Call for Paper - August 2019 Edition
IJCA solicits original research papers for the August 2019 Edition. Last date of manuscript submission is July 20, 2019. Read More

Best Fit based VM Allocation for Cloud Resource Allocation

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Saurabh Shirvastava, Rahul Dubey, Manish Shrivastava

Saurabh Shirvastava, Rahul Dubey and Manish Shrivastava. Best Fit based VM Allocation for Cloud Resource Allocation. International Journal of Computer Applications 158(9):25-27, January 2017. BibTeX

	author = {Saurabh Shirvastava and Rahul Dubey and Manish Shrivastava},
	title = {Best Fit based VM Allocation for Cloud Resource Allocation},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {158},
	number = {9},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {25-27},
	numpages = {3},
	url = {},
	doi = {10.5120/ijca2017912869},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Cloud consists of datacenters with each datacenter having large number of physical machines. On top of each physical machine a virtual machine is created. In this paper a clustering based solution for VM migrations and consolidation is proposed. The proposed best fit VM placement approach is also energy efficient as compared to traditional VM placement approach.


  2. Mladen A. Vouk, Cloud Computing – Issues, Research and Implementations, Information technology Interfaces 30th International conference, Vol. 4, pp. 235–246, June 2008.
  3. Pei Fan and Zhenbang Chen and JiWang, Topology-Aware Deployment of Scientific Applications in cloud computing, Int. J. Web and Grid Services, Vol. 36, PP. 319 - 326, July 2014.
  4. Bhupendra Panchal and Prof. R. K. Kapoor, “Dynamic VM Allocation Algorithm using Clustering in Cloud Computing”, International Journal of advanced research in computer science and software engineering, Volume 3, Issue 9, PP. 143-150, September 2011.
  5. Daniel A. Menasc´e Paul Ngo, “Understanding Cloud Computing: Experimentation and Capacity Planning”, Proc. Computer Measurement Group Conf, Dallas, TX, Dec. 7-11, 2009.
  6. Calheiros R.N., “Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments”, International Conference on Parallel Processing, page no 295-304,Sept 2011.
  7. Michael Shindler, Alex Wong, “Fast and Accurate k-means For Large Datasets”, PP. 1-9, 2011.
  8. Soumya Ray and Ajanta De Sarkar, “Execution analysis of load balancing algorithm in cloud computing environment”, International Journal on Cloud Computing: Services and Architecture (IJCCSA), Vol.2, No.5, PP. 1-13, October 2012.
  9. Ahmed ali eldin, Maria Kihl and Johan Tordsson, “Efficient provisioning of bursty scientific workload on the Cloud Using Adaptive Elasticity Contro”l, journal of Computer Science Issues, Vol. 9, Issue 1, No 1, PP. 31-40, January 2012.
  10. Nidhi Jain Kansal, Inderveer Chana, “Cloud Load Balancing Techniques : A Step Towards Green Computing, International Journal of Computer Science Issues, Vol. 9, Issue 1, No 1, PP. 1694-0814, January 2012.
  11. Rajleen Kaur, Amanpreet Kaur, “A Review Paper on Evolution of Cloud Computing, its Approaches and Comparison with Grid Computing”, International Journal of Computer Science and Information Technologies, Vol. 5, PP. 6060-6063, 2014.
  12. Abhishek Gupta, Osman Sarood, Laxmikant V Kale, “Optimizing VM Placement for HPC in the Cloud”, International Letters of Social and Humanistic Sciences, Vol. 16, PP. 1-6, 2014.
  13. Dzmitry Kliazovich, Sisay T. Arzo, Fabrizio Granelli, “e-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing”, IEEE International Conference on Green Computing and Communications, pp. 7-13, 2013.


computing, VM placement and consolidation, Best fit approach.