Applying the Heterogeneity Level Metric in a Distributed Platform
Autor(a) principal: | |
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Data de Publicação: | 2011 |
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/329 |
Resumo: | Heterogeneity Level (HL) metric has been developed by our research-group to help scheduling algorithms to adapt themselves to the existent heterogeneity in the platforms. This paper presents our results considering the HL’s behaviour in a real adaptive scheduling. HL metric quantifies qualitative aspects from heterogeneity in order to provide efficient performances and lower cost to the execution in both heterogeneous and homogeneous platforms. HL use is investigated under different perspectives:CPU, memory, network and considering benchmarks results. A simple but effective adaptive scheduling using HL is proposed and its results point out to performance-gains around 53% when a non-adaptive scheduling algorithm is used. Our case studies show that the HL was efficient, flexible and easily used for scheduling policies. |
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INFOCOMP: Jornal de Ciência da Computação |
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Applying the Heterogeneity Level Metric in a Distributed Platformeterogeneityload balan cingcluster.Heterogeneity Level (HL) metric has been developed by our research-group to help scheduling algorithms to adapt themselves to the existent heterogeneity in the platforms. This paper presents our results considering the HL’s behaviour in a real adaptive scheduling. HL metric quantifies qualitative aspects from heterogeneity in order to provide efficient performances and lower cost to the execution in both heterogeneous and homogeneous platforms. HL use is investigated under different perspectives:CPU, memory, network and considering benchmarks results. A simple but effective adaptive scheduling using HL is proposed and its results point out to performance-gains around 53% when a non-adaptive scheduling algorithm is used. Our case studies show that the HL was efficient, flexible and easily used for scheduling policies.Editora da UFLA2011-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/329INFOCOMP Journal of Computer Science; Vol. 10 No. 2 (2011): June, 2011; 17-251982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/329/313Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessSouza, Paulo S. L.Histoshi, FabioSantana, Marcos J.Santana, Regina H. C.Bruschi, Sarita M.Branco, Kalinka R. L. J. C.2015-07-29T11:56:47Zoai:infocomp.dcc.ufla.br:article/329Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:32.383451INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
Applying the Heterogeneity Level Metric in a Distributed Platform |
title |
Applying the Heterogeneity Level Metric in a Distributed Platform |
spellingShingle |
Applying the Heterogeneity Level Metric in a Distributed Platform Souza, Paulo S. L. eterogeneity load balan cing cluster. |
title_short |
Applying the Heterogeneity Level Metric in a Distributed Platform |
title_full |
Applying the Heterogeneity Level Metric in a Distributed Platform |
title_fullStr |
Applying the Heterogeneity Level Metric in a Distributed Platform |
title_full_unstemmed |
Applying the Heterogeneity Level Metric in a Distributed Platform |
title_sort |
Applying the Heterogeneity Level Metric in a Distributed Platform |
author |
Souza, Paulo S. L. |
author_facet |
Souza, Paulo S. L. Histoshi, Fabio Santana, Marcos J. Santana, Regina H. C. Bruschi, Sarita M. Branco, Kalinka R. L. J. C. |
author_role |
author |
author2 |
Histoshi, Fabio Santana, Marcos J. Santana, Regina H. C. Bruschi, Sarita M. Branco, Kalinka R. L. J. C. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Souza, Paulo S. L. Histoshi, Fabio Santana, Marcos J. Santana, Regina H. C. Bruschi, Sarita M. Branco, Kalinka R. L. J. C. |
dc.subject.por.fl_str_mv |
eterogeneity load balan cing cluster. |
topic |
eterogeneity load balan cing cluster. |
description |
Heterogeneity Level (HL) metric has been developed by our research-group to help scheduling algorithms to adapt themselves to the existent heterogeneity in the platforms. This paper presents our results considering the HL’s behaviour in a real adaptive scheduling. HL metric quantifies qualitative aspects from heterogeneity in order to provide efficient performances and lower cost to the execution in both heterogeneous and homogeneous platforms. HL use is investigated under different perspectives:CPU, memory, network and considering benchmarks results. A simple but effective adaptive scheduling using HL is proposed and its results point out to performance-gains around 53% when a non-adaptive scheduling algorithm is used. Our case studies show that the HL was efficient, flexible and easily used for scheduling policies. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-06-01 |
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/329 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/329 |
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/329/313 |
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. 10 No. 2 (2011): June, 2011; 17-25 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_ |
1799874741373239296 |