Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)

Autores
Pacini Naumovich, Elina Rocío; Mateos Diaz, Cristian Maximiliano; Garcia Garino, Carlos Gabriel
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The Cloud Computing paradigm focuses on the provisioning of reliable and scalable infrastructures (Clouds) delivering execution and storage services. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. The goal of this work is to study private Clouds to execute scientific experiments coming from multiple users, i.e., our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate hosts available in a Cloud. Then, correctly scheduling Cloud hosts is very important and it is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. The job scheduling problem is however NP-complete, and therefore many heuristics have been developed. In this work, we describe and evaluate a Cloud scheduler based on Ant Colony Optimization (ACO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performed using CloudSim and job data from real scientific problems show that our scheduler succeeds in balancing the studied metrics compared to schedulers based on Random assignment and Genetic Algorithms.
Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria; Argentina
Materia
Cloud Computing
Scientific Problems
Job Scheduling
Swarm Intelligence
Ant Colony Optimization
Genetic Algorithms
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:11336/6829
Acceder
id CONICETDig_873dc5bc5d70b5a6ee28e221f7e413ac
oai_identifier_str oai:ri.conicet.gov.ar:11336/6829
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)Pacini Naumovich, Elina RocíoMateos Diaz, Cristian MaximilianoGarcia Garino, Carlos GabrielCloud ComputingScientific ProblemsJob SchedulingSwarm IntelligenceAnt Colony OptimizationGenetic Algorithmshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The Cloud Computing paradigm focuses on the provisioning of reliable and scalable infrastructures (Clouds) delivering execution and storage services. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. The goal of this work is to study private Clouds to execute scientific experiments coming from multiple users, i.e., our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate hosts available in a Cloud. Then, correctly scheduling Cloud hosts is very important and it is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. The job scheduling problem is however NP-complete, and therefore many heuristics have been developed. In this work, we describe and evaluate a Cloud scheduler based on Ant Colony Optimization (ACO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performed using CloudSim and job data from real scientific problems show that our scheduler succeeds in balancing the studied metrics compared to schedulers based on Random assignment and Genetic Algorithms.Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria; ArgentinaElsevier2015-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/zipapplication/pdfapplication/zipapplication/pdfhttp://hdl.handle.net/11336/6829Pacini Naumovich, Elina Rocío; Mateos Diaz, Cristian Maximiliano; Garcia Garino, Carlos Gabriel; Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006); Elsevier; Advances In Engineering Software; 84; 6-2015; 31-470965-9978enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S096599781500006Xinfo:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/altIdentifier/doi/10.1016/j.advengsoft.2015.01.005info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:00:21Zoai:ri.conicet.gov.ar:11336/6829instacron: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 10:00:21.365CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)
title Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)
spellingShingle Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)
Pacini Naumovich, Elina Rocío
Cloud Computing
Scientific Problems
Job Scheduling
Swarm Intelligence
Ant Colony Optimization
Genetic Algorithms
title_short Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)
title_full Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)
title_fullStr Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)
title_full_unstemmed Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)
title_sort Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)
dc.creator.none.fl_str_mv Pacini Naumovich, Elina Rocío
Mateos Diaz, Cristian Maximiliano
Garcia Garino, Carlos Gabriel
author Pacini Naumovich, Elina Rocío
author_facet Pacini Naumovich, Elina Rocío
Mateos Diaz, Cristian Maximiliano
Garcia Garino, Carlos Gabriel
author_role author
author2 Mateos Diaz, Cristian Maximiliano
Garcia Garino, Carlos Gabriel
author2_role author
author
dc.subject.none.fl_str_mv Cloud Computing
Scientific Problems
Job Scheduling
Swarm Intelligence
Ant Colony Optimization
Genetic Algorithms
topic Cloud Computing
Scientific Problems
Job Scheduling
Swarm Intelligence
Ant Colony Optimization
Genetic Algorithms
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The Cloud Computing paradigm focuses on the provisioning of reliable and scalable infrastructures (Clouds) delivering execution and storage services. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. The goal of this work is to study private Clouds to execute scientific experiments coming from multiple users, i.e., our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate hosts available in a Cloud. Then, correctly scheduling Cloud hosts is very important and it is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. The job scheduling problem is however NP-complete, and therefore many heuristics have been developed. In this work, we describe and evaluate a Cloud scheduler based on Ant Colony Optimization (ACO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performed using CloudSim and job data from real scientific problems show that our scheduler succeeds in balancing the studied metrics compared to schedulers based on Random assignment and Genetic Algorithms.
Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria; Argentina
description The Cloud Computing paradigm focuses on the provisioning of reliable and scalable infrastructures (Clouds) delivering execution and storage services. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. The goal of this work is to study private Clouds to execute scientific experiments coming from multiple users, i.e., our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate hosts available in a Cloud. Then, correctly scheduling Cloud hosts is very important and it is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. The job scheduling problem is however NP-complete, and therefore many heuristics have been developed. In this work, we describe and evaluate a Cloud scheduler based on Ant Colony Optimization (ACO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performed using CloudSim and job data from real scientific problems show that our scheduler succeeds in balancing the studied metrics compared to schedulers based on Random assignment and Genetic Algorithms.
publishDate 2015
dc.date.none.fl_str_mv 2015-06
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/11336/6829
Pacini Naumovich, Elina Rocío; Mateos Diaz, Cristian Maximiliano; Garcia Garino, Carlos Gabriel; Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006); Elsevier; Advances In Engineering Software; 84; 6-2015; 31-47
0965-9978
url http://hdl.handle.net/11336/6829
identifier_str_mv Pacini Naumovich, Elina Rocío; Mateos Diaz, Cristian Maximiliano; Garcia Garino, Carlos Gabriel; Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006); Elsevier; Advances In Engineering Software; 84; 6-2015; 31-47
0965-9978
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S096599781500006X
info:eu-repo/semantics/altIdentifier/doi/
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.advengsoft.2015.01.005
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/zip
application/pdf
application/zip
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_ 1842269633643544576
score 13.13397

Publicaciones similares