Maximizing availability for task scheduling in computational grid using genetic algorithm

SUMMARY Computational grid provides a wide distributed platform for high‐end compute intensive applications. Grid scheduling is often carried out to schedule the submitted jobs on the nodes of the grid so that some characteristic parameter is optimized. Availability of the computational nodes is one...

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Vydáno v:Concurrency and computation Ročník 27; číslo 1; s. 193 - 210
Hlavní autoři: Prakash, Shiv, Vidyarthi, Deo Prakash
Médium: Journal Article
Jazyk:angličtina
Vydáno: Blackwell Publishing Ltd 01.01.2015
Témata:
Availability
Computation
Computational grids
genetic algorithm
Genetic algorithms
Heuristic methods
load
Mathematical models
MTTF
MTTR
processing rate
Scheduling
Task scheduling
ISSN:1532-0626, 1532-0634
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Shrnutí:SUMMARY Computational grid provides a wide distributed platform for high‐end compute intensive applications. Grid scheduling is often carried out to schedule the submitted jobs on the nodes of the grid so that some characteristic parameter is optimized. Availability of the computational nodes is one of the important characteristic parameters and measures the probability of the node availability for job execution. This paper addresses the availability of the grid computational nodes for the job execution and proposes a model to maximize it. As such, the task scheduling problem in grid is nondeterministic polynomial‐time hard, and often, metaheuristics techniques are applied to solve it. Genetic algorithm, a metaheuristic technique based on evolutionary computation, has been used to solve such complex optimization problem. This work proposes a technique for the grid scheduling problem using genetic algorithm with the objective to maximize availability. Simulation experiment, to evaluate the performance of the proposed algorithm, is conducted, and results reveal the effectiveness of the model. A comparative study has also been performed. Copyright © 2014 John Wiley & Sons, Ltd.
Bibliografie:ArticleID:CPE3216
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ark:/67375/WNG-XXK9X824-3
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SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.3216