Enhanced Load Balancing Approach to Optimize the Performance of the Cloud Service using Virtual Machine Migration

Full Text (PDF, 357KB), PP.41-48

Views: 0 Downloads: 0

Author(s)

Saurabh Jain 1 Varsha Sharma 1

1. SOIT, Rajeev Gandhi Proudyogiki Vishwavidyalaya,Bhopal,462023,India

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2017.01.04

Received: 21 Sep. 2016 / Revised: 26 Oct. 2016 / Accepted: 30 Nov. 2016 / Published: 8 Jan. 2017

Index Terms

Virtualization, migration, energy efficient, virtual machine, physical machine

Abstract

Cloud computing is a fastest growing technology in the research and industry field.It provides the on demand resources to the customers on the rent basis. These resources are provided through the virtual machines. Resources required by the virtual machines can change dynamically. So load balancing in the cloud is more challenging task as compared to the traditional computing, where the resource requirements are not changed with time. Overall performance of the cloud system can be increased by the efficient load balancing approach. Three steps are involved in the load balancing method i.e., physical machine selection, virtual machine selection and destination physical machine selection. In the past few years a number of load balancing approaches have been proposed to increase the resource utilization and minimize the energy consumption. This paper has proposed a load balancing approach which uses the lower and upper threshold to select the physical machine (PM) for migrating the virtual machine (VM). Then place the selected VM to the PM which consumes minimum power to minimize the energy consumption.
To create the cloud environment, CloudSim simulator is used which provides the interface to deal with the physical and virtual machines. To evaluate the performance, the proposed method is compared with already present load balancing approaches. Simulation result shows that proposed approach minimize the energy consumption, migrations and total simulation time.

Cite This Paper

Saurabh Jain, Varsha Sharma,"Enhanced Load Balancing Approach to Optimize the Performance of the Cloud Service using Virtual Machine Migration", International Journal of Engineering and Manufacturing(IJEM), Vol.7, No.1, pp.41-48, 2017. DOI: 10.5815/ijem.2017.01.04

Reference

[1] Rajkumar Buyya, Chee Shin Yeo, SrikumarVenugopal, James Broberg and IvonaBrandic, "Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility", Future Generation Computer Systems, vol. 25, no. 6, june 2011, pp 599-616.

[2] Peter Mell and Timothy Grance "The NIST Definition of Cloud Computing". NIST Special Publication, 2011.

[3] Barrie Sosinsky, "Cloud Computing Bible", 1st ed., USA: Wiley Publishing Inc., 2012.

[4] VMware Inc. VMware distributed power management concepts and use, 2010.

[5] Peer1 hosting site puts a survey on "Visualized: ring around the world of data center power usage", from engadget.com, 2011.

[6] Rajeev Kumar Gupta and R. K. Pateriya, "A Complete Theoretical Review on Virtual Machine Migration in Cloud Environment", International Journal of Cloud Computing and Services Science (IJ-CLOSER), Vol.3, No.3, June 2014, pp. 172-178.

[7] Daniel Guimaraes do Lago, Edmundo R. M. Medeira and Luiz Fernando Bittencourt, "Power-Aware Virtual Machine Scheduling on Clouds Using Active Cooling Control and DVFS", 10th IEEE/ACM Intl. Symp. on Cluster Computing, 2010.

[8] Mayanka Katyal and Atul Mishra, "Comparative Study of Load Balancing Algorithms in Cloud Computing Environment" Article can be accessed online at http://www.publishingindia.com, 2013.

[9] Akshay Jain, AnaghaYadav, Lohit Krishnan and Jibi Abraham, "A Threshold Band Based Model For Automatic Load Balancing in Cloud Environment", in proc. of IEEE International Conference on Cloud Computing in Emerging Markets, pp 1-7, 2013.

[10] Yiqiu Fang, Fei Wang and JunweiGe, "A Task Scheduling Algorithm Based on Load Balancing in Cloud Computing", 18th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises, 2010, pp. 271-277.

[11] Kamlesh kumar Pathak, Prasant Singh Yadav, Rameshwaram Tiwari and Dr. Tarun Kumar Gupta "A Modified Approach for Load Balancing in Cloud Computing Using Extended Honey Bee Algorithm", International Journal of Research Review in Engineering Science and Technology, December 2012, pp. 12-19.

[12] Nitish Krishna G, Subramanian S, Kiran Kumar M, Sreesh P and G. R. Karpagam, "ANAdaptive Algorithm for Dynamic Priority Based Virtual Machine Scheduling in Cloud", International Journal of Computer Science Issue, November 2012, pp. 397-402.

[13] Mohammad H. AL Shayejiand and M.D. Samrajesh, "An Energy-aware Virtual Machine Migration Algorithm", proceeding of the International Conference on Advances in Computing and Communications, August 2012, pp. 242-246.

[14] Gaston Keller, Michael Tighe, HananLutfiyya and Michael Bauer, "An Analysis of First Fit Heuristics for the Virtual Machine Relocation Problem.", proceeding of the 6th International DMTF workshop on Systems and Virtualization Management (SVM)/CNSM,October 2012, pp. 406 - 413.

[15] RabiatulAddawiyah, Mat Razali, RuhaniAbRahman, NorlizaZaini and MustaffaSamad, "Virtual Machine Migration Implementation in Load Balancing for Cloud Computing", 5th International IEEE Conference on Intelligent and Advanced Systems, 2014, pp. 1-4.

[16] R. Calheiros, R Ranjan, C├ęsar A. F. De Rose, R. Buyya, "CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services", 2011.

[17] Vijaypal S. Rathor, R. K. Pateriya, Rajeev K. Gupta, "An Efficient Virtual Machine Scheduling Technique in Cloud Computing Environment", IJMECS, vol.7, no.3, pp.39-46, 2015.DOI: 10.5815/ijmecs.2015.03.06.

[18] R. Narayani, W. Aisha Banu, "Framework for Provence based Virtual Machine Placement in Cloud", IJEME, vol. 5, no. 1. pp.19-26, 2015. DOI: 10.5815/ijeme.2015.01.03.