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Improved the Response Throughput of Load Balancing of Scientific Cloud using Particle of Swarm Optimization

by Rahul Bodkhe, Deepak Sain
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 143 - Number 2
Year of Publication: 2016
Authors: Rahul Bodkhe, Deepak Sain
10.5120/ijca2016910021

Rahul Bodkhe, Deepak Sain . Improved the Response Throughput of Load Balancing of Scientific Cloud using Particle of Swarm Optimization. International Journal of Computer Applications. 143, 2 ( Jun 2016), 26-30. DOI=10.5120/ijca2016910021

@article{ 10.5120/ijca2016910021,
author = { Rahul Bodkhe, Deepak Sain },
title = { Improved the Response Throughput of Load Balancing of Scientific Cloud using Particle of Swarm Optimization },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2016 },
volume = { 143 },
number = { 2 },
month = { Jun },
year = { 2016 },
issn = { 0975-8887 },
pages = { 26-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume143/number2/25050-2016910021/ },
doi = { 10.5120/ijca2016910021 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:45:17.021852+05:30
%A Rahul Bodkhe
%A Deepak Sain
%T Improved the Response Throughput of Load Balancing of Scientific Cloud using Particle of Swarm Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 143
%N 2
%P 26-30
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The efficiency and utility of cloud computing based on scheduling and balancing of load over cloud computing. The load balancing is important factor regarding the performance of cloud computing. Now a day’s various heuristic function are used for the balancing and scheduling of load in cloud computing. Some heuristic function faced a problem of size of data and discontinuity of sequence of data. In this paper used particle of swarm optimization technique for the balancing of job in cloud environment. The nature of dynamicity of particle of swarm optimization supports the concept of dynamic load balancing technique. The modified load balancing algorithm simulate cloudsim simulator and used two other algorithm such as round robin and genetic algorithm. For the evaluation of performance cerate multiple size of job load matrix. Our experimental result shows that better performance instead of round robin and genetic algorithm.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Cloud Computing Load balancing swarm intelligence PSO.

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