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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/69919
Ver los metadatos del registro completo
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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:UNLP2025-09-29T11:11:05Zoai: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:13292025-09-29 11:11:05.53SEDICI (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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf 62-68 |
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