Job Schedulers for Machine Learning and Data Mining algorithms distributed in Hadoop

Autores
Cornejo, Félix Martín; Zunino, Alejandro; Murazzo, María Antonia
Año de publicación
2018
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The standard scheduler of Hadoop does not consider the characteristics of jobs such as computational demand, inputs / outputs, dependencies, location of the data, etc., which could be a valuable source to allocate resources to jobs in order to optimize their use. The objective of this research is to take advantage of this information for planning, limiting the scope to ML / DM algorithms, in order to improve the execution times with respect to existing schedulers. The aim is to improve Hadoop job schedulers, seeking to optimize the execution times of machine learning and data mining algorithms in Clusters.
Facultad de Informática
Materia
Ciencias Informáticas
Big Data, Hadoop, schedulers of Hadoop, ML/DM algorithms, machine learning
Data mining
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:10915/69919
Acceder
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network_name_str SEDICI (UNLP)
spelling Job Schedulers for Machine Learning and Data Mining algorithms distributed in HadoopCornejo, Félix MartínZunino, AlejandroMurazzo, María AntoniaCiencias InformáticasBig Data, Hadoop, schedulers of Hadoop, ML/DM algorithms, machine learningData miningThe standard scheduler of Hadoop does not consider the characteristics of jobs such as computational demand, inputs / outputs, dependencies, location of the data, etc., which could be a valuable source to allocate resources to jobs in order to optimize their use. The objective of this research is to take advantage of this information for planning, limiting the scope to ML / DM algorithms, in order to improve the execution times with respect to existing schedulers. The aim is to improve Hadoop job schedulers, seeking to optimize the execution times of machine learning and data mining algorithms in Clusters.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/pdf62-68http://sedici.unlp.edu.ar/handle/10915/69919enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-1659-4info:eu-repo/semantics/reference/hdl/10915/69464info: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:UNLP2025-10-22T16:52:01Zoai:sedici.unlp.edu.ar:10915/69919Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:52:02.128SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Job Schedulers for Machine Learning and Data Mining algorithms distributed in Hadoop
title Job Schedulers for Machine Learning and Data Mining algorithms distributed in Hadoop
spellingShingle Job Schedulers for Machine Learning and Data Mining algorithms distributed in Hadoop
Cornejo, Félix Martín
Ciencias Informáticas
Big Data, Hadoop, schedulers of Hadoop, ML/DM algorithms, machine learning
Data mining
title_short Job Schedulers for Machine Learning and Data Mining algorithms distributed in Hadoop
title_full Job Schedulers for Machine Learning and Data Mining algorithms distributed in Hadoop
title_fullStr Job Schedulers for Machine Learning and Data Mining algorithms distributed in Hadoop
title_full_unstemmed Job Schedulers for Machine Learning and Data Mining algorithms distributed in Hadoop
title_sort Job Schedulers for Machine Learning and Data Mining algorithms distributed in Hadoop
dc.creator.none.fl_str_mv Cornejo, Félix Martín
Zunino, Alejandro
Murazzo, María Antonia
author Cornejo, Félix Martín
author_facet Cornejo, Félix Martín
Zunino, Alejandro
Murazzo, María Antonia
author_role author
author2 Zunino, Alejandro
Murazzo, María Antonia
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Big Data, Hadoop, schedulers of Hadoop, ML/DM algorithms, machine learning
Data mining
topic Ciencias Informáticas
Big Data, Hadoop, schedulers of Hadoop, ML/DM algorithms, machine learning
Data mining
dc.description.none.fl_txt_mv The standard scheduler of Hadoop does not consider the characteristics of jobs such as computational demand, inputs / outputs, dependencies, location of the data, etc., which could be a valuable source to allocate resources to jobs in order to optimize their use. The objective of this research is to take advantage of this information for planning, limiting the scope to ML / DM algorithms, in order to improve the execution times with respect to existing schedulers. The aim is to improve Hadoop job schedulers, seeking to optimize the execution times of machine learning and data mining algorithms in Clusters.
Facultad de Informática
description The standard scheduler of Hadoop does not consider the characteristics of jobs such as computational demand, inputs / outputs, dependencies, location of the data, etc., which could be a valuable source to allocate resources to jobs in order to optimize their use. The objective of this research is to take advantage of this information for planning, limiting the scope to ML / DM algorithms, in order to improve the execution times with respect to existing schedulers. The aim is to improve Hadoop job schedulers, seeking to optimize the execution times of machine learning and data mining algorithms in Clusters.
publishDate 2018
dc.date.none.fl_str_mv 2018-06
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/69919
url http://sedici.unlp.edu.ar/handle/10915/69919
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
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
62-68
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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