Design of QoS and Energy Efficient VM Consolidation Framework for Cloud Data Centers

  • Neha SongaraEmail author
  • Manoj Kumar Jain
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 643)


The virtualization and Virtual Machine (VM) Consolidation are the effective solutions for energy consumption reduction. The VM consolidation algorithms designed recently shows the efficiency in terms of energy consumption, Service Level Agreement Violations (SLAV) etc. The methods still suffered from the challenges to achieve the trade-off between QoS and Energy efficiency at VM consolidation. The design of VM consolidation should be efficient in terms of VM’s allocation, migration, and workload management. In this paper, we proposed the design of novel VM consolidation approach in cloud data center to address the challenges of recent methods. First the Multi Resources based (CPU, RAM, network Traffic, bandwidth etc.) dynamic Overload Decision Algorithm (MR-ODA). For best VM selection, we used the policies of CloudSim framework. After selection of VMs to migrate, the efficient VM placement algorithm proposed based on optimization method called Particle Swarm Optimization (PSO). The algorithms designed to achieve the minimize energy consumption, QoS, reliability with minimum SLAV. The outcome of this paper is the new framework for VM consolidation to optimize the CloudSim tool.


Cloud computing CloudSim Data centers Energy consumption Physical machines Virtual machine consolidation Virtualization 


  1. 1.
    Report to congress on server and data center energy efficiency, Environmental Protection Agency (online). Accessed on 21 Nov 2016
  2. 2.
    Open Compute Project, “Energy efficiency” (online). Accessed on 3 Feb 2017
  3. 3.
    Barroso LA, Holzle U (2007) The case for energy-proportional computing. Computer 40(12):33–37CrossRefGoogle Scholar
  4. 4.
    Fan X, Weber WD, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: Proceedings of the 34th annual international symposium on computer architecture (ISCA), pp 13–23Google Scholar
  5. 5.
    Natural Resources Defense Council (online): Accessed on 12 Jan 2017
  6. 6.
    Scaling up energy efficiency across the data center industry: evaluating key drivers and barriers, NRDC., accessed on 12/2/2017 (online)
  7. 7.
    Orgerie AC, Lefevre L (2011) ERIDIS: Energy-efficient reservation infrastructure for large scale distributed systems. Parallel Process Lett 21(2):133–154MathSciNetCrossRefGoogle Scholar
  8. 8.
    Zhang Q, Zhani MF, Zhang S, Zhu Q, Boutaba R, Hellerstein JL (2012) Dynamic energy-aware capacity provisioning for cloud computing environments. In: ACM International conference on autonomic computing, ICAC, pp 145–154Google Scholar
  9. 9.
    Guenter B, Jain N, Williams C (2013) Managing cost, performance and reliability trade-offs for energy-aware server provisioning. In: Proceedings. of the 30th annual IEEE international conference on computer communications (INFOCOM), pp 1332–1340Google Scholar
  10. 10.
    Chen G, He W, Liu J, Nath S, Rigas L, Xiao L, Zhao F (2013) Energy-aware server provisioning and load dispatching for connection-intensive internet services. In: Proceedings of the 5th USENIX symposium on networked systems design and implementation, pp 337–350Google Scholar
  11. 11.
    He K, Li Z, Deng D, Chen Y (2017) Energy-efficient framework for virtual machine consolidation in cloud data centers. China Commun 14(10):192–201CrossRefGoogle Scholar
  12. 12.
    Nguyen TH, Francesco MD et al (2017) Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans Serv ComputGoogle Scholar
  13. 13.
    Zhou Z, Abawajy J, Chowdhury M et al (2017) Energy-efficient virtual machine consolidation algorithm in cloud data centers. J Cent South Univ 24(10):2331–2341CrossRefGoogle Scholar
  14. 14.
    Khoshkholghi MA, Derahman MN, Abdullah A, Subramaniam S, Othman M (2017) Energy-efficient algorithms for dynamic virtual machine consolidation in cloud data centers. IEEE Access 5Google Scholar
  15. 15.
    Chang Y, Gu C, Luo F, Fan G, Fu W (2018) Energy efficient resource selection and allocation strategy for virtual machine consolidation in cloud datacenters. IEICE Trans Inf Syst E101–D(7):1816–1827CrossRefGoogle Scholar
  16. 16.
    Liu Y, Sun X, Wei W, Jing W (2018) Enhancing energy-efficient and QoS dynamic virtual machine consolidation method in cloud environment. IEEE Access 6:31224–31235CrossRefGoogle Scholar
  17. 17.
    Li L, Dong J, Zuo D, Wu J (2019) SLA-aware and energy-efficient VM consolidation in cloud data centers using robust linear regression prediction model. IEEE Access 7:9490–9500CrossRefGoogle Scholar
  18. 18.
    Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50CrossRefGoogle Scholar
  19. 19.
    Tang M, Pan S (2014) A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural Process Lett 41(2):211–221CrossRefGoogle Scholar
  20. 20.
    Feller E, Rillin, L, Morin C (2011) Energy-aware ant colony-based workload placement in clouds. In: 12th IEEE/ACM international conference on grid computing, pp 26–33Google Scholar
  21. 21.
    Lòpez-Pires F, Barán B (2013) Multi-objective virtual machine placement with service level agreement: A memetic algorithm approach. In: Proceeding of the IEEE/ACM 6th international conference on utility and cloud computing, pp 203–210Google Scholar
  22. 22.
    Adamuthe AC, Pandharpatte RM, Thampi GT (2013) Multiobjective virtual machine placement in cloud environment. In: International conference on cloud & ubiquitous computing & emerging technologies, pp 8–13. IEEEGoogle Scholar
  23. 23.
    Dashtia SE, Rahmani AM (2015) Dynamic VMs placement for energy efficiency by PSO in cloud computing. J Exp Theor Artif Intell 28:1–16Google Scholar
  24. 24.
    Chou L, Chen H et al (2018) DPRA: dynamic power-saving resource allocation for cloud data center using particle swarm optimization. IEEE Syst J 12(2):1554–1565MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Research Scholar, Computer ScienceMohanlal Sukhadia UniversityUdaipurIndia
  2. 2.Professor, Computer ScienceMohanlal Sukhadia UniversityUdaipurIndia

Personalised recommendations