Saurabh Jain

Work place: Rajeev Gandhi Proudyogiki Vishwavidyalaya,Bhopal,462023,India



Research Interests: Virtual Reality


Saurabh Jain has completed his Masters in Cyber Forensic from School of Information Technology, RG.P.V. Bhopal. His dissertation work was carried out in the field of virtual machine migration in the cloud environment under the guidance of Asst. Proff. Dr. Varsha Sharma of University Institute of Technology, R.G.P.V.Bhopal.

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

By Saurabh Jain Varsha Sharma

DOI:, Pub. Date: 8 Jan. 2017

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.

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