Implementing cloud-based parallel metaheuristics: an overview

Autores
González, Patricia; Pardo, Xoán C.; Doallo, Ramón; Banga, Julio R.
Año de publicación
2018
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel implementation applying HPC techniques is a common approach for efficiently using available resources to reduce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuristics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.
Facultad de Informática
Materia
Ciencias Informáticas
parallel metaheuristics, cloud computing, MPI, MapReduce, Spark
Heuristic methods
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/69947
Acceder
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Implementing cloud-based parallel metaheuristics: an overviewGonzález, PatriciaPardo, Xoán C.Doallo, RamónBanga, Julio R.Ciencias Informáticasparallel metaheuristics, cloud computing, MPI, MapReduce, SparkHeuristic methodsMetaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel implementation applying HPC techniques is a common approach for efficiently using available resources to reduce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuristics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.Facultad de Informática2018-06info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf101-110http://sedici.unlp.edu.ar/handle/10915/69947enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-1659-4info:eu-repo/semantics/reference/hdl/10915/69464info:eu-repo/semantics/reference/hdl/10915/71658info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:43:06Zoai:sedici.unlp.edu.ar:10915/69947Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:43:06.319SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Implementing cloud-based parallel metaheuristics: an overview
title Implementing cloud-based parallel metaheuristics: an overview
spellingShingle Implementing cloud-based parallel metaheuristics: an overview
González, Patricia
Ciencias Informáticas
parallel metaheuristics, cloud computing, MPI, MapReduce, Spark
Heuristic methods
title_short Implementing cloud-based parallel metaheuristics: an overview
title_full Implementing cloud-based parallel metaheuristics: an overview
title_fullStr Implementing cloud-based parallel metaheuristics: an overview
title_full_unstemmed Implementing cloud-based parallel metaheuristics: an overview
title_sort Implementing cloud-based parallel metaheuristics: an overview
dc.creator.none.fl_str_mv González, Patricia
Pardo, Xoán C.
Doallo, Ramón
Banga, Julio R.
author González, Patricia
author_facet González, Patricia
Pardo, Xoán C.
Doallo, Ramón
Banga, Julio R.
author_role author
author2 Pardo, Xoán C.
Doallo, Ramón
Banga, Julio R.
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
parallel metaheuristics, cloud computing, MPI, MapReduce, Spark
Heuristic methods
topic Ciencias Informáticas
parallel metaheuristics, cloud computing, MPI, MapReduce, Spark
Heuristic methods
dc.description.none.fl_txt_mv Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel implementation applying HPC techniques is a common approach for efficiently using available resources to reduce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuristics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.
Facultad de Informática
description Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel implementation applying HPC techniques is a common approach for efficiently using available resources to reduce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuristics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.
publishDate 2018
dc.date.none.fl_str_mv 2018-06
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info:eu-repo/semantics/publishedVersion
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info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/69947
url http://sedici.unlp.edu.ar/handle/10915/69947
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-34-1659-4
info:eu-repo/semantics/reference/hdl/10915/69464
info:eu-repo/semantics/reference/hdl/10915/71658
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
101-110
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instname:Universidad Nacional de La Plata
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collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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