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Population based Heuristic Approaches for Grid Job Scheduling

by Sana Alyaseri, Alaa Aljanaby
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 91 - Number 5
Year of Publication: 2014
Authors: Sana Alyaseri, Alaa Aljanaby
10.5120/15881-4853

Sana Alyaseri, Alaa Aljanaby . Population based Heuristic Approaches for Grid Job Scheduling. International Journal of Computer Applications. 91, 5 ( April 2014), 45-50. DOI=10.5120/15881-4853

@article{ 10.5120/15881-4853,
author = { Sana Alyaseri, Alaa Aljanaby },
title = { Population based Heuristic Approaches for Grid Job Scheduling },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 91 },
number = { 5 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 45-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume91/number5/15881-4853/ },
doi = { 10.5120/15881-4853 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:12:00.796462+05:30
%A Sana Alyaseri
%A Alaa Aljanaby
%T Population based Heuristic Approaches for Grid Job Scheduling
%J International Journal of Computer Applications
%@ 0975-8887
%V 91
%N 5
%P 45-50
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Several strategies and approaches have been proposed to provide quality solutions for the grid job scheduling problem. Recently population based heuristics approaches are used widely to solve this problem. These approaches have demonstrated a surprising degree of effectiveness for handling combinatorial optimization problems. In this paper, the population based approaches for grid job scheduling have been studied. The focus was on investigating the criteria that help in the selection of the best scheduling algorithm for a certain type of grid and also shedding the light on how to improve the available population based approaches.

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Index Terms

Computer Science
Information Sciences

Keywords

Genetic algorithms Particle swarm optimization ant colony optimization artificial bee colony grid job scheduling.

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