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

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...

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Detalles Bibliográficos
Autores principales: Cornejo, Félix Martín, Zunino, Alejandro, Murazzo, María Antonia
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2018
Materias:
Ciencias Informáticas
Big Data, Hadoop, schedulers of Hadoop, ML/DM algorithms, machine learning
Data mining
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/69919
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-69919
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
Big Data, Hadoop, schedulers of Hadoop, ML/DM algorithms, machine learning
Data mining
spellingShingle Ciencias Informáticas
Big Data, Hadoop, schedulers of Hadoop, ML/DM algorithms, machine learning
Data mining
Cornejo, Félix Martín
Zunino, Alejandro
Murazzo, María Antonia
Job Schedulers for Machine Learning and Data Mining algorithms distributed in Hadoop
topic_facet Ciencias Informáticas
Big Data, Hadoop, schedulers of Hadoop, ML/DM algorithms, machine learning
Data mining
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.
format Objeto de conferencia
Objeto de conferencia
author Cornejo, Félix Martín
Zunino, Alejandro
Murazzo, María Antonia
author_facet Cornejo, Félix Martín
Zunino, Alejandro
Murazzo, María Antonia
author_sort Cornejo, Félix Martín
title Job Schedulers for Machine Learning and Data Mining algorithms distributed in Hadoop
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
publishDate 2018
url http://sedici.unlp.edu.ar/handle/10915/69919
work_keys_str_mv AT cornejofelixmartin jobschedulersformachinelearninganddataminingalgorithmsdistributedinhadoop
AT zuninoalejandro jobschedulersformachinelearninganddataminingalgorithmsdistributedinhadoop
AT murazzomariaantonia jobschedulersformachinelearninganddataminingalgorithmsdistributedinhadoop
bdutipo_str Repositorios
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