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The Journal of Supercomputing

Erschienen in:

29.03.2021

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

verfasst von: Nisha Chaurasia, Mohit Kumar, Rashmi Chaudhry, Om Prakash Verma

Erschienen in: The Journal of Supercomputing | Ausgabe 10/2021

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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.

Vorheriger Artikel Reliability-aware task scheduling for energy efficiency on heterogeneous multiprocessor systems

Nächster Artikel Epidemic zone of COVID-19 from social media using hypergraph with weighting factor (HWF)

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

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

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

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

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

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

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

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

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

10.

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

11.

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

12.

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

13.

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

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

16.

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

17.

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

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

19.

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

20.

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

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

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

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 60:552–564MathSciNetMATH

28.

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

29.

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

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

32.

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

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

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

39.

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

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

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

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

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 26:344–353MathSciNet

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

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

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

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

56.

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

57.

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

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

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

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

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

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

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

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 63:3012–3025MathSciNetMATH

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

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.2021.101722CrossRef

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

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

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

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

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

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

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

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

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

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

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

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 2016:1–15MathSciNet

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

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

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

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

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

103.

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

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

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

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 79:1230–1242MathSciNetMATH

108.

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

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

112.

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

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

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

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

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

122.

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

123.

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

Titel: Comprehensive survey on energy-aware server consolidation techniques in cloud computing
verfasst von: Nisha Chaurasia
Mohit Kumar
Rashmi Chaudhry
Om Prakash Verma
Publikationsdatum: 29.03.2021
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 10/2021
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-021-03760-1

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