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Job Scheduling in Grid Computing with Cuckoo Optimization Algorithm

by Maryam Rabiee, Hedieh Sajedi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 16
Year of Publication: 2013
Authors: Maryam Rabiee, Hedieh Sajedi
10.5120/10168-5076

Maryam Rabiee, Hedieh Sajedi . Job Scheduling in Grid Computing with Cuckoo Optimization Algorithm. International Journal of Computer Applications. 62, 16 ( January 2013), 38-44. DOI=10.5120/10168-5076

@article{ 10.5120/10168-5076,
author = { Maryam Rabiee, Hedieh Sajedi },
title = { Job Scheduling in Grid Computing with Cuckoo Optimization Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 16 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 38-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number16/10168-5076/ },
doi = { 10.5120/10168-5076 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:12:01.934585+05:30
%A Maryam Rabiee
%A Hedieh Sajedi
%T Job Scheduling in Grid Computing with Cuckoo Optimization Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 16
%P 38-44
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Computational grid is a hardware and software infrastructure that provides dependable, inclusive and credible to other computing capabilities. Grid computing intercommunicated with a set of computational resources on a large scale. Scheduling independent jobs is an important issues in such areas as computational grid. Scheduling is the process of assigning jobs to resources in order to achieve different goals. The grid schedule, find the optimal resource allocation to it over heterogeneous resources and maximize overall system performance. As yet evolutionary methods such as Genetic, Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to solve the problem in the grid schedule has been adopted. The disadvantage of these techniques premature convergence and trapping in local optimum in large-scale problems. In this paper, a method by Cuckoo Optimization Algorithm (COA) to solve job scheduling in grids computational design, implementation and results are presented. The results show our proposed schedule have more efficient and better performing compared with Genetic and Particle Swarm Optimization.

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Index Terms

Computer Science
Information Sciences

Keywords

Grid computing job scheduling Cuckoo Optimization Genetic algorithm Particle Swarm Optimization

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