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Comprehensive survey on energy-aware server consolidation techniques in cloud computing

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Abstract

The objective of cloud computing is to provide seamless services using virtualization technology over the Internet to serve the Quality of Service (QoS)-driven end users requirements. In order to provide various services like Software as a Service, Platform as a Service, Infrastructure as a Service, the cloud server’s datacenters are kept active, which have large electrical consumption. Due to improper utilization of resources, optimizing the servers energy consumption becomes a significant challenge for service vendors from environmental and economic perspectives as well. The challenge to provide services with a low energy consumption profile opens up a new dimension of optimized server use for intelligent management of resources (such as CPU/disk/memory), with reduced power consumption through server consolidation. This enables fewer active physical servers to provide the required services without compromising the QoS. This article presents a narrative recent advancement comprehensive as well as systematic survey of existing energy-efficient techniques along with their limitations and the challenges associated in implementing them.

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References

  1. Kumar M, Sharma SC (2018) Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment. Comput Electr Eng 69:395–411

    Google Scholar 

  2. Kumar M, Dubey K, Sharma SC (2018) Elastic and flexible deadline constraint load balancing algorithm for cloud computing. Procedia Comput Sci 125:717–724

    Google Scholar 

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

    Google Scholar 

  4. Ho Y, Liu P, Wu J-J (2011) Server consolidation algorithms with bounded migration cost and performance guarantees in cloud computing. In: Proceedings of 4th International Conference on Utility and Cloud Computing. IEEE, pp 154–161

  5. Thakur A, Goraya MS (2017) A taxonomic survey on load balancing in cloud. J Netw Comput Appl 98:43–57

    Google Scholar 

  6. 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 

  7. Mishra SK, Sahoo B, Parida PP (2020) Load balancing in cloud computing: a big picture. J King Saud Univ Comput Inf Sci 32(2):149–158

    Google Scholar 

  8. Ghomi EJ, Rahmani AM, Qader NN (2017) Load-balancing algorithms in cloud computing: a survey. J Netw Comput Appl 88:50–71

    Google Scholar 

  9. Smanchat S, Viriyapant K (2015) Taxonomies of workflow scheduling problem and techniques in the cloud. Futur Gener Comput Syst 52:1–12

    Google Scholar 

  10. Kalra M, Singh S (2015) A review of metaheuristic scheduling techniques in cloud computing. Egypt Inform J 16(3):275–295

    Google Scholar 

  11. Singh S, Chana I (2016) Cloud resource provisioning: survey, status and future research directions. Knowl Inf Syst 49(3):1005–1069

    Google Scholar 

  12. Masdari M et al (2017) A survey of PSO-based scheduling algorithms in cloud computing. J Netw Syst Manage 25(1):122–158

    Google Scholar 

  13. Masdari M et al (2016) Towards workflow scheduling in cloud computing: a comprehensive analysis. J Netw Comput Appl 66:64–82

    Google Scholar 

  14. Dutta M, Aggarwal N (2016) Meta-heuristics based approach for workflow scheduling in cloud computing: a survey. In: Artificial intelligence and evolutionary computations in engineering systems. Springer, New Delhi, 1331–1345

  15. Arunarani AR, Manjula D, Sugumaran V (2019) Task scheduling techniques in cloud computing: a literature survey. Futur Gener Comput Syst 91:407–415

    Google Scholar 

  16. Kumar M et al (2019) A comprehensive survey for scheduling techniques in cloud computing. J Netw Comput App 143:1–33

    Google Scholar 

  17. Endo PT et al (2011) Resource allocation for distributed cloud: concepts and research challenges. IEEE Netws 25(4):42–46

    Google Scholar 

  18. Madni SH, Hussain MS, Latiff A, Coulibaly Y (2017) Recent advancements in resource allocation techniques for cloud computing environment: a systematic review. Clust Comput 20(3):2489–2533

    Google Scholar 

  19. Singh S, Chana I (2016) A survey on resource scheduling in cloud computing: issues and challenges. J Grid Comput 14(2):217–264

    Google Scholar 

  20. Ghanbari Z et al (2019) Resource allocation mechanisms and approaches on the internet of things. Clust Comput 22(4):1253–1282

    Google Scholar 

  21. Li, J., Shuang, K., Su, S., Huang, Q., Xu, P., Cheng, X., and Wang, J. (2012) Reducing operational costs through consolidation with resource prediction in the cloud. In: Proceedings of 12th International Symposium on Cluster, Cloud and Grid Computing. IEEE/ACM, pp 793–798

  22. Tian W et al (2018) On minimizing total energy consumption in the scheduling of virtual machine reservations. J Netw Comput Appl 113:64–74

    Google Scholar 

  23. Ngenzi A, Nair SR (2015) Dynamic resource management in cloud datacenters for server consolidation. In: Distributed, parallel, and cluster computing, May, 1–8

  24. Srikantaiah S, Kansal A, Zhao F (2009) Energy aware consolidation for cloud computing. ACM J Clust Comput 12:1–15

    Google Scholar 

  25. Nwe KM, Oo MK, Htay MM (2018) Efficient resource management for virtual machine allocation in cloud data centers. In: 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE). IEEE

  26. Beloglazov A, Buyya R (2010) Energy efficient allocation of virtual machines in cloud data centers. In: 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. IEEE

  27. Jang J-W, Jeon M, Kim H-S, Jo H, Kim J-S, Maeng S (2011) Energy reduction in consolidated servers through memory-aware virtual machine scheduling. IEEE Trans Comput 60:552–564

    MathSciNet  MATH  Google Scholar 

  28. Hwang I, Pedram M (2016) Hierarchical, portfolio theory-based virtual machine consolidation in a compute cloud. IEEE Trans Serv Comput 11:63–77

    Google Scholar 

  29. Abohamama AS, Hamoud E (2020) A hybrid energy—aware virtual machine placement algorithm for cloud environments. Expert Syst Appl 150:113306

    Google Scholar 

  30. Gong Z, Gu X (2010) PAC: pattern-driven application consolidation for efficient cloud computing. In: Proceedings of International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems. IEEE/ACM, pp 24–33

  31. Nevithitha S, Sriram VS (2013) Consolidated batch and transactional workloads using dependency structure prioritization. Int J Eng Technol 5:1328–1334

    Google Scholar 

  32. Sekhar J, Jeba G (2013) Energy efficient VM live migration in cloud data centers. Int J Comput Sci Netw 2:71–75

    Google Scholar 

  33. Sarvabhatla M et al (2017) A dynamic and energy efficient greedy scheduling algorithm for cloud data centers. In: 2017 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE

  34. Zuo XD, and Jia, H.-M. (2013) An energy saving heuristic algorithm based on consolidation of virtual machines. In: Proceedings of International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, pp 1578–1583

  35. Gai K et al (2016) Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. J Netw Comput Appl 59:46–54

    Google Scholar 

  36. Usman MJ et al (2017) Energy-efficient virtual machine allocation technique using interior search algorithm for cloud datacenter. In: 2017 6th ICT International Student Project Conference (ICT-ISPC). IEEE

  37. Guo P, Ming L, Zhi X (2018) A PSO-based energy-efficient fault-tolerant static scheduling algorithm for real-time tasks in clouds. In: 2018 IEEE 4th International Conference on Computer and Communications (ICCC). IEEE

  38. Sharma M, Ritu G (2020) An artificial neural network based approach for energy efficient task scheduling in cloud data centers. Sustain Comput Inform Syst 26:100373

    Google Scholar 

  39. Lee YC, Zomaya AY (2012) Energy efficient utilization of resources in cloud computing systems. J Supercomput 60:268–280

    Google Scholar 

  40. Singh A, Hemalatha M (2013) Cluster based bee algorithm for virtual machine placement in cloud data center. J Theor Appl Inf Technol 57:1–10

    Google Scholar 

  41. Wang Y, Wang X (2013) Virtual batching: request batching for server energy conservation in virtualized data centers. IEEE Trans Parallel Distrib Syst 24:1695–1705

    Google Scholar 

  42. Gao Y, Guan H, Qi Z, Song T, Huan F, Liu L (2014) Service level agreement based energy-efficient resource management in cloud data centers. J Comput Electr Eng 40:1621–1633

    Google Scholar 

  43. Razavi R, Rajabi A, Faragardi HR, Pourashraf T, Yazdani N (2014) Energy-efficient scheduling of real-time cloud services using task consolidation and dynamic voltage scaling. In: Proceedings of 7th International Symposium on Telecommunications (IST’2014). IEEE, pp 675–682

  44. Xiao X et al (2018) Maximizing reliability of energy constrained parallel applications on heterogeneous distributed systems. J Comput Sci 26:344–353

    MathSciNet  Google Scholar 

  45. Safari M, Khorsand R (2018) Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment. Simul Model Pract Theory 87:311–326

    Google Scholar 

  46. Niyato D, Chaisiri S, Sung LB (2009) Optimal power management for server farm to support green computing. In: Proceedings of 9th International Symposium on Cluster Computing and the Grid. IEEE/ACM, pp 84–91

  47. Lee S, Sahu S (2011) Efficient server consolidation intra-cluster traffic. In: Proceedings of Global Telecommunications Conference (GLOBECOM 2011). IEEE, pp 1–6

  48. Ribas BC, Suguimoto RM, Montana RA, Silva F, de Bona L, Castilho M (2012) On modelling virtual machine consolidation to pseudo-boolean constraints. In: AIBERAMIA 2012, Lecture Notes in Artificial Intelligence, vol 7637. Springer, pp 361–370

  49. Huang Z, Tsang DH, She J (2012) A virtual machine consolidation framework for mapreduce enabled computing clouds. In: Proceedings of 24th International Teletraffic Congress (ITC). ACM, pp 1–8

  50. Padmavathi S, Rajeshwari P, Pradheeba P, Mythili R (2012) Achieving cost efficiency using CaaS model in the cloud. In: Proceedings of 4th International Conference on Advanced Computing (ICoAC). IEEE, pp 1–5

  51. Liu X, Wang C, Zhou BB, Chen J, Yang T, Zomaya AY (2013) Priority-based consolidation of parallel workload in the cloud. IEEE Trans Parallel Distrib Syst 24:1874–1883

    Google Scholar 

  52. S, V., P, S., and P, S. (2014) Effective management of re-source allocation and provisioning cost using virtualization in cloud. In: Proceedings of IEEE International Conference on Advanced Communication Control and Computing Technologies (lCACCCT), pp 1726–1731

  53. Thanavanich T (2018) Energy-aware and Performance-aware of workflow application with hybrid scheduling algorithm on cloud computing. In: 2018 22nd International Computer Science and Engineering Conference (ICSEC). IEEE

  54. Chen H et al (2018) ERECT: energy-efficient reactive scheduling for real-time tasks in heterogeneous virtualized clouds. J Comput Sci 28:416–425

    Google Scholar 

  55. Thi MT, Pierson JM, Da Costa G, Stolf P, Nicod JM, Rostirolla G, Haddad M (2020) Negotiation game for joint IT and energy management in green datacenters. Future Gener Comput Syst 110:1116–1138

    Google Scholar 

  56. Ding D et al (2020) Q-learning based dynamic task scheduling for energy-efficient cloud computing. Future Gener Comput Syst 108:361–371

    Google Scholar 

  57. Feng H, Deng Y, Li J (2021) A global-energy-aware virtual machine placement strategy for cloud datacenters. J Syst Archit 116:102048

    Google Scholar 

  58. Wang Z, Chen Y, Gmach D, Singhal S, Watson BJ (2009) AppRAISE: application-level performance management in virtualized server environments. IEEE Trans Netw Serv Manage 6:240–254

    Google Scholar 

  59. Beloglazov A, Buyya R (2010) Adaptive threshold- based approach for energy-efficient consolidation of virtual machines in cloud data centers. In: Proceedings of 8th International Workshop on Middleware for Grids, Clouds and e-Science Article No. 4. ACM

  60. 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 24:1397–1420

    Google Scholar 

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

    Google Scholar 

  62. Khanna G, Beaty K, Kar G, Kochut A (2006) Application performance management in virtualized server environments. In: Proceedings of 10th Network Operations and Management Symposium (NOMS). IEEE/IFIP, pp 373–381

  63. Ye K, Jaing X, Huang D, Chen J, Wang B (2011) Live migration of multiple virtual machines with resource reservation in cloud computing environments. In: Proceedings of International Conference on Cloud Computing. IEEE, pp 267–274

  64. Ferreto TC, Netto MA, Calheiros RN, Rose CAD (2011) Server consolidation with migration control for virtualized data centers. J Future Gener Comput Syst 27:1027–1034

    Google Scholar 

  65. Gutierrez-Garcia JO, Ramirez-Nafarrate A (2013) Policy-based agents for virtual machine migration in cloud data centers. In: Proceedings of 10th International Conference on Services Computing. IEEE, pp 603–610

  66. Zheng J, Ng TSE, Sripanidkulchai K, Liu Z (2013) Pacer: a progress management system for live virtual machine migration in cloud computing. IEEE Trans Netw Serv Manage 10:369–382

    Google Scholar 

  67. Liu H, Jin H, Liao X, Hu L, Yu C (2009) Live migration of virtual machine based on full system trace and replay. In: Proceedings of 18th Symposium on High- Performance Parallel and Distributed Computing (HIPC09). ACM, pp 101–110

  68. Liu H, Jin H, Liao X, Yu C, Xu C-Z (2011) Live virtual machine migration via asynchronous replication and state synchronization. IEEE Trans Parallel Distrib Syst 22:1986–1999

    Google Scholar 

  69. Sahu Y, Pateriya R, Gupta RK (2013) Cloud server optimization with load balancing and green computing techniques using dynamic compare and balance algorithm. In: Proceedings of 5th International Conference on Computational Intelligence and Communication Networks. IEEE, pp 527–531

  70. Xu F, Liu F, Liu L, Hai Jin BL, Li B (2014) iAware: making live migration of virtual machines interference-aware in the cloud. IEEE Trans Comput 63:3012–3025

    MathSciNet  MATH  Google Scholar 

  71. Gholipour N, Arianyan E, Buyya R (2020) A novel energy-aware resource management technique using joint VM and container consolidation approach for green computing in cloud data centers. Simul Model Pract Theory 104:102–127

    Google Scholar 

  72. Shaw R, Howley E, Barrett E (2021) Applying reinforcement learning towards automating energy efficient virtual machine consolidation in cloud data centers. Inf Syst. https://doi.org/10.1016/j.is.2021.101722

    Article  Google Scholar 

  73. Abdulgafer AR, Marimuthu PN, Habib SJ (2009) Network redesign through servers consolidations. In: Proceedings of 11th International Conference on Information Integration and Web-based Applications and Services (ii- WAS2009). ACM, pp 623–627

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

  75. Hsu C-H, Slagter KD, Chen S-C, Chung Y-C (2014) Optimizing energy consumption with task consolidation in clouds. J Inf Sci 258:452–462

    Google Scholar 

  76. Li D, Wu J, Liu Z, Zhang F (2014) Joint power optimization through VM placement and flow scheduling in data centers. In: Proceedings of International Conference on Performance Computing and Communications. IEEE, pp 1–8

  77. Soni SK, Kapoor RK (2013) Enhanced live migration of virtual machine using comparison of modified and unmodified pages. Int J Comput Sci Mob Comput 3:779–787

    Google Scholar 

  78. Yu B, Han Y, Yuan H, Zhou X, Xu Z (2015) A cost-effective scheme supporting adaptive service migration in cloud data center. Front Comput Sci 9:875–886

    Google Scholar 

  79. Shen D, Luo J, Dong F, Fei X, Wang W, Jin G, Li W (2015) Stochastic modeling of dynamic right-sizing for energy-efficiency in cloud data centers. J Future Gener Comput Syst 48:82–95

    Google Scholar 

  80. Esfandiarpoor S, Pahlavan A, Goudarzi M (2015) Structure-aware online virtual machine consolidation for datacenter energy improvement in cloud computing. J Comput Electr Eng 42:74–89

    Google Scholar 

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

  82. 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 

  83. Wu X, Zeng Y, Lin G (2017) An energy efficient VM migration algorithm in data centers. In: 2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES). IEEE

  84. Tziritas N et al (2018) A communication-aware energy-efficient graph-coloring algorithm for VM placement in clouds. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation. IEEE

  85. Abohamama AS, Hamouda E (2020) A hybrid energy–aware virtual machine placement algorithm for cloud environments. Expert Syst Appl 150:113306

    Google Scholar 

  86. Speitkamp B, Bichler M (2010) A mathematical programming approach for server consolidation problem in virtualized data centers. IEEE Trans Serv Comput 3:266–278

    Google Scholar 

  87. Aziz M, Oh S (2011) Sercon: server consolidation algorithm using live migration of virtual machines for green computing. IETE Tech Rev 287:212–231

    Google Scholar 

  88. Liu H, Jin H, Xu C-Z, Liao X (2011) Performance and energy modeling for live migration of virtual machines. In: Proceedings of 20th Symposium on High- Performance Parallel and Distributed Computing (HIPC11). ACM, pp 171–181

  89. Liu H, Jin H, Xu C-Z, Liao X (2013) Performance and energy modeling for live migration of virtual machines. Clust Comput 16:249–264

    Google Scholar 

  90. Rybina K, Dargie W, Strunk A, Schill A (2013) Investigation into the energy cost of live migration of virtual machines. In: Proceedings of 3rd Conference on Sustainable Internet and ICT for Sustainability (SustainIT). IEEE/IFIP, pp 1–8

  91. Corradi A, Fanelli M, Foschini L (2014) VM consolidation: a real case based on openstack cloud. J Future Gener Comput Syst 32:118–127

    Google Scholar 

  92. Rybina K, Patni A, Schill A (2014) Analysing the migration time of live migration of multiple virtual machines. In: Proceedings of 4th International Conference on Cloud Computing and Services Science. ACM, pp 590–597

  93. Perumal B, Murugaiyan A (2016) A firefly colony and its fuzzy approach for server consolidation and virtual machine placement in cloud datacenters. J Adv Fuzzy Syst 2016:1–15

    MathSciNet  Google Scholar 

  94. 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: Proceedings of 4th International Conference on Cloud Computing Technology and Science. IEEE, pp 18–25

  95. Ye K, Wu Z, Wang C, Zhou BB, Si W, Jiang X, Zomaya AY (2015) Profiling-based workload consolidation and migration in virtualized data centers. IEEE Trans Parallel Distrib Syst 26:878–890

    Google Scholar 

  96. Pham C, Tran NH, Do CT, Huh E-N, Hong CS (2016) Joint consolidation and service-aware load balancing for data centers. IEEE Commun Lett 20:292–295

    Google Scholar 

  97. Maezolla M, Babaoglu O, Panzieri F (2011) Server consolidation in clouds through gossiping. In: Proceedings of International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). IEEE, pp 1–6

  98. Pop CB, Anghel I, Cioara T, Solemie I, Vartic I (2012) A swarm-inspired data center consolidation methodology. In: Proceedings of 2nd International Conference on Web Intelligence, Mining and Semantics Article No. 41. ACM

  99. Rao KS, Thilagam PS (2015) Heuristics based server consolidation with residual resource. J Future Gener Comput Syst 50:87–98

    Google Scholar 

  100. Lin C-C, Liu P, Wu J-J (2011) Energy-efficient virtual machine provision algorithms for cloud systems. In: Proceedings of 4th International Conference on Utility and Cloud Computing. IEEE, pp 81–88

  101. Hongyou L, Jiangyong W, Jian P, Junfeng W, Tang L (2013) Energy-aware scheduling scheme using workload-aware consolidation technique in cloud data centres. IEEE Commun Softw 10:114–124

    Google Scholar 

  102. 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 27:964–997

    Google Scholar 

  103. Xu H et al (2019) Minimizing energy consumption with reliability goal on heterogeneous embedded systems. J Parallel Distrib Comput 127:44–57

    Google Scholar 

  104. Mc-Donnell N, Howley E, Duggan J (2020) Dynamic virtual machine consolidation using a multi-agent system to optimise energy efficiency in cloud computing. Future Gener Comput Syst 108:288–301

    Google Scholar 

  105. Khan AA, Zakarya M, Khan R, Rahman IU, Khan M, Khan AR (2020) An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters. J Netw Comput Appl 150:102497

    Google Scholar 

  106. Gao Y, Wang Y, Gupta SK, Pedram M (2013) An energy and deadline aware resource provisioning scheduling and optimization framework for cloud systems. In: Proceedings of Hardware/Software Codesign and System Synthesis (CODES+ISSS). IEEE, pp 1–10

  107. Gao Y, Guan H, Qi Z, Houb Y, Liu L (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J Comput Syst Sci 79:1230–1242

    MathSciNet  MATH  Google Scholar 

  108. Khan AA et al (2020) An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters. J Netw Comput Appl 150:102497

    Google Scholar 

  109. Jung G, Joshi KR, Hiltunen MA, Schlichting RD, Pu C (2009) A cost-sensitive adaptation engine for server consolidation of multitier applications. In: ACM/IFIP/USENIX ICDSPODP, Lecture Notes in Computer Science, vol 5896. pp 163–183

  110. Zhang R, Routray R, Eyers DM, Chambliss D, Sarkar P, Willcocks D, Pietzuch P (2011) IO Tetris: deep storage consolidation for the cloud via fine-grained workload analysis. In: Proceedings of 4th International Conference on Cloud Computing. IEEE, pp 700–707

  111. Xia Y, Zhou MC, Luo X, Zhu Q, Li J, Huang Y (2015) Stochastic modeling and quality evaluation of infrastructure-as-a-service clouds. IEEE Trans Autom Eng 12:162–170

    Google Scholar 

  112. Fu X, Zhou C (2015) Virtual machine selection and placement for dynamic consolidation in cloud computing environment. Front Comput Sci 9:322–330

    MathSciNet  Google Scholar 

  113. Hieu NT, Di-Francesco M, Yia-Jaaski A (2020) Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans Serv Comput 13(1):186–199

    Google Scholar 

  114. Hsieh S-Y, Liu C-S, Buyya R, Zomaya AY (2020) Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers. J Parallel Distrib Comput 139:99–109

    Google Scholar 

  115. Goudarzi H, Ghasemazar M, Pedram M (2012) SLA-based optimization of power and migration cost in cloud computing. In: Proceedings of 12th International Symposium on Cluster, Cloud and Grid Computing. IEEE/ACM, pp 172–179

  116. Janpan T, Visoottiviseth V, Takano R (2014) A virtual machine consolidation framework for CloudStack platforms. In: Proceedings of International Conference on Information Networking (ICOIN 2014). IEEE, pp 28–33

  117. He L, Zou D, Zhang Z, Chen C, Jin H, Jarvis SA (2014) Developing resource consolidation frameworks for moldable virtual machines in clouds. J Future Gener Comput Syst 32:69–81

    Google Scholar 

  118. Sharma O, Saini H (2016) VM consolidation for cloud data center using median based threshold approach. In: Proceedings of 12th International Multi-Conference on Information Processing-2016 (IMCIP-2016). Elsevier, pp 27–33

  119. Chilipirea C et al (2016) A comparison of private cloud systems. In: 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE

  120. Kumar M, Sharma SC (2019) PSO-based novel resource scheduling technique to improve QoS parameters in cloud computing. Neural Comput Appl 1–24

  121. Kumar M, Sharma SC (2018) PSO-COGENT: cost and energy efficient scheduling in cloud environment with deadline constraint. Sustain Comput Inform Syst 19:147–164

    Google Scholar 

  122. Sindhu HS (2014) Comparative analysis of scheduling algorithms of Cloudsim in cloud computing. Int J Comput Appl 97(16):8887

    Google Scholar 

  123. Zhou Q et al (2020) Energy efficient algorithms based on VM consolidation for cloud computing: comparisons and evaluations. arXiv:2002.04860

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Chaurasia, N., Kumar, M., Chaudhry, R. et al. Comprehensive survey on energy-aware server consolidation techniques in cloud computing. J Supercomput 77, 11682–11737 (2021). https://doi.org/10.1007/s11227-021-03760-1

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