An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds
- Autores
- Yannibelli, Virginia Daniela; Pacini Naumovich, Elina Rocío; Monge Bosdari, David Antonio; Mateos Diaz, Cristian Maximiliano; Rodríguez, Guillermo Horacio; Millán, Emmanuel Nicolás; Santos, Jorge Ruben
- Año de publicación
- 2023
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. Tese applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that ofer instances of diferent VM (virtual machine) types, under a pay-per-use scheme, to execute diverse applications. One of the most recent approaches is the autoscaler MOEA (multiobjective evolutive algorithm), which is based on the multiobjective evolutionary algorithm NSGA-II (nondominated sorting genetic algorithm II). MOEA considers on-demand and spot VM instances and three optimization objectives relevant for users: minimizing the computing time, monetary cost, and spot instance interruptions of the application’s execution. However, MOEA’s performance regarding these optimization objectives depends signifcantly on the optimization algorithm used. It has been shown recently that MOEA’s performance improves considerably when NSGA-II is replaced by a more recent algorithm named NSGA-III. In this paper, we analyze the incorporation of other multiobjective optimization algorithms into MOEA to enhance the performance of this autoscaler. First, we consider three multiobjective optimization algorithms named E-NSGA-III (extreme NSGA-III), SMS-EMOA (S-metric selection evolutionary multiobjective optimization algorithm), and SMPSO (speed-constrained multiobjective particle swarm optimization), which have behavioral diferences with NSGA-III. Ten, we evaluate the performance of MOEA with each of these algorithms, considering the three optimization objectives, on four real-world PSE applications from the meteorology and molecular dynamics areas, considering diferent application sizes. To do that, we use the well-known CloudSim simulator and consider diferent VM types available in Amazon EC2. Finally, we analyze the obtained performance results, which show that MOEA with E-NSGA-III arises as the best alternative, reaching better and signifcant savings in terms of computing time (10%–17%), monetary cost (10%– 40%), and spot instance interruptions (33%–100%).
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. Facultad de Ingeniería; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Monge Bosdari, David Antonio. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; 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
Fil: Millán, Emmanuel Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina
Fil: Santos, Jorge Ruben. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina - Materia
-
PARAMETER SWEEP EXPERIMENTS
CLOUD COMPUTING
CLOUD AUTOSCALING
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM
MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION - 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:11336/224139
Ver los metadatos del registro completo
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An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public CloudsYannibelli, Virginia DanielaPacini Naumovich, Elina RocíoMonge Bosdari, David AntonioMateos Diaz, Cristian MaximilianoRodríguez, Guillermo HoracioMillán, Emmanuel NicolásSantos, Jorge RubenPARAMETER SWEEP EXPERIMENTSCLOUD COMPUTINGCLOUD AUTOSCALINGMULTI-OBJECTIVE EVOLUTIONARY ALGORITHMMULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. Tese applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that ofer instances of diferent VM (virtual machine) types, under a pay-per-use scheme, to execute diverse applications. One of the most recent approaches is the autoscaler MOEA (multiobjective evolutive algorithm), which is based on the multiobjective evolutionary algorithm NSGA-II (nondominated sorting genetic algorithm II). MOEA considers on-demand and spot VM instances and three optimization objectives relevant for users: minimizing the computing time, monetary cost, and spot instance interruptions of the application’s execution. However, MOEA’s performance regarding these optimization objectives depends signifcantly on the optimization algorithm used. It has been shown recently that MOEA’s performance improves considerably when NSGA-II is replaced by a more recent algorithm named NSGA-III. In this paper, we analyze the incorporation of other multiobjective optimization algorithms into MOEA to enhance the performance of this autoscaler. First, we consider three multiobjective optimization algorithms named E-NSGA-III (extreme NSGA-III), SMS-EMOA (S-metric selection evolutionary multiobjective optimization algorithm), and SMPSO (speed-constrained multiobjective particle swarm optimization), which have behavioral diferences with NSGA-III. Ten, we evaluate the performance of MOEA with each of these algorithms, considering the three optimization objectives, on four real-world PSE applications from the meteorology and molecular dynamics areas, considering diferent application sizes. To do that, we use the well-known CloudSim simulator and consider diferent VM types available in Amazon EC2. Finally, we analyze the obtained performance results, which show that MOEA with E-NSGA-III arises as the best alternative, reaching better and signifcant savings in terms of computing time (10%–17%), monetary cost (10%– 40%), and spot instance interruptions (33%–100%).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. Facultad de Ingeniería; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Monge Bosdari, David Antonio. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; 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; ArgentinaFil: Millán, Emmanuel Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Santos, Jorge Ruben. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; ArgentinaHindawi Publishing Corporation2023-02info: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/11336/224139Yannibelli, Virginia Daniela; Pacini Naumovich, Elina Rocío; Monge Bosdari, David Antonio; Mateos Diaz, Cristian Maximiliano; Rodríguez, Guillermo Horacio; et al.; An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds; Hindawi Publishing Corporation; Scientific Programming; 2023; 2-2023; 1-261058-9244CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/sp/2023/8345646/info:eu-repo/semantics/altIdentifier/doi/10.1155/2023/8345646info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:06:44Zoai:ri.conicet.gov.ar:11336/224139instacron: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 10:06:44.599CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds |
title |
An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds |
spellingShingle |
An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds Yannibelli, Virginia Daniela PARAMETER SWEEP EXPERIMENTS CLOUD COMPUTING CLOUD AUTOSCALING MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION |
title_short |
An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds |
title_full |
An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds |
title_fullStr |
An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds |
title_full_unstemmed |
An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds |
title_sort |
An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds |
dc.creator.none.fl_str_mv |
Yannibelli, Virginia Daniela Pacini Naumovich, Elina Rocío Monge Bosdari, David Antonio Mateos Diaz, Cristian Maximiliano Rodríguez, Guillermo Horacio Millán, Emmanuel Nicolás Santos, Jorge Ruben |
author |
Yannibelli, Virginia Daniela |
author_facet |
Yannibelli, Virginia Daniela Pacini Naumovich, Elina Rocío Monge Bosdari, David Antonio Mateos Diaz, Cristian Maximiliano Rodríguez, Guillermo Horacio Millán, Emmanuel Nicolás Santos, Jorge Ruben |
author_role |
author |
author2 |
Pacini Naumovich, Elina Rocío Monge Bosdari, David Antonio Mateos Diaz, Cristian Maximiliano Rodríguez, Guillermo Horacio Millán, Emmanuel Nicolás Santos, Jorge Ruben |
author2_role |
author author author author author author |
dc.subject.none.fl_str_mv |
PARAMETER SWEEP EXPERIMENTS CLOUD COMPUTING CLOUD AUTOSCALING MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION |
topic |
PARAMETER SWEEP EXPERIMENTS CLOUD COMPUTING CLOUD AUTOSCALING MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. Tese applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that ofer instances of diferent VM (virtual machine) types, under a pay-per-use scheme, to execute diverse applications. One of the most recent approaches is the autoscaler MOEA (multiobjective evolutive algorithm), which is based on the multiobjective evolutionary algorithm NSGA-II (nondominated sorting genetic algorithm II). MOEA considers on-demand and spot VM instances and three optimization objectives relevant for users: minimizing the computing time, monetary cost, and spot instance interruptions of the application’s execution. However, MOEA’s performance regarding these optimization objectives depends signifcantly on the optimization algorithm used. It has been shown recently that MOEA’s performance improves considerably when NSGA-II is replaced by a more recent algorithm named NSGA-III. In this paper, we analyze the incorporation of other multiobjective optimization algorithms into MOEA to enhance the performance of this autoscaler. First, we consider three multiobjective optimization algorithms named E-NSGA-III (extreme NSGA-III), SMS-EMOA (S-metric selection evolutionary multiobjective optimization algorithm), and SMPSO (speed-constrained multiobjective particle swarm optimization), which have behavioral diferences with NSGA-III. Ten, we evaluate the performance of MOEA with each of these algorithms, considering the three optimization objectives, on four real-world PSE applications from the meteorology and molecular dynamics areas, considering diferent application sizes. To do that, we use the well-known CloudSim simulator and consider diferent VM types available in Amazon EC2. Finally, we analyze the obtained performance results, which show that MOEA with E-NSGA-III arises as the best alternative, reaching better and signifcant savings in terms of computing time (10%–17%), monetary cost (10%– 40%), and spot instance interruptions (33%–100%). 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. Facultad de Ingeniería; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Monge Bosdari, David Antonio. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; 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 Fil: Millán, Emmanuel Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina Fil: Santos, Jorge Ruben. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina |
description |
Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. Tese applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that ofer instances of diferent VM (virtual machine) types, under a pay-per-use scheme, to execute diverse applications. One of the most recent approaches is the autoscaler MOEA (multiobjective evolutive algorithm), which is based on the multiobjective evolutionary algorithm NSGA-II (nondominated sorting genetic algorithm II). MOEA considers on-demand and spot VM instances and three optimization objectives relevant for users: minimizing the computing time, monetary cost, and spot instance interruptions of the application’s execution. However, MOEA’s performance regarding these optimization objectives depends signifcantly on the optimization algorithm used. It has been shown recently that MOEA’s performance improves considerably when NSGA-II is replaced by a more recent algorithm named NSGA-III. In this paper, we analyze the incorporation of other multiobjective optimization algorithms into MOEA to enhance the performance of this autoscaler. First, we consider three multiobjective optimization algorithms named E-NSGA-III (extreme NSGA-III), SMS-EMOA (S-metric selection evolutionary multiobjective optimization algorithm), and SMPSO (speed-constrained multiobjective particle swarm optimization), which have behavioral diferences with NSGA-III. Ten, we evaluate the performance of MOEA with each of these algorithms, considering the three optimization objectives, on four real-world PSE applications from the meteorology and molecular dynamics areas, considering diferent application sizes. To do that, we use the well-known CloudSim simulator and consider diferent VM types available in Amazon EC2. Finally, we analyze the obtained performance results, which show that MOEA with E-NSGA-III arises as the best alternative, reaching better and signifcant savings in terms of computing time (10%–17%), monetary cost (10%– 40%), and spot instance interruptions (33%–100%). |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02 |
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/11336/224139 Yannibelli, Virginia Daniela; Pacini Naumovich, Elina Rocío; Monge Bosdari, David Antonio; Mateos Diaz, Cristian Maximiliano; Rodríguez, Guillermo Horacio; et al.; An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds; Hindawi Publishing Corporation; Scientific Programming; 2023; 2-2023; 1-26 1058-9244 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/224139 |
identifier_str_mv |
Yannibelli, Virginia Daniela; Pacini Naumovich, Elina Rocío; Monge Bosdari, David Antonio; Mateos Diaz, Cristian Maximiliano; Rodríguez, Guillermo Horacio; et al.; An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds; Hindawi Publishing Corporation; Scientific Programming; 2023; 2-2023; 1-26 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/2023/8345646/ info:eu-repo/semantics/altIdentifier/doi/10.1155/2023/8345646 |
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 application/pdf |
dc.publisher.none.fl_str_mv |
Hindawi Publishing Corporation |
publisher.none.fl_str_mv |
Hindawi Publishing Corporation |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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13.13397 |