Job scheduling approaches based on firefly algorithm for computational grid

Computational Grid emerged to satisfy the rising demand for bandwidth, storage, and computational resources. Job Scheduling on computational grids is identified as NP-hard problem due to the heterogeneity of grid resources. Numerous researches have applied metaheuristics to find polynomial times for...

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Main Author:	Aboalgassim Alfaki, Adil Yousif
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK7885-7895 Computer engineer Computer hardware
Online Access:	http://eprints.utm.my/id/eprint/38953/5/AdilYousifAboalgassimPFSKSM2013.pdf
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id	my-utm-ep.38953
record_format	uketd_dc
spelling	my-utm-ep.389532017-07-19T07:26:28Z Job scheduling approaches based on firefly algorithm for computational grid 2013-04 Aboalgassim Alfaki, Adil Yousif TK7885-7895 Computer engineer. Computer hardware Computational Grid emerged to satisfy the rising demand for bandwidth, storage, and computational resources. Job Scheduling on computational grids is identified as NP-hard problem due to the heterogeneity of grid resources. Numerous researches have applied metaheuristics to find polynomial times for the job scheduling problem. These metaheuristics generated good but not optimal schedules. The current metaheuristics suffer from several limitations that cause long makespan time and flowtime. The aim of this research is to design and implement grid job scheduling approaches to map clients’ jobs to the available resources in order to finish the submitted jobs within the optimal makespan time and flowtime. This research presents novel static, hybrid static and dynamic metaheuristics approaches based on Firefly Algorithm for grid job scheduling. Based on the review of the available literature, Firefly Algorithm has yet to be applied in the job scheduling on computational grid. Experiments using simulations and real workload traces were conducted to study the performance of the proposed scheduling approaches. Empirical results revealed that the proposed scheduling approaches outperform other scheduling approaches in the case of typical and heavy workloads in terms of both makespan time and flowtime. The average improvement ratios achieved by the static, hybrid static and dynamic scheduling approaches over Genetic Algorithm in the case of makespan time were 23%, 32% and 28% respectively for typical workloads, and 51%, 59% and 42% for heavy workloads. In the case of flowtime, the average improvement ratios were 62%, 81 % and 21% respectively for typical workloads, and 40%, 58% and 57% for heavy workloads. 2013-04 Thesis http://eprints.utm.my/id/eprint/38953/ http://eprints.utm.my/id/eprint/38953/5/AdilYousifAboalgassimPFSKSM2013.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK7885-7895 Computer engineer Computer hardware
spellingShingle	TK7885-7895 Computer engineer Computer hardware Aboalgassim Alfaki, Adil Yousif Job scheduling approaches based on firefly algorithm for computational grid
description	Computational Grid emerged to satisfy the rising demand for bandwidth, storage, and computational resources. Job Scheduling on computational grids is identified as NP-hard problem due to the heterogeneity of grid resources. Numerous researches have applied metaheuristics to find polynomial times for the job scheduling problem. These metaheuristics generated good but not optimal schedules. The current metaheuristics suffer from several limitations that cause long makespan time and flowtime. The aim of this research is to design and implement grid job scheduling approaches to map clients’ jobs to the available resources in order to finish the submitted jobs within the optimal makespan time and flowtime. This research presents novel static, hybrid static and dynamic metaheuristics approaches based on Firefly Algorithm for grid job scheduling. Based on the review of the available literature, Firefly Algorithm has yet to be applied in the job scheduling on computational grid. Experiments using simulations and real workload traces were conducted to study the performance of the proposed scheduling approaches. Empirical results revealed that the proposed scheduling approaches outperform other scheduling approaches in the case of typical and heavy workloads in terms of both makespan time and flowtime. The average improvement ratios achieved by the static, hybrid static and dynamic scheduling approaches over Genetic Algorithm in the case of makespan time were 23%, 32% and 28% respectively for typical workloads, and 51%, 59% and 42% for heavy workloads. In the case of flowtime, the average improvement ratios were 62%, 81 % and 21% respectively for typical workloads, and 40%, 58% and 57% for heavy workloads.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Aboalgassim Alfaki, Adil Yousif
author_facet	Aboalgassim Alfaki, Adil Yousif
author_sort	Aboalgassim Alfaki, Adil Yousif
title	Job scheduling approaches based on firefly algorithm for computational grid
title_short	Job scheduling approaches based on firefly algorithm for computational grid
title_full	Job scheduling approaches based on firefly algorithm for computational grid
title_fullStr	Job scheduling approaches based on firefly algorithm for computational grid
title_full_unstemmed	Job scheduling approaches based on firefly algorithm for computational grid
title_sort	job scheduling approaches based on firefly algorithm for computational grid
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/38953/5/AdilYousifAboalgassimPFSKSM2013.pdf
_version_	1747816533862645760

Job scheduling approaches based on firefly algorithm for computational grid

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