a systematic literature review on virtual machine consolidation

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

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

2019, The Journal of Supercomputing

Related Papers

Hesham Eldeeb

Adnan Ashraf

Background: Virtual Machine (VM) consolidation is an effective technique to improve resource utilization and reduce energy footprint in cloud data centers. It can be implemented in a centralized or a distributed fashion. Distributed VM consolidation approaches are currently gaining popularity because they are often more scalable than their centralized counterparts and they avoid a single point of failure. Objective: To present a comprehensive, unbiased overview of the state-of-the-art on distributed VM consolidation approaches. Method: A Systematic Mapping Study (SMS) of the existing distributed VM consolidation approaches. Results: 19 papers on distributed VM consolidation categorized in a variety of ways. The results show that the existing distributed VM consolidation approaches use four types of algorithms, optimize a number of different objectives, and are often evaluated with experiments involving simulations. Conclusion: There is currently an increasing amount of interest on developing and evaluating novel distributed VM consolidation approaches. A number of research gaps exist where the focus of future research may be directed.

Sangmin Lee

This paper examines two fundamental issues pertaining to virtual machines (VM) consolidation. Current virtualization management tools, both commercial and academic, enable multiple virtual machines to be consolidated into few servers so that other servers can be turned off, saving power. These tools determine effective strategies for VM placement with the help of clever optimization algorithms, relying on two inputs: a model of resource utilization vs performance tradeoff when multiple VMs are hosted together and estimates of resource requirements for each VM in terms of CPU, network and storage. This paper investigates the following key questions: What factors govern the performance model that drives VM placement , and how do competing resource demands in multiple dimensions affect VM consolidation? It establishes a few basic insights about these questions through a combination of experiments and empirical analysis. This experimental study points out potential pitfalls in the use of current VM management tools and identifies promising opportunities for more effective performance consolidation algorithms. In addition to providing valuable guidance to practitioners , we believe this paper will serve as a starting point for research into next-generation virtualization platforms and tools.

The Journal of Supercomputing

Belén Bermejo

Mohammad Firoj Mithani

How do the existing data centers operate in a high capacity environment? Are they adequately equipped to handle peak hour loads? Are these data centers plagued with low average server utilization? If yes, then the issue might be in providing applications for peak loads and the concept of having dedicated server for each application. This results in situations that are termed as server sprawls [1]. Virtualization technologies, however, provide mechanism for enabling multi-tenancy within next generation data centers.

2012 13th Symposium on Computer Systems

Xiaoyun Zhu , Sharad Singhal

Journal of Petroleum Technology

Bill Bartling

Future Generation Computer Systems

Luca Foschini

2015 International Conference on High Performance Computing & Simulation (HPCS)

Jesús A Omaña Iglesias

Carlos Juiz

Server consolidation is one of the most commonly used techniques for reducing energy consumption in datacenters; however, this results in inherent performance degradation due to the coallocation of virtual servers, i.e., virtual machines (VMs) and containers, in physical ones. Given the widespread use of containers and their combination with VMs, it is necessary to quantify the performance degradation in these new consolidation scenarios, as this information will help system administrators make decisions based on server performance management. In this paper, a general method for quantifying performance degradation, that is, server overhead, is proposed for arbitrary consolidation scenarios. To demonstrate the applicability of the method, we develop a set of experiments with varying combinations of VMs, containers, and workload demands. From the results, we can obtain a suitable method for quantifying performance degradation that can be implemented as a recursive algorithm. From the ...

RELATED PAPERS

Aysha Ferdoushi

Christian Gilot

Journal of Tropical …

Migracijske i etničke teme / Migration and Ethnic Themes

Sanja Lazanin

Anais Do Cbmfc

Simone Portella Teixeira de Mello

Journal of Systems and Software

Irene Mavrommati

Eliete Araujo

Oblicza Komunikacji

Małgorzata Dawidziak-Kładoczna

Jurnal Perspektif Pembiayaan dan Pembangunan Daerah

Siti Syuhada

2017 7th International Annual Engineering Seminar (InAES)

hadi saputra

Ziyadul Hunaifi

Biodiversity and Conservation - BIODIVERS CONSERV

Carlos Gómez Hinostrosa

Gastrointestinal Endoscopy

Animesh Mishra

Infection Control & Hospital Epidemiology

Moi Lin Ling

shiva bakhtiari

Teaching and Teacher Education

Sangmee Kim

SEMNASTEKNOMEDIA ONLINE

Lailil Muflikhah

hilda gonzales

International Journal of Swarm Intelligence and Evolutionary Computation

Dipti Yadav

Góndola, Enseñanza y Aprendizaje de las Ciencias

Juan Esteban Abril

RePEc: Research Papers in Economics

Eduard Nezinsky

Ikbal maulana

European Journal of Pharmacology

Guglielmina Froldi

Dwi Cahyaningdyah

aziz deraman

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

A Comprehensive Review of Cloud Computing Virtual Machine Consolidation

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

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

• Published: 14 November 2019
• Volume 76 , pages 2876–2927, ( 2020 )

Cite this article

• Reza Mohamadi Bahram Abadi 1 ,
• Amir Masoud Rahmani 2 &
• Sasan Hossein Alizadeh 3  

641 Accesses

4 Citations

Explore all metrics

A Correction to this article was published on 16 December 2019

This article has been updated

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

A Survey of Energy-Aware Server Consolidation in Cloud Computing

Dynamic Virtual Machine Consolidation Algorithms for Energy-Efficient Cloud Resource Management: A Review

The $$CiS^2$$ : a new metric for performance and energy trade-off in consolidated servers

Change history, 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.

http://dl.acm.org .

http://ieeexplore.ieee.org .

http://www.sciencedirect.com .

https://link.springer.com .

https://onlinelibrary.wiley.com/ .

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  

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

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)

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

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  

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

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

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

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

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

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

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  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Download references

Author information

Authors and affiliations.

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

Reza Mohamadi Bahram Abadi

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

Amir Masoud Rahmani

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

Sasan Hossein Alizadeh

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Reza Mohamadi Bahram Abadi .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original version of this article was revised: The wording of Sasan Hossein Alizadeh’s name was incorrect.

Rights and permissions

Reprints and permissions

About this article

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

Download citation

Published : 14 November 2019

Issue Date : April 2020

DOI : https://doi.org/10.1007/s11227-019-03068-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Virtualization
• Server consolidation
• Data center
• Virtual machine
• Find a journal
• Publish with us
• Track your research