Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics

Autores
Micheletto, Matías Javier; Santos, Rodrigo Martin; Orozco, Javier Dario
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper we present meta-heuristics to solve the energy aware reward based scheduling of real-time tasks with mandatory and optional parts in homogeneous multi-core processors. The problem is NP-Hard. An objective function to maximize the performance of the system considering the execution of optional parts, the benefits of slowing down the processor and a penalty for changing the operation power-mode is introduced together with a set of constraints that guarantee the real-time performance of the system. The meta-heuristics are the bio-inspired methods Particle Swarm Optimization and Genetic Algorithm. Experiments are made to evaluate the proposed algorithms using a set of synthetic systems of tasks. As these have been used previously with an Integer Lineal Programming approach, the results are compared and show that the solutions obtained with bio-inspired methods are within the Pareto frontier and obtained in less time. Finally, precedence related tasks systems are analyzed and the meta-heuristics proposed are extended to solve also this kind of systems. The evaluation is made by solving a traditional example of the real-time precedence related tasks systems on multiprocessors. The solutions obtained through the methods proposed in this paper are good and show that the methods are competitive. In all cases, the solutions are similar to the ones provided by other methods but obtained in less time and with fewer iterations.
Fil: Micheletto, Matías Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina
Fil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina
Fil: Orozco, Javier Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina
Materia
ENERGY HANDLING
MULTICORE SYSTEMS
REWARD BASED SCHEDULING
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:11336/117620
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/117620
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-HeuristicsMicheletto, Matías JavierSantos, Rodrigo MartinOrozco, Javier DarioENERGY HANDLINGMULTICORE SYSTEMSREWARD BASED SCHEDULINGhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In this paper we present meta-heuristics to solve the energy aware reward based scheduling of real-time tasks with mandatory and optional parts in homogeneous multi-core processors. The problem is NP-Hard. An objective function to maximize the performance of the system considering the execution of optional parts, the benefits of slowing down the processor and a penalty for changing the operation power-mode is introduced together with a set of constraints that guarantee the real-time performance of the system. The meta-heuristics are the bio-inspired methods Particle Swarm Optimization and Genetic Algorithm. Experiments are made to evaluate the proposed algorithms using a set of synthetic systems of tasks. As these have been used previously with an Integer Lineal Programming approach, the results are compared and show that the solutions obtained with bio-inspired methods are within the Pareto frontier and obtained in less time. Finally, precedence related tasks systems are analyzed and the meta-heuristics proposed are extended to solve also this kind of systems. The evaluation is made by solving a traditional example of the real-time precedence related tasks systems on multiprocessors. The solutions obtained through the methods proposed in this paper are good and show that the methods are competitive. In all cases, the solutions are similar to the ones provided by other methods but obtained in less time and with fewer iterations.Fil: Micheletto, Matías Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Orozco, Javier Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaGraz University of Technology2019-05-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/117620Micheletto, Matías Javier; Santos, Rodrigo Martin; Orozco, Javier Dario; Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics; Graz University of Technology; Journal of Universal Computer Science; 25; 4; 28-5-2019; 390-4170948-695X0948-6968CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.jucs.org/jucs_25_4/scheduling_mandatory_optional_realinfo:eu-repo/semantics/altIdentifier/doi/10.3217/jucs-025-04-0390info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:52:40Zoai:ri.conicet.gov.ar:11336/117620instacron: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 09:52:41.122CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics
title Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics
spellingShingle Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics
Micheletto, Matías Javier
ENERGY HANDLING
MULTICORE SYSTEMS
REWARD BASED SCHEDULING
title_short Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics
title_full Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics
title_fullStr Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics
title_full_unstemmed Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics
title_sort Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics
dc.creator.none.fl_str_mv Micheletto, Matías Javier
Santos, Rodrigo Martin
Orozco, Javier Dario
author Micheletto, Matías Javier
author_facet Micheletto, Matías Javier
Santos, Rodrigo Martin
Orozco, Javier Dario
author_role author
author2 Santos, Rodrigo Martin
Orozco, Javier Dario
author2_role author
author
dc.subject.none.fl_str_mv ENERGY HANDLING
MULTICORE SYSTEMS
REWARD BASED SCHEDULING
topic ENERGY HANDLING
MULTICORE SYSTEMS
REWARD BASED SCHEDULING
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this paper we present meta-heuristics to solve the energy aware reward based scheduling of real-time tasks with mandatory and optional parts in homogeneous multi-core processors. The problem is NP-Hard. An objective function to maximize the performance of the system considering the execution of optional parts, the benefits of slowing down the processor and a penalty for changing the operation power-mode is introduced together with a set of constraints that guarantee the real-time performance of the system. The meta-heuristics are the bio-inspired methods Particle Swarm Optimization and Genetic Algorithm. Experiments are made to evaluate the proposed algorithms using a set of synthetic systems of tasks. As these have been used previously with an Integer Lineal Programming approach, the results are compared and show that the solutions obtained with bio-inspired methods are within the Pareto frontier and obtained in less time. Finally, precedence related tasks systems are analyzed and the meta-heuristics proposed are extended to solve also this kind of systems. The evaluation is made by solving a traditional example of the real-time precedence related tasks systems on multiprocessors. The solutions obtained through the methods proposed in this paper are good and show that the methods are competitive. In all cases, the solutions are similar to the ones provided by other methods but obtained in less time and with fewer iterations.
Fil: Micheletto, Matías Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina
Fil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina
Fil: Orozco, Javier Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina
description In this paper we present meta-heuristics to solve the energy aware reward based scheduling of real-time tasks with mandatory and optional parts in homogeneous multi-core processors. The problem is NP-Hard. An objective function to maximize the performance of the system considering the execution of optional parts, the benefits of slowing down the processor and a penalty for changing the operation power-mode is introduced together with a set of constraints that guarantee the real-time performance of the system. The meta-heuristics are the bio-inspired methods Particle Swarm Optimization and Genetic Algorithm. Experiments are made to evaluate the proposed algorithms using a set of synthetic systems of tasks. As these have been used previously with an Integer Lineal Programming approach, the results are compared and show that the solutions obtained with bio-inspired methods are within the Pareto frontier and obtained in less time. Finally, precedence related tasks systems are analyzed and the meta-heuristics proposed are extended to solve also this kind of systems. The evaluation is made by solving a traditional example of the real-time precedence related tasks systems on multiprocessors. The solutions obtained through the methods proposed in this paper are good and show that the methods are competitive. In all cases, the solutions are similar to the ones provided by other methods but obtained in less time and with fewer iterations.
publishDate 2019
dc.date.none.fl_str_mv 2019-05-28
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/11336/117620
Micheletto, Matías Javier; Santos, Rodrigo Martin; Orozco, Javier Dario; Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics; Graz University of Technology; Journal of Universal Computer Science; 25; 4; 28-5-2019; 390-417
0948-695X
0948-6968
CONICET Digital
CONICET
url http://hdl.handle.net/11336/117620
identifier_str_mv Micheletto, Matías Javier; Santos, Rodrigo Martin; Orozco, Javier Dario; Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics; Graz University of Technology; Journal of Universal Computer Science; 25; 4; 28-5-2019; 390-417
0948-695X
0948-6968
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.jucs.org/jucs_25_4/scheduling_mandatory_optional_real
info:eu-repo/semantics/altIdentifier/doi/10.3217/jucs-025-04-0390
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
dc.publisher.none.fl_str_mv Graz University of Technology
publisher.none.fl_str_mv Graz University of Technology
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_ 1842269175735648256
score 13.13397

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