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Workload Analysis in a Grid Computing Environment: A Genetic Approach

by P. K. Yadav, Anuradha Aggarwal, M. P. Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 93 - Number 16
Year of Publication: 2014
Authors: P. K. Yadav, Anuradha Aggarwal, M. P. Singh
10.5120/16300-6106

P. K. Yadav, Anuradha Aggarwal, M. P. Singh . Workload Analysis in a Grid Computing Environment: A Genetic Approach. International Journal of Computer Applications. 93, 16 ( May 2014), 26-29. DOI=10.5120/16300-6106

@article{ 10.5120/16300-6106,
author = { P. K. Yadav, Anuradha Aggarwal, M. P. Singh },
title = { Workload Analysis in a Grid Computing Environment: A Genetic Approach },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 93 },
number = { 16 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 26-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume93/number16/16300-6106/ },
doi = { 10.5120/16300-6106 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:15:54.844967+05:30
%A P. K. Yadav
%A Anuradha Aggarwal
%A M. P. Singh
%T Workload Analysis in a Grid Computing Environment: A Genetic Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 93
%N 16
%P 26-29
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Grid computing is the collection of computer resources from multiple locations to reach a common goal. The grid is a special type of distributed system with non-interactive workloads that involve a large number of files. Partitioning of the application program/ software into a number of small groups of modules among dissimilar processors is an important parameter to determine the efficient utilization of available resources in a grid computing environment. It also enhances the computation speed. The task partitioning and task allocation activities influence the distributed program/ software properties such as IPC. This paper presents a metaheuristic model, that performs static allocation of a set of "m" modules of distributed tasks/program considering the two conflicting objectives i. e. minimizing the makespan time and balanced utilization of a set of "n" available resources of a grid computing. Experimental results using genetic algorithm indicates that the proposed algorithm achieved these two objectives as well as improve the dynamic heuristics presented in literature.

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Index Terms

Computer Science
Information Sciences

Keywords

Grid computing Task Allocation Makespan Execution Cost Inter Task (module) Communication Cost

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