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Self-economy in Cloud Data Centers: Statistical Assignment and Migration of Virtual Machines

  • Carlo Mastroianni
  • Michela Meo
  • Giuseppe Papuzzo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6852)

Abstract

The success of Cloud computing has led to the establishment of large data centers to serve the increasing need for on-demand computational power, but data centers consume a huge amount of electrical power. The problem can be alleviated by mapping virtual machines, VMs, which run client applications, on as few servers as possible, so that some servers with low traffic can be put in low consuming sleep modes. This paper presents a new approach for the adaptive assignment of VMs to servers and their dynamic migration, with a twofold goal: reduce the energy consumption and meet the Service Level Agreements established with users. The approach, based on ant-inspired algorithms, founds on statistical processes: the mapping and migration of VMs are driven by Bernoulli trials whose success probability depends on the utilization of single servers. Experiments highlight the two main advantages with respect to the state of the art: the approach is self-organizing and mostly decentralized, since each server locally decides whether or not a new VM can be served, and the migration process is continuous and adaptive, thus avoiding the need for the simultaneous reassignment of many VMs.

Keywords

Cloud Computing Virtual Machine Data Center Reduce Power Consumption Assignment Procedure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. IEEE Computer 40(12), 33–37 (2007)CrossRefGoogle Scholar
  2. 2.
    Beloglazov, A., Buyya, R.: Energy efficient allocation of virtual machines in cloud data centers. In: 10th IEEE/ACM Int. Symp. on Cluster Computing and the Grid, CCGrid 2010, pp. 577–578 (2010)Google Scholar
  3. 3.
    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Compututer Systems 25(6), 599–616 (2009)CrossRefGoogle Scholar
  4. 4.
    Chen, Y., Das, A., Qin, W., Sivasubramaniam, A., Wang, Q., Gautam, N.: Managing server energy and operational costs in hosting centers. SIGMETRICS Perform. Eval. Rev. 33(1), 303–314 (2005)CrossRefGoogle Scholar
  5. 5.
    Dubois, D.J., Mirandola, R., Barbagallo, D., Di Nitto, E.: A bio-inspired algorithm for energy optimization in a self-organizing data center. In: Self-Organizing Architectures. Springer, Heidelberg (2010)Google Scholar
  6. 6.
    Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chrétien, L.: The dynamics of collective sorting: robot-like ants and ant-like robots. In: First International Conference on Simulation of Adaptive Behavior on From Animals to Animats, pp. 356–363. MIT Press, Cambridge (1990)Google Scholar
  7. 7.
    Forestiero, A., Mastroianni, C., Spezzano, G.: So-grid: A self-organizing grid featuring bio-inspired algorithms. ACM Transactions on Autonomous and Adaptive Systems 3(2) (May 2008)Google Scholar
  8. 8.
    Greenberg, A., Hamilton, J., Maltz, D.A., Patel, P.: The cost of a cloud: research problems in data center networks. SIGCOMM Comput. Commun. Rev. 39(1), 68–73 (2009)CrossRefGoogle Scholar
  9. 9.
    Hirofuchi, T., Ogawa, H., Nakada, H., Itoh, S., Sekiguchi, S.: A live storage migration mechanism over wan for relocatable virtual machine services on clouds. In: 9th IEEE/ACM Int. Symp. on Cluster Computing and the Grid, CCGrid 2009 (2009)Google Scholar
  10. 10.
    Mazzucco, M., Dyachuk, D., Deters, R.: Maximizing cloud providers’ revenues via energy aware allocation policies. In: 10th IEEE/ACM Int. Symp. on Cluster Computing and the Grid, CCGrid 2010, pp. 131–138 (2010)Google Scholar
  11. 11.
    Mi, H., Wang, H., Yin, G., Zhou, Y., Shi, D., Yuan, L.: Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers. In: 2010 IEEE Int. Conference on Services Computing, SCC 2010, Miami, Fl, USA, pp. 514–521 (July 2010)Google Scholar
  12. 12.
    Verma, A., Ahuja, P., Neogi, A.: pMapper: Power and migration cost aware application placement in virtualized systems. In: Issarny, V., Schantz, R. (eds.) Middleware 2008. LNCS, vol. 5346, pp. 243–264. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Yue, M.: A simple proof of the inequality FFD (L) ≤ 11/9 OPT (L) + 1, for all L for the FFD bin-packing algorithm. Acta Mathematicae Applicatae Sinica 7(4), 321–331 (1991)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carlo Mastroianni
    • 1
  • Michela Meo
    • 2
  • Giuseppe Papuzzo
    • 1
  1. 1.ICAR-CNR, Rende (CS)Italy
  2. 2.Politecnico di TorinoItaly

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