Implementing cloud-based parallel metaheuristics: an overview

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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 enoug...

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Detalles Bibliográficos
Autores principales:	González, Patricia, Pardo, Xoán C., Doallo, Ramón, Banga, Julio R.
Formato:	Objeto de conferencia
Lenguaje:	Inglés
Publicado:	2018
Materias:	Ciencias Informáticas parallel metaheuristics, cloud computing, MPI, MapReduce, Spark Heuristic methods
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/69947
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-69947
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas parallel metaheuristics, cloud computing, MPI, MapReduce, Spark Heuristic methods
spellingShingle	Ciencias Informáticas parallel metaheuristics, cloud computing, MPI, MapReduce, Spark Heuristic methods González, Patricia Pardo, Xoán C. Doallo, Ramón Banga, Julio R. Implementing cloud-based parallel metaheuristics: an overview
topic_facet	Ciencias Informáticas parallel metaheuristics, cloud computing, MPI, MapReduce, Spark Heuristic methods
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.
format	Objeto de conferencia Objeto de conferencia
author	González, Patricia Pardo, Xoán C. Doallo, Ramón Banga, Julio R.
author_facet	González, Patricia Pardo, Xoán C. Doallo, Ramón Banga, Julio R.
author_sort	González, Patricia
title	Implementing cloud-based parallel metaheuristics: an overview
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
publishDate	2018
url	http://sedici.unlp.edu.ar/handle/10915/69947
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bdutipo_str	Repositorios
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Implementing cloud-based parallel metaheuristics: an overview

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