The Journal of Supercomputing

, Volume 75, Issue 4, pp 2126–2147 | Cite as

A reliable energy-aware approach for dynamic virtual machine consolidation in cloud data centers

  • Monireh H. Sayadnavard
  • Abolfazl Toroghi HaghighatEmail author
  • Amir Masoud Rahmani


To achieve energy efficiency in data centers, dynamic virtual machine (VM) consolidation as a key technique has become increasingly important nowadays due to the significant amounts of power needed to operate these data centers. Most of the existing works on VM consolidation have been focused only on reducing the number of active physical machines (PMs) using VM live migration to prevent inefficient usage of resources. But on the other hand, high frequency of VM consolidation has a negative effect on the system reliability. Indeed, there is a crucial trade-off between reliability and energy efficiency, and to optimize the relationship between these two metrics, further research is needed. Therefore, in this paper a novel approach is proposed that considers the reliability of each PM along with reducing the number of active PMs simultaneously. To determine the reliability of PMs, a Markov chain model is designed, and then, PMs have prioritized based on their CPU utilization level and the reliability status. In each phase of the consolidation process, a new algorithm is proposed. A target PM selection criterion is also presented that by considering both energy consumption and reliability selects the appropriate PM. We have validated the effectiveness of our proposed approach by conducting a performance evaluation study using CloudSim toolkit. The simulation results show that the proposed approach can significantly improve energy efficiency, avoid inefficient VM migrations and reduce SLA violations.


Cloud computing VM consolidation Live migration Energy efficiency Reliability Markov chain 


  1. 1.
    Armbrust M et al (2010) A view of cloud computing. Commun ACM 53(4):50–58CrossRefGoogle Scholar
  2. 2.
    Mills M (2013) The cloud begins with coal. Big data, big networks, big infrastructure, and big power. An overview of the electricity used by the digital ecosystem, Technical reportGoogle Scholar
  3. 3.
    Ahmad RW et al (2015) A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J Netw Comput Appl 52:11–25CrossRefGoogle Scholar
  4. 4.
    Varasteh A, Goudarzi M (2017) Server consolidation techniques in virtualized data centers: a survey. IEEE Syst J 11(2):772–783CrossRefGoogle Scholar
  5. 5.
    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(7):1366–1379CrossRefGoogle Scholar
  6. 6.
    Beloglazov A (2013) Energy-efficient management of virtual machines in data centers for cloud computing, PhD dissertationGoogle Scholar
  7. 7.
    Sharma Y et al (2016) Reliability and energy efficiency in cloud computing systems: survey and taxonomy. J Netw Comput Appl 74:66–85CrossRefGoogle Scholar
  8. 8.
    Deng W et al (2014) Reliability-aware server consolidation for balancing energy-lifetime tradeoff in virtualized cloud datacenters. Int J Commun Syst 27(4):623–642CrossRefGoogle Scholar
  9. 9.
    Varasteh A, Tashtarian F, Goudarzi M (2017) On reliability-aware server consolidation in cloud datacenters. arXiv:1709.00411
  10. 10.
    Grit L, et al (2006) Virtual machine hosting for networked clusters: Building the foundations for autonomic orchestration. In: Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing. IEEE Computer SocietyGoogle Scholar
  11. 11.
    Speitkamp B, Bichler M (2010) A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE Trans Serv Comput 3(4):266–278CrossRefGoogle Scholar
  12. 12.
    Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28(5):755–768CrossRefGoogle Scholar
  13. 13.
    Beloglazov A, Buyya R (2010) Energy efficient resource management in virtualized cloud data centers. In: Proceedings of the 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing. IEEE Computer SocietyGoogle Scholar
  14. 14.
    Esfandiarpoor S, Pahlavan A, Goudarzi M (2015) Structure-aware online virtual machine consolidation for datacenter energy improvement in cloud computing. Comput Electr Eng 42:74–89CrossRefGoogle Scholar
  15. 15.
    Zhang S et al (2016) Burstiness-aware resource reservation for server consolidation in computing clouds. IEEE Trans Parallel Distrib Syst 27(4):964–977CrossRefGoogle Scholar
  16. 16.
    Arianyan E, Taheri H, Khoshdel V (2017) Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers. J Netw Comput Appl 78:43–61CrossRefGoogle Scholar
  17. 17.
    Rao KS, Thilagam PS (2015) Heuristics based server consolidation with residual resource defragmentation in cloud data centers. Future Gener Comput Syst 50:87–98CrossRefGoogle Scholar
  18. 18.
    Li Z et al (2017) Bayesian network-based virtual machines consolidation method. Future Gener Comput Syst 69:75–87CrossRefGoogle Scholar
  19. 19.
    Khani H et al (2015) Distributed consolidation of virtual machines for power efficiency in heterogeneous cloud data centers. Comput Electr Eng 47:173–185CrossRefGoogle Scholar
  20. 20.
    Farahnakian F et al (2015) Using ant colony system to consolidate VMs for green cloud computing. IEEE Trans Serv Comput 8(2):187–198CrossRefGoogle Scholar
  21. 21.
    Mi H, et al (2010) Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers. In: Services Computing (SCC), 2010 IEEE International Conference on. IEEEGoogle Scholar
  22. 22.
    Li H et al (2016) Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing. Computing 98(3):303–317MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Fuqua NB (2003) The applicability of Markov analysis methods to reliability, maintainability, and safety. START 2(10):1–8Google Scholar
  24. 24.
    Trivedi KS (2008) Probability and statistics with reliability, queuing and computer science applications. Wiley, New YorkzbMATHGoogle Scholar
  25. 25.
    Goyal A, Lavenberg SS, Trivedi KS (1987) Probabilistic modeling of computer system availability. Ann Oper Res 8(1):285–306CrossRefGoogle Scholar
  26. 26.
    Meyer JF (1982) Closed-form solutions of performability. IEEE Trans Comput 7:648–657CrossRefGoogle Scholar
  27. 27.
    Machida F, Kim DS, Trivedi KS (2013) Modeling and analysis of software rejuvenation in a server virtualized system with live VM migration. Perform Eval 70(3):212–230CrossRefGoogle Scholar
  28. 28.
    Ghosh JK (2012) Introduction to modeling and analysis of stochastic systems, by VG Kulkarni. Int Stat Rev 80(3):487CrossRefGoogle Scholar
  29. 29.
    Sericola B (2000) Occupation times in Markov processes. Stoch Models 16(5):479–510MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    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):1397–1420CrossRefGoogle Scholar
  31. 31.
    Wei B, Lin C, Kong X (2011) Dependability modeling and analysis for the virtual data center of cloud computing. In: High Performance Computing and Communications (HPCC), IEEE 13th International Conference on 2011. IEEEGoogle Scholar
  32. 32.
    Fan X, Weber W-D, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: ACM SIGARCH Computer Architecture News. ACMGoogle Scholar
  33. 33.
    Calheiros RN et al (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50MathSciNetCrossRefGoogle Scholar
  34. 34.
    Park K, Pai VS (2006) CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Oper Syst Rev 40(1):65–74CrossRefGoogle Scholar
  35. 35.
    Matos RdS et al (2012) Sensitivity analysis of server virtualized system availability. IEEE Trans Reliab 61(4):994–1006CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019
corrected publication 2019

Authors and Affiliations

  • Monireh H. Sayadnavard
    • 1
  • Abolfazl Toroghi Haghighat
    • 2
    Email author
  • Amir Masoud Rahmani
    • 1
  1. 1.Department of Computer Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Faculty of Computer and Information Technology EngineeringQazvin Branch, Islamic Azad UniversityQazvinIran

Personalised recommendations