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Reseach Article

Energy Efficient Virtual Machine Optimization

by Vikram Yadav, Pooja Malik, Ajay Singh Chauhan, G. Sahoo
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 106 - Number 7
Year of Publication: 2014
Authors: Vikram Yadav, Pooja Malik, Ajay Singh Chauhan, G. Sahoo

Vikram Yadav, Pooja Malik, Ajay Singh Chauhan, G. Sahoo . Energy Efficient Virtual Machine Optimization. International Journal of Computer Applications. 106, 7 ( November 2014), 23-28. DOI=10.5120/18533-9741

@article{ 10.5120/18533-9741,
author = { Vikram Yadav, Pooja Malik, Ajay Singh Chauhan, G. Sahoo },
title = { Energy Efficient Virtual Machine Optimization },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 106 },
number = { 7 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 23-28 },
numpages = {9},
url = { },
doi = { 10.5120/18533-9741 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:38:46.760156+05:30
%A Vikram Yadav
%A Pooja Malik
%A Ajay Singh Chauhan
%A G. Sahoo
%T Energy Efficient Virtual Machine Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 7
%P 23-28
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

The optimization of energy consumption in the cloud computing environment is the question how to use various energy conservation strategies to efficiently allocate resources. The need of different resources in cloud environment is unpredictable. It is observed that load management in cloud is utmost needed in order to provide QOS. The jobs at over-loaded physical machine are shifted to under-loaded physical machine and turning the idle machine off in order to provide green cloud. For energy optimization, DVFS and Power-Nap are good strategies. As much of this energy is wasted in idle systems: in typical deployments, server utilization is below 30%, but idle servers still consume 60% of their peak power draw. In this paper, we have proposed an hybrid approach for energy optimization using Ant Colony optimization, Bee Colony optimization, PowerNap, DVFS and RAILS having the constraint QOS.

  1. The Google File System, http://labs. google. com/papers/gfssosp2003. pdf.
  2. Bigtable:A Distributed Storage System for Structured Data ,http://labs. google. com/papers/bigtable-osdi06. pdf
  3. MapReduce:Simplifed Data Processing on LargeClusters,http://labs. google. com/papers/mapre uceosdi04. pdf
  4. Hadoop, http://lucene. apache. org/hadoop/
  5. Amazon Simple Storage Service, http://aws. amazon. com/s3/.
  6. The Datacenter Journal, http://www. datacenterjournal. com/facilities/the-green-data-center-opportunity/.
  7. L. Barroso and U. Holzle, "The case for energy- proportional computing,"IEEE Computer, Jan 2007.
  8. X. Fan, W. D. Weber, and L. A. Barroso, "Power provisioning for a warehouse-sized computer," in Proc. of the 34th Annual InternationalSymposium on Computer Architecture, 2007.
  9. C. Lefurgy, X. Wang, and M. Ware, "Server-level powercontrol,"in Proc. of the IEEE International Conference on Autonomic Computing, Jan 2007.
  10. P. Bohrer, E. Elnozahy, T. Keller, M. Kistler, C. Lefurgy, and R. Rajamony, "The case for power management in web servers,"Power Aware Computing, Jan 2002.
  11. http://www. netxt. com/power-103-megawatt-secret- google-container-data-center/
  12. Microsoft Dublin Data Center, http://www. datacenterknowledge. com/inside-microsofts- dublin-mega-data-center/dublin-data-center-generators/
  13. D. Meisner and B. Gold, "PowerNap: Eliminating Server Idle Power", ACM 978-1-60558-215-3/09/03.
  14. Liang Luo, Wenjun Wu "A Resource Scheduling Algorithm of Cloud Computing based on Energy Efficient Optimization Methods" IEEE-2010.
  15. E. Feller. , D. Leprince and C. Morin. "State of the art of power saving in clusters results from the EDF", case study. 2010.
  16. C. H. Hsu & S. W. Poole. "Power Signature Analysis of the SPECpower_ssj2008 Benchmark[C]. Performance Analysis of Systems and Software (ISPASS)", 2011 IEEE International Symposium, 2011: 227-236.
  17. C. Lively , X. Wu, V. Taylor, S. Moore, H. Chang and K. Cameron. "Energy and performance characteristics of different parallel implementations of scientific applications on multicore systems", International Journal of High Performance Computing Applications, 2011, 25(3): 342- 350.
  18. H. Aydin, R. G Melhem, D Mosse, and P. Mejia-Alvarez. "Power-Aware Scheduling for Periodic Ral-Time Task", IEEE Transactions on Computers, May 2004, , 53(5)
  19. K. Mukherjee and G. Sahoo "Mathematical Model of Cloud computing framework using Fuzzy Bee Colony optimization Technique", International Conference on Advances in Computing, Control and Telecommunication Technologies, IEEE Xplorer, 2009.
Index Terms

Computer Science
Information Sciences


Cloud Computing Hadoop BigData DVFS Power-Nap Ant colony algorithm bee colony algorithm etc.