Advertisement

SAVE: self-adaptive consolidation of virtual machines for energy efficiency of CPU-intensive applications in the cloud

  • Wenxia GuoEmail author
  • Ping Kuang
  • Yaqiu Jiang
  • Xiang Xu
  • Wenhong Tian
Article

Abstract

In virtualized data centers, consolidation of virtual machines (VMs) on minimizing the number of total physical machines (PMs) has been recognized as a very efficient approach. This paper considers the energy-efficient consolidation of VMs in a cloud datacenter. Concentrating on CPU-intensive applications, the objective is to schedule all requests non-preemptively, subjecting to constraints of PM capacities and running time interval spans, to make the total energy consumption of all PMs is minimized (called MinTE for abbreviation). The MinTE problem is NP-complete in general. We propose a self-adaptive approach called SAVE. The approach makes decisions of the assignment and migration of VMs by probabilistic processes and is based exclusively on local information. Both simulation and real environment test show that our proposed method SAVE can reduce energy consumption about \(30\%\) against VMWare DRS and 10–20% against ecoCloud on average. Extensive experiments show that our method outperforms the existing method and achieves significant energy savings and high utilization.

Keywords

Cloud computing Consolidation of virtual machines Energy efficiency Self-adaptive 

Notes

Acknowledgements

This research is sponsored by the Natural Science Foundation of China (NSFC) Grants 61672136, 61828202; and Xi Bu Zhi Guang Plan of Chinese Academy of Science (R51A150Z10), and Science and Technology Plan of Sichuan Province (2016GZ0322).

References

  1. 1.
    Amazon EC2. http://aws.amazon.com/ec2/. Accessed 2006
  2. 2.
    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
  3. 3.
    Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2010) 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
  4. 4.
    VMWare. http://www.vmware.com/. Accessed 2019
  5. 5.
    Feller E, Morin C, Esnault A (2013) A case for fully decentralized dynamic VM consolidation in clouds. In: IEEE International Conference on Cloud Computing Technology and Science, vol 43, no. 8, pp 26–33Google Scholar
  6. 6.
    Mastroianni C, Meo M, Papuzzo G (2013) Probabilistic consolidation of virtual machines in self-organizing cloud data centers. IEEE Trans Cloud Comput 1(2):215–228CrossRefGoogle Scholar
  7. 7.
    Mathew V, Sitaraman RK, Shenoy P (2012) Energy-aware load balancing in content delivery networks. Proc INFOCOM 2012:954–962Google Scholar
  8. 8.
    Guo W, Ren X, Tian W, Venugopal S (2017) Self-adaptive consolidation of virtual machines for energy-efficiency in the cloud. In: Proceedings of the 2017 6th International Conference on Network, Communication and Computing, pp 120–124Google Scholar
  9. 9.
    Beloglazov A, Buyya R, Lee YC, Zomaya AY (2011) A taxonomy and survey of energy-efficient data centers and cloud computing systems. In: Zelkowitz M (ed) Advances in computers, vol 82. Elsevier, Amsterdam, pp 47–111Google Scholar
  10. 10.
    Kaur A, Luthra MP (2018) A review on load balancing in cloud environment. Int J Comput Technol 12(1):7120–7125CrossRefGoogle Scholar
  11. 11.
    Xu M, Tian W, Buyya R (2017) A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurr Comput Pract Exp 29(12):e4123CrossRefGoogle Scholar
  12. 12.
    Xu M, Buyya R (2019) brownout approach for adaptive management of resources and applications in cloud computing systems: a taxonomy and future directions. ACM Comput Surv (CSUR) 51(1):8Google Scholar
  13. 13.
    Priya V, Kumar CS, Kannan R (2019) Resource scheduling algorithm with load balancing for cloud service provisioning. Appl Soft Comput 76:416–424CrossRefGoogle Scholar
  14. 14.
    Liu Q, Jiang YH (2018) A survey of machine learning-based resource scheduling algorithms in cloud computing environment. In: International Conference on Cloud Computing and Security. Springer, pp 243–252Google Scholar
  15. 15.
    Imes C, Hofmeyr S, Hoffmann H (2018) Energy-efficient application resource scheduling using machine learning classifiers. In: Proceedings of the 47th International Conference on Parallel Processing. ACM, p 45Google Scholar
  16. 16.
    Yang R, Ouyang X, Chen Y, Townend P, Xu J (2018) Intelligent resource scheduling at scale: a machine learning perspective. In: 2018 IEEE Symposium on Service-Oriented System Engineering (SOSE). IEEE, pp 132–141Google Scholar
  17. 17.
    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, pp 1–10Google Scholar
  18. 18.
    Beloglazov A, Buyya R (2010) Energy efficient allocation of virtual machines in cloud data centers. In: IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp 577–578Google Scholar
  19. 19.
    Lee YC, Zomaya AY (2012) Energy efficient utilization of resources in cloud computing systems. J Supercomput 60(2):268–280CrossRefGoogle Scholar
  20. 20.
    Tian W, Yeo CS, Xue R, Zhong Y (2013) Power-aware scheduling of real-time virtual machines in cloud data centers considering fixed processing intervals. In: IEEE International Conference on Cloud Computing and Intelligent Systems, vol 1, pp 269–273Google Scholar
  21. 21.
    Kansal A, Zhao F, Liu J, Kothari N, Bhattacharya AA (2010) Virtual machine power metering and provisioning. In: ACM Symposium on Cloud Computing, pp 39–50Google Scholar
  22. 22.
    Economou D, Rivoire S, Kozyrakis C, Ranganathan P (2006) Full-system power analysis and modeling for server environments. In: Workshop on Modeling Benchmarking and Simulation (MOBS)Google Scholar
  23. 23.
    Bohra AEH, Chaudhary V (2010) VMeter: power modelling for virtualized clouds. In: IEEE International Symposium on Parallel & Distributed Processing, Workshops and Ph.D. Forum, pp 1–8Google Scholar
  24. 24.
    Guazzone M, Anglano C, Canonico M (2011) Energy-efficient resource management for cloud computing infrastructures. In: Proceedings of 3rd IEEE International Conference on Cloud Computing Technology and Science, pp 424–431Google Scholar
  25. 25.
    Flammini M, Monaco G, Moscardelli L, Shachnai H, Shalom M, Tamir T, Zaks S (2009) Minimizing total busy time in parallel scheduling with application to optical networks. In: IEEE International Symposium on Parallel & Distributed Processing, vol. 411, no. 40, pp1–12Google Scholar
  26. 26.
    Kim K, Beloglazov A, Buyya R (2011) Power-aware provisioning of virtual machines for real-time Cloud services. Concurr Comput Pract Exp 23(13):1491–1505CrossRefGoogle Scholar
  27. 27.
    Tian WH, Xiong Q, Cao J (2013) An online parallel scheduling method with application to energy-efficiency in cloud computing. J Supercomput 66:1773–1790CrossRefGoogle Scholar
  28. 28.
    Shalom M, Voloshin A, Wong PWH, Yung FCC, Zaks S (2012) Online optimization of busy time on parallel machines. In: International Conference on Theory and Applications of MODELS of Computation, pp 448–460Google Scholar
  29. 29.
    Tian W, Xue R, Cao J, Xiong Q, Hu Y (2013) An energy-efficient online parallel scheduling algorithm for cloud data centers, pp 397–402Google Scholar
  30. 30.
    Tian WH, Yeo CS (2015) Minimizing total busy-time in offline parallel scheduling with application to energy efficiency in cloud computing. Concurr Comput Pract Exp 27(9):2191–2502CrossRefGoogle Scholar
  31. 31.
    Rohit K, Schieber B, Shachnai H, Tamir T (2010) Minimizing busy time in multiple machine real-time scheduling. In: IARCS Conference on Foundations of Software Technology and Theoretical Computer Science, vol. 8, no 4, pp 169–180Google Scholar

Copyright information

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

Authors and Affiliations

  • Wenxia Guo
    • 1
    Email author
  • Ping Kuang
    • 1
  • Yaqiu Jiang
    • 1
  • Xiang Xu
    • 1
  • Wenhong Tian
    • 1
  1. 1.University of Electronic Science and Technology of China (UESTC)ChengduChina

Personalised recommendations