A novel Task Scheduling in Multiprocessor Systems with Genetic Algorithm by using Elitism stepping method
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/218 |
Resumo: | Task scheduling is essential for the suitable operation of multiprocessor systems. The aim of task scheduling is to determine an assignment of tasks to processors for shortening the length of schedules. The problem of task scheduling on multiprocessor systems is known to be NP-complete in general. Solving this problem using by conventional techniques needs reasonable amounts of time. Therefore, many heuristic techniques were introduced for solving it. This paper presents a new heuristic algorithm for task scheduling, based on evolutionary method which embeds a new fast technique named Elitism Stepping into Genetic Algorithm (GA). By comparing the proposed algorithm with an existing GA-based algorithm, it is found that the computation time of the new algorithm to find a sub-optimal schedule is decreased; however, the length of schedule or the finish time is decreased too. |
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INFOCOMP: Jornal de Ciência da Computação |
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A novel Task Scheduling in Multiprocessor Systems with Genetic Algorithm by using Elitism stepping methodTask schedulingMultiprocessor SystemsGenetic AlgorithmElitism Stepping.Task scheduling is essential for the suitable operation of multiprocessor systems. The aim of task scheduling is to determine an assignment of tasks to processors for shortening the length of schedules. The problem of task scheduling on multiprocessor systems is known to be NP-complete in general. Solving this problem using by conventional techniques needs reasonable amounts of time. Therefore, many heuristic techniques were introduced for solving it. This paper presents a new heuristic algorithm for task scheduling, based on evolutionary method which embeds a new fast technique named Elitism Stepping into Genetic Algorithm (GA). By comparing the proposed algorithm with an existing GA-based algorithm, it is found that the computation time of the new algorithm to find a sub-optimal schedule is decreased; however, the length of schedule or the finish time is decreased too.Editora da UFLA2008-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/218INFOCOMP Journal of Computer Science; Vol. 7 No. 2 (2008): June, 2008; 58-641982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/218/203Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessRahmani, Amir MasoudAli Vahedi, Mohammad2015-06-27T23:52:42Zoai:infocomp.dcc.ufla.br:article/218Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:25.240044INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
A novel Task Scheduling in Multiprocessor Systems with Genetic Algorithm by using Elitism stepping method |
title |
A novel Task Scheduling in Multiprocessor Systems with Genetic Algorithm by using Elitism stepping method |
spellingShingle |
A novel Task Scheduling in Multiprocessor Systems with Genetic Algorithm by using Elitism stepping method Rahmani, Amir Masoud Task scheduling Multiprocessor Systems Genetic Algorithm Elitism Stepping. |
title_short |
A novel Task Scheduling in Multiprocessor Systems with Genetic Algorithm by using Elitism stepping method |
title_full |
A novel Task Scheduling in Multiprocessor Systems with Genetic Algorithm by using Elitism stepping method |
title_fullStr |
A novel Task Scheduling in Multiprocessor Systems with Genetic Algorithm by using Elitism stepping method |
title_full_unstemmed |
A novel Task Scheduling in Multiprocessor Systems with Genetic Algorithm by using Elitism stepping method |
title_sort |
A novel Task Scheduling in Multiprocessor Systems with Genetic Algorithm by using Elitism stepping method |
author |
Rahmani, Amir Masoud |
author_facet |
Rahmani, Amir Masoud Ali Vahedi, Mohammad |
author_role |
author |
author2 |
Ali Vahedi, Mohammad |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Rahmani, Amir Masoud Ali Vahedi, Mohammad |
dc.subject.por.fl_str_mv |
Task scheduling Multiprocessor Systems Genetic Algorithm Elitism Stepping. |
topic |
Task scheduling Multiprocessor Systems Genetic Algorithm Elitism Stepping. |
description |
Task scheduling is essential for the suitable operation of multiprocessor systems. The aim of task scheduling is to determine an assignment of tasks to processors for shortening the length of schedules. The problem of task scheduling on multiprocessor systems is known to be NP-complete in general. Solving this problem using by conventional techniques needs reasonable amounts of time. Therefore, many heuristic techniques were introduced for solving it. This paper presents a new heuristic algorithm for task scheduling, based on evolutionary method which embeds a new fast technique named Elitism Stepping into Genetic Algorithm (GA). By comparing the proposed algorithm with an existing GA-based algorithm, it is found that the computation time of the new algorithm to find a sub-optimal schedule is decreased; however, the length of schedule or the finish time is decreased too. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/218 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/218 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/218/203 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 7 No. 2 (2008): June, 2008; 58-64 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874740815396864 |