Markovian approximations for a grid computing network with a ring structure

Optical grid networks allow many computing sites to share their resources by connecting them through high-speed links, providing a more efficient use of the resources and a timely response for incoming jobs. These jobs originate from users connected to each of the sites and, in contrast to tradition...

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Autores:
Tipo de recurso:
Fecha de publicación:
2010
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/28113
Acceso en línea:
https://doi.org/10.1080/15326349.2010.498315
https://repository.urosario.edu.co/handle/10336/28113
Palabra clave:
Markov chains
Optical grids
Queueing networks
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License
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id EDOCUR2_c8b5175d75174f6923a68bd8b5d262ba
oai_identifier_str oai:repository.urosario.edu.co:10336/28113
network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
spelling 800352026009434e46a-0bd9-48dd-af17-71c10f74be0b2020-08-19T14:45:51Z2020-08-19T14:45:51Z2010-08-04Optical grid networks allow many computing sites to share their resources by connecting them through high-speed links, providing a more efficient use of the resources and a timely response for incoming jobs. These jobs originate from users connected to each of the sites and, in contrast to traditional queueing networks, a particular job does not have to be processed in a predefined site. Furthermore, a job is always processed locally if there is an available local server. In this paper we propose two different methods to approximate the performance of an optical grid network with a ring topology. The first method is based on approximating the inter-overflow time process, while the second separately characterizes the periods where jobs are overflowed and the periods where they are served locally. Both approaches rely on a marked Markovian representation of the overflow process at each station and on reducing this representation by moment-matching methods. The results show that the methods accurately approximate the rate of locally processed jobs, one of the main performance measures.application/pdfhttps://doi.org/10.1080/15326349.2010.498315ISSN: 1532-6349EISSN: 1532-4214https://repository.urosario.edu.co/handle/10336/28113engThe Institute for Operations Research and the Management SciencesTaylor & Francis383No. 3357Stochastic ModelsVol. 26Stochastic Models, ISSN: 1532-6349;EISSN: 1532-4214, Vol.26, No.3 (2010); pp. 357–383https://www.tandfonline.com/doi/abs/10.1080/15326349.2010.49831Restringido (Acceso a grupos específicos)http://purl.org/coar/access_right/c_16ecStochastic Modelsinstname:Universidad del Rosarioreponame:Repositorio Institucional EdocURMarkov chainsOptical gridsQueueing networksMarkovian approximations for a grid computing network with a ring structureAproximaciones de Markov para una red informática en cuadrícula con estructura de anilloarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Pérez, Juan F.Van Houdt, B.10336/28113oai:repository.urosario.edu.co:10336/281132021-09-23 12:28:08.421https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co
dc.title.spa.fl_str_mv Markovian approximations for a grid computing network with a ring structure
dc.title.TranslatedTitle.spa.fl_str_mv Aproximaciones de Markov para una red informática en cuadrícula con estructura de anillo
title Markovian approximations for a grid computing network with a ring structure
spellingShingle Markovian approximations for a grid computing network with a ring structure
Markov chains
Optical grids
Queueing networks
title_short Markovian approximations for a grid computing network with a ring structure
title_full Markovian approximations for a grid computing network with a ring structure
title_fullStr Markovian approximations for a grid computing network with a ring structure
title_full_unstemmed Markovian approximations for a grid computing network with a ring structure
title_sort Markovian approximations for a grid computing network with a ring structure
dc.subject.keyword.spa.fl_str_mv Markov chains
Optical grids
Queueing networks
topic Markov chains
Optical grids
Queueing networks
description Optical grid networks allow many computing sites to share their resources by connecting them through high-speed links, providing a more efficient use of the resources and a timely response for incoming jobs. These jobs originate from users connected to each of the sites and, in contrast to traditional queueing networks, a particular job does not have to be processed in a predefined site. Furthermore, a job is always processed locally if there is an available local server. In this paper we propose two different methods to approximate the performance of an optical grid network with a ring topology. The first method is based on approximating the inter-overflow time process, while the second separately characterizes the periods where jobs are overflowed and the periods where they are served locally. Both approaches rely on a marked Markovian representation of the overflow process at each station and on reducing this representation by moment-matching methods. The results show that the methods accurately approximate the rate of locally processed jobs, one of the main performance measures.
publishDate 2010
dc.date.created.spa.fl_str_mv 2010-08-04
dc.date.accessioned.none.fl_str_mv 2020-08-19T14:45:51Z
dc.date.available.none.fl_str_mv 2020-08-19T14:45:51Z
dc.type.eng.fl_str_mv article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.spa.spa.fl_str_mv Artículo
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1080/15326349.2010.498315
dc.identifier.issn.none.fl_str_mv ISSN: 1532-6349
EISSN: 1532-4214
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/28113
url https://doi.org/10.1080/15326349.2010.498315
https://repository.urosario.edu.co/handle/10336/28113
identifier_str_mv ISSN: 1532-6349
EISSN: 1532-4214
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationEndPage.none.fl_str_mv 383
dc.relation.citationIssue.none.fl_str_mv No. 3
dc.relation.citationStartPage.none.fl_str_mv 357
dc.relation.citationTitle.none.fl_str_mv Stochastic Models
dc.relation.citationVolume.none.fl_str_mv Vol. 26
dc.relation.ispartof.spa.fl_str_mv Stochastic Models, ISSN: 1532-6349;EISSN: 1532-4214, Vol.26, No.3 (2010); pp. 357–383
dc.relation.uri.spa.fl_str_mv https://www.tandfonline.com/doi/abs/10.1080/15326349.2010.49831
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.rights.acceso.spa.fl_str_mv Restringido (Acceso a grupos específicos)
rights_invalid_str_mv Restringido (Acceso a grupos específicos)
http://purl.org/coar/access_right/c_16ec
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv The Institute for Operations Research and the Management Sciences
Taylor & Francis
dc.source.spa.fl_str_mv Stochastic Models
institution Universidad del Rosario
dc.source.instname.none.fl_str_mv instname:Universidad del Rosario
dc.source.reponame.none.fl_str_mv reponame:Repositorio Institucional EdocUR
repository.name.fl_str_mv Repositorio institucional EdocUR
repository.mail.fl_str_mv edocur@urosario.edu.co
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