An improved evolutlonary approach for the cluster allocation problem
- Autores
- Apolloni, Rubén; Molina, Silvia; Gallard, Raúl Hector
- Año de publicación
- 1999
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- In a distributed system, consisting of a set of interconnected local area networks, users migrate to different machines, users invoke different programs and users and programs need distinct data files to satisfy their expectations. Consequently optimal allocation of parallel program tasks can increase system performance as results of traffic cost reducti9n between clusters2• The problem of allocating a program in a particular system node can be divided into two subproblems: i) allocate the program in a cluster such that traffic costs are minimized and ii) within a particular cluster choose the node following sorne load balancing criteria [5]. To solve subproblem i), in 1992 U. M. Borghoff [2] proposed the Individual Program Execution Location Algorithm IPELA, where essentially giving a distribution of data files the best allocation for program execution, minimizing the expected intercluster traffic, is searched. The algorithm uses diverse input data such us the cost for starting a program at sorne node [10], thedependencies between program and data files [1], separated read and write access costs [9], the impact of l/O activities on the communication costs [8] and the allocation of program and data files [3]. . As the number of possible allocations induce high complexity and the model could not be solved too optimality Borghoff reduced the number of combinations by limiting the number of data file replicas and looking for those combinations where the relevant file sets's allocation is varied. This approach reduced complexity. Nevertheless running ¡PELA implied evaluation of each solution in a large problem space.
Eje: Redes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
cluster allocation problem
Clustering
improved evolutlonary - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/22226
Ver los metadatos del registro completo
id |
SEDICI_c81c9ab770e9a7df1abb926a92c0dafd |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/22226 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
network_name_str |
SEDICI (UNLP) |
spelling |
An improved evolutlonary approach for the cluster allocation problemApolloni, RubénMolina, SilviaGallard, Raúl HectorCiencias InformáticasARTIFICIAL INTELLIGENCEcluster allocation problemClusteringimproved evolutlonaryIn a distributed system, consisting of a set of interconnected local area networks, users migrate to different machines, users invoke different programs and users and programs need distinct data files to satisfy their expectations. Consequently optimal allocation of parallel program tasks can increase system performance as results of traffic cost reducti9n between clusters2• The problem of allocating a program in a particular system node can be divided into two subproblems: i) allocate the program in a cluster such that traffic costs are minimized and ii) within a particular cluster choose the node following sorne load balancing criteria [5]. To solve subproblem i), in 1992 U. M. Borghoff [2] proposed the Individual Program Execution Location Algorithm IPELA, where essentially giving a distribution of data files the best allocation for program execution, minimizing the expected intercluster traffic, is searched. The algorithm uses diverse input data such us the cost for starting a program at sorne node [10], thedependencies between program and data files [1], separated read and write access costs [9], the impact of l/O activities on the communication costs [8] and the allocation of program and data files [3]. . As the number of possible allocations induce high complexity and the model could not be solved too optimality Borghoff reduced the number of combinations by limiting the number of data file replicas and looking for those combinations where the relevant file sets's allocation is varied. This approach reduced complexity. Nevertheless running ¡PELA implied evaluation of each solution in a large problem space.Eje: Redes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)1999-05info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/22226enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:54:57Zoai:sedici.unlp.edu.ar:10915/22226Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:54:58.045SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
An improved evolutlonary approach for the cluster allocation problem |
title |
An improved evolutlonary approach for the cluster allocation problem |
spellingShingle |
An improved evolutlonary approach for the cluster allocation problem Apolloni, Rubén Ciencias Informáticas ARTIFICIAL INTELLIGENCE cluster allocation problem Clustering improved evolutlonary |
title_short |
An improved evolutlonary approach for the cluster allocation problem |
title_full |
An improved evolutlonary approach for the cluster allocation problem |
title_fullStr |
An improved evolutlonary approach for the cluster allocation problem |
title_full_unstemmed |
An improved evolutlonary approach for the cluster allocation problem |
title_sort |
An improved evolutlonary approach for the cluster allocation problem |
dc.creator.none.fl_str_mv |
Apolloni, Rubén Molina, Silvia Gallard, Raúl Hector |
author |
Apolloni, Rubén |
author_facet |
Apolloni, Rubén Molina, Silvia Gallard, Raúl Hector |
author_role |
author |
author2 |
Molina, Silvia Gallard, Raúl Hector |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas ARTIFICIAL INTELLIGENCE cluster allocation problem Clustering improved evolutlonary |
topic |
Ciencias Informáticas ARTIFICIAL INTELLIGENCE cluster allocation problem Clustering improved evolutlonary |
dc.description.none.fl_txt_mv |
In a distributed system, consisting of a set of interconnected local area networks, users migrate to different machines, users invoke different programs and users and programs need distinct data files to satisfy their expectations. Consequently optimal allocation of parallel program tasks can increase system performance as results of traffic cost reducti9n between clusters2• The problem of allocating a program in a particular system node can be divided into two subproblems: i) allocate the program in a cluster such that traffic costs are minimized and ii) within a particular cluster choose the node following sorne load balancing criteria [5]. To solve subproblem i), in 1992 U. M. Borghoff [2] proposed the Individual Program Execution Location Algorithm IPELA, where essentially giving a distribution of data files the best allocation for program execution, minimizing the expected intercluster traffic, is searched. The algorithm uses diverse input data such us the cost for starting a program at sorne node [10], thedependencies between program and data files [1], separated read and write access costs [9], the impact of l/O activities on the communication costs [8] and the allocation of program and data files [3]. . As the number of possible allocations induce high complexity and the model could not be solved too optimality Borghoff reduced the number of combinations by limiting the number of data file replicas and looking for those combinations where the relevant file sets's allocation is varied. This approach reduced complexity. Nevertheless running ¡PELA implied evaluation of each solution in a large problem space. Eje: Redes y sistemas inteligentes Red de Universidades con Carreras en Informática (RedUNCI) |
description |
In a distributed system, consisting of a set of interconnected local area networks, users migrate to different machines, users invoke different programs and users and programs need distinct data files to satisfy their expectations. Consequently optimal allocation of parallel program tasks can increase system performance as results of traffic cost reducti9n between clusters2• The problem of allocating a program in a particular system node can be divided into two subproblems: i) allocate the program in a cluster such that traffic costs are minimized and ii) within a particular cluster choose the node following sorne load balancing criteria [5]. To solve subproblem i), in 1992 U. M. Borghoff [2] proposed the Individual Program Execution Location Algorithm IPELA, where essentially giving a distribution of data files the best allocation for program execution, minimizing the expected intercluster traffic, is searched. The algorithm uses diverse input data such us the cost for starting a program at sorne node [10], thedependencies between program and data files [1], separated read and write access costs [9], the impact of l/O activities on the communication costs [8] and the allocation of program and data files [3]. . As the number of possible allocations induce high complexity and the model could not be solved too optimality Borghoff reduced the number of combinations by limiting the number of data file replicas and looking for those combinations where the relevant file sets's allocation is varied. This approach reduced complexity. Nevertheless running ¡PELA implied evaluation of each solution in a large problem space. |
publishDate |
1999 |
dc.date.none.fl_str_mv |
1999-05 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/22226 |
url |
http://sedici.unlp.edu.ar/handle/10915/22226 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
reponame_str |
SEDICI (UNLP) |
collection |
SEDICI (UNLP) |
instname_str |
Universidad Nacional de La Plata |
instacron_str |
UNLP |
institution |
UNLP |
repository.name.fl_str_mv |
SEDICI (UNLP) - Universidad Nacional de La Plata |
repository.mail.fl_str_mv |
alira@sedici.unlp.edu.ar |
_version_ |
1844615807874629632 |
score |
13.070432 |