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Improving Performance in Cloud using Multi-Job Scheduling based Group Discovery Algorithm

by Nandhini A, Saravana Balaji B
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 91 - Number 2
Year of Publication: 2014
Authors: Nandhini A, Saravana Balaji B
10.5120/15852-4751

Nandhini A, Saravana Balaji B . Improving Performance in Cloud using Multi-Job Scheduling based Group Discovery Algorithm. International Journal of Computer Applications. 91, 2 ( April 2014), 11-17. DOI=10.5120/15852-4751

@article{ 10.5120/15852-4751,
author = { Nandhini A, Saravana Balaji B },
title = { Improving Performance in Cloud using Multi-Job Scheduling based Group Discovery Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 91 },
number = { 2 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume91/number2/15852-4751/ },
doi = { 10.5120/15852-4751 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:11:42.652396+05:30
%A Nandhini A
%A Saravana Balaji B
%T Improving Performance in Cloud using Multi-Job Scheduling based Group Discovery Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 91
%N 2
%P 11-17
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cloud computing is a large model change of computing system. It provides high scalability and flexibility among an assortment of on-demand services. To imporve the performance of the multi-cloud environment in distributed application might require less energy efficiency and minimal inter-node latency correspondingly. The major problem is that the energy efficiency of the cloud computing data center is less if the number of server is low, else it increases. To overcome the energy efficiency and network latency problem a novel energy-efficient particle swarm optimization representation for multi-job scheduling and Latency representation for the grouping of nodes with respect to network latency is proposed. Design a realistic particle swarm optimization algorithm for the cloud servers and construct an overall energy competence based on the purpose of the servers and calculation of fitness value for each cloud servers. Also, in order to speed up the convergent speed and improve the probing aptitude of our algorithm, a local search operative is introduced. Finally, the experiment demonstrates that the proposed algorithm is effectual and well-organized.

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Index Terms

Computer Science
Information Sciences

Keywords

Cloud Computing Network Latency Energy Efficiency Particle Swarm Optimization (PSO) and Multi-Job Scheduling.

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