A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud
- Autores
- Yannibelli, Virginia Daniela; Pacini Naumovich, Elina Rocío; Monge, David; Mateos Diaz, Cristian Maximiliano; Rodríguez, Guillermo Horacio
- Año de publicación
- 2020
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.
Fil: Yannibelli, Virginia Daniela. 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; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Monge, David. Universidad Nacional de Cuyo; Argentina
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: Rodríguez, Guillermo Horacio. 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 - Materia
-
AUTOSCALING
CLOUD COMPUTING
METAHEURISTICS - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/138197
Ver los metadatos del registro completo
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A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloudYannibelli, Virginia DanielaPacini Naumovich, Elina RocíoMonge, DavidMateos Diaz, Cristian MaximilianoRodríguez, Guillermo HoracioAUTOSCALINGCLOUD COMPUTINGMETAHEURISTICShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.Fil: Yannibelli, Virginia Daniela. 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; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Monge, David. Universidad Nacional de Cuyo; ArgentinaFil: 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: Rodríguez, Guillermo Horacio. 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; ArgentinaIOS Press2020-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/138197Yannibelli, Virginia Daniela; Pacini Naumovich, Elina Rocío; Monge, David; Mateos Diaz, Cristian Maximiliano; Rodríguez, Guillermo Horacio; A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud; IOS Press; Scientific Programming; 2020; 8-2020; 1-171058-9244CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/sp/2020/4653204/info:eu-repo/semantics/altIdentifier/doi/10.1155/2020/4653204info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:04:28Zoai:ri.conicet.gov.ar:11336/138197instacron: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 10:04:28.94CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud |
title |
A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud |
spellingShingle |
A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud Yannibelli, Virginia Daniela AUTOSCALING CLOUD COMPUTING METAHEURISTICS |
title_short |
A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud |
title_full |
A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud |
title_fullStr |
A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud |
title_full_unstemmed |
A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud |
title_sort |
A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud |
dc.creator.none.fl_str_mv |
Yannibelli, Virginia Daniela Pacini Naumovich, Elina Rocío Monge, David Mateos Diaz, Cristian Maximiliano Rodríguez, Guillermo Horacio |
author |
Yannibelli, Virginia Daniela |
author_facet |
Yannibelli, Virginia Daniela Pacini Naumovich, Elina Rocío Monge, David Mateos Diaz, Cristian Maximiliano Rodríguez, Guillermo Horacio |
author_role |
author |
author2 |
Pacini Naumovich, Elina Rocío Monge, David Mateos Diaz, Cristian Maximiliano Rodríguez, Guillermo Horacio |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
AUTOSCALING CLOUD COMPUTING METAHEURISTICS |
topic |
AUTOSCALING CLOUD COMPUTING METAHEURISTICS |
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 is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off. Fil: Yannibelli, Virginia Daniela. 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; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina Fil: Monge, David. Universidad Nacional de Cuyo; Argentina 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: Rodríguez, Guillermo Horacio. 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 |
description |
The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-08 |
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/11336/138197 Yannibelli, Virginia Daniela; Pacini Naumovich, Elina Rocío; Monge, David; Mateos Diaz, Cristian Maximiliano; Rodríguez, Guillermo Horacio; A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud; IOS Press; Scientific Programming; 2020; 8-2020; 1-17 1058-9244 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/138197 |
identifier_str_mv |
Yannibelli, Virginia Daniela; Pacini Naumovich, Elina Rocío; Monge, David; Mateos Diaz, Cristian Maximiliano; Rodríguez, Guillermo Horacio; A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud; IOS Press; Scientific Programming; 2020; 8-2020; 1-17 1058-9244 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.hindawi.com/journals/sp/2020/4653204/ info:eu-repo/semantics/altIdentifier/doi/10.1155/2020/4653204 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
IOS Press |
publisher.none.fl_str_mv |
IOS Press |
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 |
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1842269857198899200 |
score |
13.13397 |