Reduce Energy Consumption through Virtual Machine Placement in Cloud Data Centre

  • Nongmaithem Ajith Singh
  • M. Hemalatha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)

Abstract

In this paper, energy consumption in the data centre was studied where thousands of servers and other devices runs, energy are utilized to run the server and cooling the environment. Energy consumption can be reduced by switching off the idle server by means of migration of Virtual Machine from under-load Host. Load in cloud computing is maintained by migration of VM from the overloaded Host to a free Host or activate new Host. Based on this study, a reservation technique by using BIN packing was proposed in this paper with an overload detection algorithm. The proposed algorithm RBIN is experimented in 800 servers with 1024 Virtual Machines. From the experimental result, proposed method RBIN reduces energy in higher level.

Keywords

Cloud Computing energy virtual machine RBIN PR 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mell, P., Grance, T.: NIST. The NIST Definition of Cloud Computing (ver. 15). National Institute of Standards and Technology, Information Technology Laboratory (October 7 2009) Google Scholar
  2. 2.
    Randles, M., Lamb, D., Taleb-Bendiab, A.: A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing. In: IEEE 24th International Conference on Advanced Information Networking and Applications Workshops, pp. 551–556 (2010)Google Scholar
  3. 3.
    Abdul-Rahman, O., Munetomo, M., Akama, K.: Live Migration-based Resource Managers for Virtualized Environments: ASurvey. In: The First International Conference on Cloud Computing, GRIDs, and Virtualization, pp. 32–40 (2010)Google Scholar
  4. 4.
    Esnault, A.: Energy-Aware Distributed Ant Colony Based Virtual Machine Consolidation in IaaS Clouds, http://dumas.ccsd.cnrs.fr/dumas-00725215/
  5. 5.
    Beloglazov, A., Buyya, R.: Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers. Software: Practice and Experience 41(1), 23–50 (2011)Google Scholar
  6. 6.
    Wang, S.-C., Yan, K.-Q., Liao, W.-P., Wang, S.-S.: Load Balancing in Three-Level Cloud Computing Network, 978-1-4244-5540-9/10/$26.00 ©2010 IEEEGoogle Scholar
  7. 7.
    Grewal, R.K., Pateriya, P.K.: A Rule-based Approach for Effective Resource Provisioning in Hybrid Cloud Environment. International Journal of Computer Science and Informatics 1(4) (2012)Google Scholar
  8. 8.
    Bobroff, N., Kochut, A., Beaty, K.: Dynamic placement of virtual machines for managing SLA violations’. In: Proc. International Symposium on Integrated Network Management 2007 (2007)Google Scholar
  9. 9.
    Verma, A., Dasgupta, G., Nayak, T.K., De, P., Kothari, R.: Server workload analysis for power minimization using consolidation. In: Proceedings of the 2009 Conference on USENIX Annual Technical Conference, San Diego, California, June 14-19, p. 28 (2009)Google Scholar
  10. 10.
    Clark, C., Fraser, K., Hand, S., Hansen, J.G., Jul, E., Limpach, C., Pratt, I., Warfield, A.: Live migration of virtual machines. In: NSDI 2005: Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation, pp. 273–286. USENIX Association, Berkeley (2005)Google Scholar
  11. 11.
  12. 12.
    Calheiros, R.N., et al.: CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services, Technical Report, GRIDS-TR-2009-1, Grid Computing and Distributed Systems Laboratory, The University of Melbourne, Australia (March 13, 2009)Google Scholar
  13. 13.

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Nongmaithem Ajith Singh
    • 1
  • M. Hemalatha
    • 1
  1. 1.Department of Computer ScienceKarpagam UniversityCoimbatoreIndia

Personalised recommendations