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Multi-Objective Job Scheduler using Genetic Algorithm in Grid Computing

by Pritibahen Sumanbhai Patel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 92 - Number 14
Year of Publication: 2014
Authors: Pritibahen Sumanbhai Patel
10.5120/16079-5312

Pritibahen Sumanbhai Patel . Multi-Objective Job Scheduler using Genetic Algorithm in Grid Computing. International Journal of Computer Applications. 92, 14 ( April 2014), 34-43. DOI=10.5120/16079-5312

@article{ 10.5120/16079-5312,
author = { Pritibahen Sumanbhai Patel },
title = { Multi-Objective Job Scheduler using Genetic Algorithm in Grid Computing },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 92 },
number = { 14 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 34-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume92/number14/16079-5312/ },
doi = { 10.5120/16079-5312 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:14:20.303889+05:30
%A Pritibahen Sumanbhai Patel
%T Multi-Objective Job Scheduler using Genetic Algorithm in Grid Computing
%J International Journal of Computer Applications
%@ 0975-8887
%V 92
%N 14
%P 34-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents multi-objective Job scheduler using Genetic Algorithm which provides efficient utilization of resources by completing the different tasks in a minimum period of time. Grid is a kind of distributed system that provides the sharing of geographically distributed independent resources dynamically at runtime depending on their availability, capability, performance and cost. Scheduling is a key problem in evolving grid computational systems. Dealing with the multiple criteria in a heterogeneous and dynamic environment like Grid is very complex and computationally hard. There are ample approaches for Job scheduling like Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony optimization (ACO) and Particle Swarm Optimization (PSO) Algorithm. This paper presents Genetic algorithm for designing efficient multi-objective job schedulers by considering multiple parameter like makespan and flow time to find optimal/nearly optimal schedule. It searches solution space in parallel and solution can be found more quickly.

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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm (GA) Scheduler Makespan Minimum completion time Fitness Flow Time.

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