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Neural Processing Letters

, Volume 41, Issue 2, pp 211–221 | Cite as

A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers

  • Maolin TangEmail author
  • Shenchen Pan
Article

Abstract

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.

Keywords

Virtual machine placement Server consolidation Data center Cloud computing Hybrid genetic algorithm 

References

  1. 1.
    Meng X, Pappas V, Zhang L (2010) Improving the scalability of data center networks with traffic-aware virtual machine placement. In: Proceeding of IEEE international conference on computer communications, pp 1–9Google Scholar
  2. 2.
    Verma A, Ahuja P, Neogi A (2008) pMapper: power and migration cost aware application placement in virtualized systems. In: Proceedings of the ACM/IFIP/USENIX international conference on middleware middleware, pp 243–264Google Scholar
  3. 3.
    Hermenier F, Lorca X, Menaud JM, Muller G, Lawall J (2009) Entropy: a consolidation manager for clusters. In: Proceedings of the ACM SIGPLAN/SIGOPS international conference on virtual execution, environments, pp 41–50Google Scholar
  4. 4.
    Xu J, Fortes JAB (2010) Multi-objective virtual machine placement in virtualized data center environments. In: Proceeding of IEEE/ACM international conference on green computing and communications, pp 179–188Google Scholar
  5. 5.
    Stillwell M, Schanzenbach D, Vivien F, Casanova H (2010) Resource allocation algorithms for virtualized service hosting platforms. J Parallel Distrib Comput 70(9):962–974CrossRefzbMATHGoogle Scholar
  6. 6.
    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
  7. 7.
    Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison Wesley, New YorkzbMATHGoogle Scholar
  8. 8.
    Yao X (1999) Evolutionary computation: theory and applications. World Scientific, SingaporeCrossRefGoogle Scholar
  9. 9.
    Ong Y-S, Lim M-H, Chen X (2010) Memetic computation—past, present and future. IEEE Comp Int Mag 5(2):24–31CrossRefGoogle Scholar
  10. 10.
    Tang M, Yao X (2007) A memetic algorithm for VLSI floorplanning. IEEE Trans Syst Man Cybern 37(1):62–69CrossRefGoogle Scholar
  11. 11.
    Benson T, Akella A, Maltz DA (2010) Network traffic characteristics of data centers in the wild. In: Proceedings of the 10th annual conference on internet, measurement, pp 267–280Google Scholar
  12. 12.
    Mahadevan P, Sharma P, Banerjee S, Ranganathan P (2009) Energy aware network operations. In: Proceedings of the IEEE international conference on computer communications. pp 25–30Google Scholar
  13. 13.
    Wu G, Tang M, Tian Y-C, Li W (2012) Energy-efficient virtual machine placement in data centers by genetic algorithm. In: Proceeding of international conference on neural information processing. pp. 315–323Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceQueensland University of TechnologyBrisbaneAustralia

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