An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments

Autores
Mateos Diaz, Cristian Maximiliano; Pacini Naumovich, Elina Rocío; Garcia Garino, Carlos Gabriel
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Parameter Sweep Experiments (PSEs) allow scientists and engineers to conduct experiments by running the same program code against different input data. This usually results in many jobs with high computational requirements. Thus, distributed environments, particularly Clouds, can be employed to fulfill these demands. However, job scheduling is challenging as it is an NP-complete problem. Recently, Cloud schedulers based on bio-inspired techniques-which work well in approximating problems with little input information-have been proposed. Unfortunately, existing proposals ignore job priorities, which is a very important aspect in PSEs since it allows accelerating PSE results processing and visualization in scientific Clouds. We present a new Cloud scheduler based on Ant Colony Optimization, the most popular bio-inspired technique, which also exploits well-known notions from operating systems theory. Simulated experiments performed with real PSE job data and other Cloud scheduling policies indicate that our proposal allows for a more agile job handling while reducing PSE completion time.
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
ANT COLONY OPTIMIZATION
CLOUD COMPUTING
JOB SCHEDULING
PARAMETER SWEEP EXPERIMENTS
SWARM INTELLIGENCE
WEIGHTED FLOWTIME
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/90999
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experimentsMateos Diaz, Cristian MaximilianoPacini Naumovich, Elina RocíoGarcia Garino, Carlos GabrielANT COLONY OPTIMIZATIONCLOUD COMPUTINGJOB SCHEDULINGPARAMETER SWEEP EXPERIMENTSSWARM INTELLIGENCEWEIGHTED FLOWTIMEhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Parameter Sweep Experiments (PSEs) allow scientists and engineers to conduct experiments by running the same program code against different input data. This usually results in many jobs with high computational requirements. Thus, distributed environments, particularly Clouds, can be employed to fulfill these demands. However, job scheduling is challenging as it is an NP-complete problem. Recently, Cloud schedulers based on bio-inspired techniques-which work well in approximating problems with little input information-have been proposed. Unfortunately, existing proposals ignore job priorities, which is a very important aspect in PSEs since it allows accelerating PSE results processing and visualization in scientific Clouds. We present a new Cloud scheduler based on Ant Colony Optimization, the most popular bio-inspired technique, which also exploits well-known notions from operating systems theory. Simulated experiments performed with real PSE job data and other Cloud scheduling policies indicate that our proposal allows for a more agile job handling while reducing PSE completion time.Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier2013-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/90999Mateos Diaz, Cristian Maximiliano; Pacini Naumovich, Elina Rocío; Garcia Garino, Carlos Gabriel; An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments; Elsevier; Advances in Engineering Software; 56; 2-2013; 38-500965-9978CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0965997812001585info:eu-repo/semantics/altIdentifier/doi/10.1016/j.advengsoft.2012.11.011info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:03:42Zoai:ri.conicet.gov.ar:11336/90999instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:03:42.657CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments
title An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments
spellingShingle An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments
Mateos Diaz, Cristian Maximiliano
ANT COLONY OPTIMIZATION
CLOUD COMPUTING
JOB SCHEDULING
PARAMETER SWEEP EXPERIMENTS
SWARM INTELLIGENCE
WEIGHTED FLOWTIME
title_short An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments
title_full An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments
title_fullStr An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments
title_full_unstemmed An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments
title_sort An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments
dc.creator.none.fl_str_mv Mateos Diaz, Cristian Maximiliano
Pacini Naumovich, Elina Rocío
Garcia Garino, Carlos Gabriel
author Mateos Diaz, Cristian Maximiliano
author_facet Mateos Diaz, Cristian Maximiliano
Pacini Naumovich, Elina Rocío
Garcia Garino, Carlos Gabriel
author_role author
author2 Pacini Naumovich, Elina Rocío
Garcia Garino, Carlos Gabriel
author2_role author
author
dc.subject.none.fl_str_mv ANT COLONY OPTIMIZATION
CLOUD COMPUTING
JOB SCHEDULING
PARAMETER SWEEP EXPERIMENTS
SWARM INTELLIGENCE
WEIGHTED FLOWTIME
topic ANT COLONY OPTIMIZATION
CLOUD COMPUTING
JOB SCHEDULING
PARAMETER SWEEP EXPERIMENTS
SWARM INTELLIGENCE
WEIGHTED FLOWTIME
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Parameter Sweep Experiments (PSEs) allow scientists and engineers to conduct experiments by running the same program code against different input data. This usually results in many jobs with high computational requirements. Thus, distributed environments, particularly Clouds, can be employed to fulfill these demands. However, job scheduling is challenging as it is an NP-complete problem. Recently, Cloud schedulers based on bio-inspired techniques-which work well in approximating problems with little input information-have been proposed. Unfortunately, existing proposals ignore job priorities, which is a very important aspect in PSEs since it allows accelerating PSE results processing and visualization in scientific Clouds. We present a new Cloud scheduler based on Ant Colony Optimization, the most popular bio-inspired technique, which also exploits well-known notions from operating systems theory. Simulated experiments performed with real PSE job data and other Cloud scheduling policies indicate that our proposal allows for a more agile job handling while reducing PSE completion time.
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Parameter Sweep Experiments (PSEs) allow scientists and engineers to conduct experiments by running the same program code against different input data. This usually results in many jobs with high computational requirements. Thus, distributed environments, particularly Clouds, can be employed to fulfill these demands. However, job scheduling is challenging as it is an NP-complete problem. Recently, Cloud schedulers based on bio-inspired techniques-which work well in approximating problems with little input information-have been proposed. Unfortunately, existing proposals ignore job priorities, which is a very important aspect in PSEs since it allows accelerating PSE results processing and visualization in scientific Clouds. We present a new Cloud scheduler based on Ant Colony Optimization, the most popular bio-inspired technique, which also exploits well-known notions from operating systems theory. Simulated experiments performed with real PSE job data and other Cloud scheduling policies indicate that our proposal allows for a more agile job handling while reducing PSE completion time.
publishDate 2013
dc.date.none.fl_str_mv 2013-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/90999
Mateos Diaz, Cristian Maximiliano; Pacini Naumovich, Elina Rocío; Garcia Garino, Carlos Gabriel; An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments; Elsevier; Advances in Engineering Software; 56; 2-2013; 38-50
0965-9978
CONICET Digital
CONICET
url http://hdl.handle.net/11336/90999
identifier_str_mv Mateos Diaz, Cristian Maximiliano; Pacini Naumovich, Elina Rocío; Garcia Garino, Carlos Gabriel; An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments; Elsevier; Advances in Engineering Software; 56; 2-2013; 38-50
0965-9978
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0965997812001585
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.advengsoft.2012.11.011
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269815262150656
score 13.13397

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