012) A service level agreement framework of cloud computing based on the Cloud Bank model. In: IEEE International Conference on Computer Science and Automation Engineering (CSAE), 2012,vol 1. IEEE, pp 255–259

  83. Dhiman G, Mihic K, Rosing T (2010) A system for online power prediction in virtualized environments using gaussian mixture models. In: Design Automation Conference (DAC), 2010 47th ACM/IEEE. IEEE, pp 807–812

  84. Rajabzadeh M, Haghighat AT (2017) Energy-aware framework with Markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud datacenters. J Supercomput 73(5):2001–2017

    Google Scholar 

  85. Wei W, Fan X, Song H, Fan X, Yang J (2016) Imperfect information dynamic stackelberg game based resource allocation using hidden Markov for cloud computing. IEEE Trans Serv Comput 11(1):78–89

    Google Scholar 

  86. Rao KS, Thilagam PS (2015) Heuristics based server consolidation with residual resource defragmentation in cloud datacenters. Future Gener Comput Syst 50:87–98

    Google Scholar 

  87. Beloglazov A, Buyya R (2013) Managing overloaded hosts for dynamic consolidation of virtual machines in cloud datacenters under quality of service constraints. IEEE Trans Parallel Distrib Syst 24(7):1366–1379

    Google Scholar 

  88. Bobroff N, Kochut A, Beaty K (2007) Dynamic placement of virtual machines for managing sla violations. In: 10th IFIP/IEEE International Symposium on Integrated Network Management, 2007. IM’07. IEEE, pp 119–128

  89. Wang Y, Wang X (2014) Performance-controlled server consolidation for virtualized datacenters with multi-tier applications. Sustain Comput Inform Syst 4(1):52–65

    MathSciNet  Google Scholar 

  90. Guenter B, Jain N, Williams C (2011) Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning. In: INFOCOM, 2011 Proceedings IEEE. IEEE, pp 1332–1340

  91. Gaggero M, Caviglione L (2016) Predictive control for energy-aware consolidation in cloud datacenters. IEEE Trans Control Syst Technol 24(2):461–474

    Google Scholar 

  92. Wang M, Meng X, Zhang L (2011) Consolidating virtual machines with dynamic bandwidth demand in datacenters. In: INFOCOM, 2011 Proceedings IEEE. IEEE, pp 71–75

  93. Farahnakian F, Ashraf A, Pahikkala T, Liljeberg P, Plosila J, Porres I, Tenhunen H (2015) Using ant colony system to consolidate vms for green cloud computing. IEEE Trans Serv Comput 8(2):187–198

    Google Scholar 

  94. 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, vol 10, pp 1–5

  95. Verma A, Ahuja P, Neogi A (2008) pMapper: power and migration cost aware application placement in virtualized systems. In: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware. Springer, New York, pp 243–264

  96. Feller E, Rilling L, Morin C (2011) Energy-aware ant colony based workload placement in clouds. In: Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing. IEEE Computer Society, pp 26–33

  97. Ferreto T, De Rose C, Heiss HU (2011) Maximum migration time guarantees in dynamic server consolidation for virtualized datacenters. In: Euro-Par 2011 Parallel Processing, pp 443–454

  98. 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 datacenters. J Netw Comput Appl 52:11–25

    Google Scholar 

  99. Li Z, Yan C, Yu X, Yu N (2017) Bayesian network-based Virtual Machines consolidation method. Future Gener Comput Syst 69:75–87

    Google Scholar 

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

    Google Scholar 

  101. Arianyan E, Taheri H, Sharifian S (2015) Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud datacenters. Comput Electr Eng 47:222–240

    Google Scholar 

  102. Li Z, Yan C, Yu L, Yu X (2018) Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method. Future Gener Comput Syst 80:139–156

    Google Scholar 

  103. Mesbahi MR, Rahmani AM, Hosseinzadeh M (2018) Reliability and high availability in cloud computing environments: a reference roadmap. Hum Centric Comput Inf Sci 8(1):20

    Google Scholar 

  104. Khazaei H, Misic J, Misic VB (2013) A fine-grained performance model of cloud computing centers. IEEE Trans Parallel Distrib Syst 24(11):2138–2147

    Google Scholar 

  105. Khazaei H, Mišić J, Mišić VB (2010) Performance analysis of cloud computing centers. In: International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness. Springer, Berlin, pp 251–264

  106. Hosseinimotlagh S, Khunjush F (2014) Migration-less energy-aware task scheduling policies in cloud environments. In: 28th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2014. IEEE, pp 391–397

  107. Moon Y, Yu H, Gil JM, Lim J (2017) A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments. Hum Centric Comput Inf Sci 7(1):28

    Google Scholar 

  108. Zhang Y, Chen L, Shen H, Cheng X (2016) An energy-efficient task scheduling heuristic algorithm without virtual machine migration in real-time cloud environments. In: International Conference on Network and System Security. Springer International Publishing, pp 80–97

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Authors and Affiliations

  1. Qazvin Branch, Faculty of Computer and Information Technology Engineering, Islamic Azad University, Qazvin, Iran

    Reza Mohamadi Bahram Abadi

  2. Computer Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Amir Masoud Rahmani

  3. Department of Information Technology, ICT Research Institute (Iran Telecommunication Research Center), Tehran, Iran

    Sasan Hossein Alizadeh

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  1. Reza Mohamadi Bahram Abadi
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  3. Sasan Hossein Alizadeh
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The original version of this article was revised: The wording of Sasan Hossein Alizadeh’s name was incorrect.

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Abadi, R.M.B., Rahmani, A.M. & Alizadeh, S.H. Challenges of server consolidation in virtualized data centers and open research issues: a systematic literature review. J Supercomput 76, 2876–2927 (2020). https://doi.org/10.1007/s11227-019-03068-1

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