Skip to main content

A metaheuristic virtual machine placement framework toward power efficiency of sustainable cloud environment


The primary aim of Virtual Machine Placement (VMP) is the mapping of Virtual Machines (VMs) to Physical Machines (PMs), such that the PMs may be utilized to their maximum efficiency, where the already active VMs are not to be interrupted. It provides a list of live VM migrations that must be accomplished to get the optimum solution and reduces energy consumption significantly. The inefficient VMP leads to wastage of resources and excessive energy consumption and increases the overall operational cost of the data center. A Metaheuristic Virtual Machine Placement Framework towards the Power Efficiency of Sustainable Cloud Environment (MV-PESC) approach is suggested to address the issues mentioned above. An Extended Flower Pollination Optimization algorithm is suggested, which combines the concept of the Random Fit algorithm and the Flower Pollination Optimization algorithm. The proposed work’s performance is evaluated using actual workload traces of the benchmark Google Cluster Data set. The obtained results are compared with various state-of-the-art and demonstrate a notable reduction in power consumption, the number of active PMs, and execution time up to 64.89%, 35%, and 21.12%, respectively.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Data Availability

The data are available upon reasonable request to the corresponding authors.


  • Abdel-Basset M, Abdle-Fatah L, Sangaiah AK (2019) An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Cluster Comput 22(4):8319–8334

    Article  Google Scholar 

  • Al-Dulaimy A, Itani W, Zantout R, Zekri A (2018) Type-aware virtual machine management for energy efficient cloud data centers. Sustain Comput Inf Syst 19:185–203

  • Alsadie D (2022) Virtual machine placement methods using metaheuristic algorithms in a cloud environment—a comprehensive review. Int J Comput Sci Netw Secur 22(4):147–158

    Google Scholar 

  • Amazon: Amazon EC2 instances (1999). Accessed 19 Jan 2022

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

    Article  Google Scholar 

  • Beloglazov A, Buyya R (2012) Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans Parallel Distrib Syst 24(7):1366–1379

    Article  Google Scholar 

  • Bianchini R, Rajamony R (2004) Power and energy management for server systems. Computer 37(11):68–76

    Article  Google Scholar 

  • Brey T, Lamers L (2009) Using virtualization to improve data center efficiency. The green grid, whitepaper, vol 19

  • Costa CM, Leite CRM, Sousa AL (2015) Service response time measurement model of service level agreements in cloud environment. In: 2015 IEEE international conference on smart city/SocialCom/SustainCom (SmartCity), pp 969–974.

  • Dabbagh M, Hamdaoui B, Guizani M, Rayes A (2016) An energy-efficient VM prediction and migration framework for overcommitted clouds. IEEE Trans Cloud Comput 6(4):955–966

    Article  Google Scholar 

  • Farzai S, Shirvani MH, Rabbani M (2020) Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters. Sustain Comput Inf Syst 28:100374

    Google Scholar 

  • Garg SK, Buyya R (2012) Green cloud computing and environmental sustainability. Harnessing Green IT Princ Pract 2012:315–340

    Google Scholar 

  • Garg N, Singh D, Goraya MS (2018) Power and resource-aware VM placement in cloud environment. In: 2018 IEEE 8th international advance computing conference (IACC). IEEE, pp. 113–118

  • Goudarzi H, Pedram M (2012) Energy-efficient virtual machine replication and placement in a cloud computing system. In: 2012 IEEE fifth international conference on cloud computing. IEEE, pp 750–757

  • Han J, Zang W, Chen S, Yu M (2017) Reducing security risks of clouds through virtual machine placement. In: IFIP annual conference on data and applications security and privacy. Springer, pp 275–292

  • Hosseini Shirvani M (2021) Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm. J Exp Theor Artif Intell 33(2):179–202

    Article  Google Scholar 

  • Jafari V, Rezvani M.H (2021) Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II metaheuristic algorithm. J Ambient Intell Hum Comput 1–24 (2021)

  • Jangiti S, Sri Ram E, Shankar Sriram V (2019) Aggregated rank in first-fit-decreasing for green cloud computing. In: Cognitive informatics and soft computing. Springer, Singapore, pp 545–555

  • Jung G, Hiltunen MA, Joshi KR, Schlichting RD, Pu C (2010) Mistral: dynamically managing power, performance, and adaptation cost in cloud infrastructures. In: 2010 IEEE 30th international conference on distributed computing systems. IEEE, pp 62–73

  • Minas L, Ellison B (2009) Energy efficiency for information technology: how to reduce power consumption in servers and data centers. Intel Press

    Google Scholar 

  • Mokaripoor P, Hosseini Shirvani M (2016) A state of the art survey on DVFS techniques in cloud computing environment. J Multidiscip Eng Sci Technol 3(5):4740–4743

    Google Scholar 

  • Nathuji R, Schwan K (2007) Virtualpower: coordinated power management in virtualized enterprise systems. ACM SIGOPS Oper Syst Rev 41(6):265–278

    Article  Google Scholar 

  • Ramzanpoor Y, Hosseini Shirvani M, Golsorkhtabaramiri M (2022) Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure. Complex Intell Syst 8(1):361–392

    Article  Google Scholar 

  • Reddy MA, Ravindranath K (2020) Virtual machine placement using JAYA optimization algorithm. Appl Artif Intell 34(1):31–46

    Article  Google Scholar 

  • Reiss C, Wilkes J, Hellerstein JL (2011) Google cluster-usage traces: format + schema. Google Inc., White Paper, 1

  • Saeedi P, Hosseini Shirvani M (2021) An improved thermodynamic simulated annealing-based approach for resource-skewness-aware and power-efficient virtual machine consolidation in cloud datacenters. Soft Comput 25(7):5233–5260

    Article  Google Scholar 

  • Saxena D (2022) A high availability management model based on VM significance ranking and resource estimation for cloud applications. IEEE Trans Serv Comput.

    Article  Google Scholar 

  • Saxena D, Singh AK (2021) A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center. Neurocomputing 426:248–264

    Article  Google Scholar 

  • Saxena D, Gupta I, Kumar J, Singh AK, Wen X (2021) A secure and multiobjective virtual machine placement framework for cloud data center. IEEE Syst J 16:3163

    Article  Google Scholar 

  • Saxena D, Singh AK, Buyya R (2021) OP-MLB: an online VM prediction based multi-objective load balancing framework for resource management at cloud datacenter. IEEE Trans Cloud Comput.

    Article  Google Scholar 

  • Saxena D, Gupta I, Singh AK, Lee C-N (2022) A fault tolerant elastic resource management framework towards high availability of cloud services. IEEE Trans Netw Serv Manag.

    Article  Google Scholar 

  • Saxena D, Singh AK (2021) Energy aware resource efficient-(EARE) server consolidation framework for cloud datacenter. In: Advances in communication and computational technology. Springer, Singapore, pp 1455–1464

  • Shang L, Peh L-S, Jha NK (2003) Dynamic voltage scaling with links for power optimization of interconnection networks. In: The ninth international symposium on high-performance computer architecture, 2003. HPCA-9 2003. Proceedings. IEEE, pp 91–102

  • Sharma NK, Reddy GRM (2016) Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Trans Serv Comput 12(1):158–171

    Article  Google Scholar 

  • Shirvani MH, Rahmani AM, Sahafi A (2020) A survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: Taxonomy and challenges. J King Saud Univ Comput Inf Sci 32(3):267–286

  • Shirvastava S, Dubey R, Shrivastava M (2017) Best fit based VM allocation for cloud resource allocation. Int J Comput Appl 158(9):25

    Google Scholar 

  • Singh AK, Kumar J (2019) Secure and energy aware load balancing framework for cloud data centre networks. Electron Lett 55(9):540–541

    Article  Google Scholar 

  • Tseng F-H, Wang X, Chou L-D, Chao H-C, Leung VC (2017) Dynamic resource prediction and allocation for cloud data center using the multiobjective genetic algorithm. IEEE Syst J 12(2):1688–1699

    Article  Google Scholar 

  • Wang Y, Xia Y (2016) Energy optimal VM placement in the cloud. In: 2016 IEEE 9th international conference on cloud computing (CLOUD). IEEE, pp. 84–91

  • Xu J, Fortes JAB (2010) Multi-objective virtual machine placement in virtualized data center environments. In: 2010 IEEE/ACM international conference on green computing and communications international conference on cyber, physical and social computing, pp 179–188.

  • Yang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, pp 240–249

Download references


The authors would like to thank the National Institute of Technology, Kurukshetra, India, for financially supporting this research work.


Funding was provided by National Institute of Technology Kurukshetra.

Author information

Authors and Affiliations



All the authors have discussed and constructed the ideas, designed the Virtual Machine Placement framework, and wrote the paper together.

Corresponding author

Correspondence to Smruti Rekha Swain.

Ethics declarations

Conflict of interest

The authors have no conflict of interest regarding the publication.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Singh, A.K., Swain, S.R. & Lee, C.N. A metaheuristic virtual machine placement framework toward power efficiency of sustainable cloud environment. Soft Comput 27, 3817–3828 (2023).

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Virtual machine
  • Physical machine
  • Energy consumption
  • Random fit algorithm
  • Flower pollination optimization