Journal of Network and Systems Management

, Volume 27, Issue 1, pp 149–165 | Cite as

Virtual Machine Placement Algorithm for Energy Saving and Reliability of Servers in Cloud Data Centers

  • JungYul ChoiEmail author


Since cloud data centers operating from thousands to tens of thousands of servers consume enormous amount of power, there is a strong interest in energy efficiency. With virtualization technology, a server can accommodate multiple virtual machines. Once a server is running, it consumes a high amount of power even if the utilization is low, so placing as many virtual machines on a few servers as possible is desirable to save power. On the other hand, in a highly integrated environment, the heat generated from servers can cause a heat island. High-temperature environment can cause serious problems with the reliability of the servers, so the virtual machine should be placed in consideration of this. For resolving the problems, this paper proposes a virtual machine placement algorithm for energy saving considering server reliability. According to the performance evaluation, the proposed algorithm shows the similar or better level of power consumption as the existing methods, while it achieves the target server reliability and no heat islands generated.


Cloud data center Energy saving Reliability Queueing system Virtual machine placement 


  1. 1.
    TTAK.KO-10.0762 Evaluation framework for energy efficiency of cloud data centers. TTA standards, 2014Google Scholar
  2. 2.
    ITU-T Recommendation L.1300 Best practices for Green Data Centres, 2014Google Scholar
  3. 3.
    Fulpagare, Y., Bhargav, A.: Advances in data center thermal management. Renew. Sustain. Energy Rev. 43, 981–996 (2015)CrossRefGoogle Scholar
  4. 4.
    Oro, E., Depoorter, V., Garcia, A., Salom, J.: Energy efficiency and renewable energy integration in data centres. Renew. Sustain. Energy Rev. 42, 429–445 (2015)CrossRefGoogle Scholar
  5. 5.
    Beloglazov, A., Jemal, A., Rajkumar, B.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)CrossRefGoogle Scholar
  6. 6.
    Ajiro, Y., Tanaka, A.: Improving packing algorithms for server consolidation. In: Int. CMG Conference 2007, vol. 253, pp. 399–406, Dec. 2007Google Scholar
  7. 7.
    Kaplan, F., Meng, J., Coskun, A.K.: Optimizing communication and cooling costs in HPC data centers via intelligent job allocation. In: IEEE International Green Computing Conference 2013, June 2013Google Scholar
  8. 8.
    Choi, J.: Virtual machine placement algorithm for saving energy and avoiding heat islands in high-density cloud computing environment. J. Korean Inst. Commun. Sci. 41(10), 1233–1235 (2016)Google Scholar
  9. 9.
    Al-Qawasmeh, A.M., Pasricha, S., Maciejewski, A.A., Siegel, H.J.: Power and thermal-aware workload allocation in heterogeneous data centers. IEEE Trans. Comput. 64(2), 477–491 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Lee, C., Lee, D.-T.: A simple on-line bin-packing algorithm. J. ACM (JACM) 32(3), 562–572 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Dósa, G.: The tight bound of first fit decreasing bin-packing algorithm is FFD (I) ≤ 11/9OPT (I) + 6/9, Lecture Notes in Computer Science, vol. 4614 (Combinatorics, Algorithms, Probabilistic and Experimental Methodologies). Springer, pp. 1–11, 2007Google Scholar
  12. 12.
    Levine, J., Ducatelle, F.: Ant colony optimization and local search for bin packing and cutting stock problems. J. Oper. Res. Soc. 55(7), 705–716 (2004)CrossRefzbMATHGoogle Scholar
  13. 13.
    Feller, E., Louis, R., Christine, M.: Energy-aware ant colony based workload placement in clouds. In: Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing, pp. 1–19, 2011Google Scholar
  14. 14.
    Ferdaus, M.H., Murshed, M., Calheiros, R.N., Buyya, R.: Virtual machine consolidation in cloud data centers using ACO metaheuristic. In: European Conference on Parallel Processing. Springer, 2014Google Scholar
  15. 15.
    Gao, Y., Guan, H., Qi, Z., Hou, Y., Lie, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(8), 1230–1242 (2013)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ali, H.M., Lee, D.C.: A biogeography-based optimization algorithm for energy efficient virtual machine placement. In: 2014 IEEE Symposium on Swarm Intelligence (SIS), pp. 1–6, 2014Google Scholar
  17. 17.
    Zheng, Q., Li, R., Li, X., Wu, J.: A multi-objective biogeography-based optimization for virtual machine placement. In: 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 687–696, 2015Google Scholar
  18. 18.
    Tang, Q., Gupta, S.K.S., Varsamopoulos, G.: Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: a cyber-physical approach. IEEE Trans. Parallel Distrib. Syst. 19(11), 1458–1472 (2008)CrossRefGoogle Scholar
  19. 19.
    Wang, L., Laszewski, G., Dayal, J., He, X., Younge, A.J., Furlani, T.R.: Towards thermal aware workload scheduling in a data center. In: 10th International Symposium on Pervasive Systems, Algorithms, and Networks. IEEE, pp. 116–122, Dec. 2009Google Scholar
  20. 20.
    Islam, M.A., Ren, S., Pissinou, N., Mahmud, A.H., Vasilakos, A.V.: Distributed temperature-aware resource management in virtualized data center. Sustain. Comput. Inf. Syst. 6, 3–16 (2015)Google Scholar
  21. 21.
    Wang, W., Chen, H., Chen, X.: An availability-aware virtual machine placement approach for dynamic scaling of cloud applications. In: 9th International Conference on Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing (UIC/ATC), pp. 509–516, 2012Google Scholar
  22. 22.
    ASHRAE TC 9.9 (2011) Thermal guidelines for data processing environments—expanded data center classes and usage guidance, 2011Google Scholar
  23. 23.
    ISO/IEC 30134-2:2016 Information technology—data centres—key performance indicators—Part 2: power usage effectiveness (PUE)Google Scholar
  24. 24.
    Choi, J., Woo, S., Shim, B.: Reliable service provisioning in converged multimedia network environment. J. Netw. Comput. Appl. 34(1), 394–401 (2011)CrossRefGoogle Scholar
  25. 25.
    Kleinrock, L.: Queueing Systems, Volume 1: Theory. Wiley-Interscience Publication, New York (1975)zbMATHGoogle Scholar
  26. 26.
    Matlab programming tool, Release 2016b. The MathWorks, Inc., Natick, MA (2016).
  27. 27.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithm, 3rd edn. The MIT Press, Cambridge, MA (2009)zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Computer EngineeringSungkyul UniverisityAnyangSouth Korea

Personalised recommendations