Including accurate user estimates in HPC schedulers: ban empirical analysis

This article focuses on the problem of dealing with low accuracy of job runtime estimates provided by users of high performance computing systems. The main goal of the study is to evaluate the benefits on the system utilization of providing accurate estimations, in order to motivate users to make an...

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Detalles Bibliográficos
Autores principales: Rocchetti, Néstor, Iturriaga, Santiago, Nesmachnow, Sergio
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2015
Materias:
Ciencias Informáticas
high performance computing
execution time estimation
Scheduling
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/50190
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-50190
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
high performance computing
execution time estimation
Scheduling
spellingShingle Ciencias Informáticas
high performance computing
execution time estimation
Scheduling
Rocchetti, Néstor
Iturriaga, Santiago
Nesmachnow, Sergio
Including accurate user estimates in HPC schedulers: ban empirical analysis
topic_facet Ciencias Informáticas
high performance computing
execution time estimation
Scheduling
description This article focuses on the problem of dealing with low accuracy of job runtime estimates provided by users of high performance computing systems. The main goal of the study is to evaluate the benefits on the system utilization of providing accurate estimations, in order to motivate users to make an effort to provide better estimates. We propose the Penalty Scheduling Policy for including information about user estimates. The experimental evaluation is performed over realistic workload and scenarios, and validated by the use of a job scheduler simulator. We simulated different static and dynamic scenarios, which emulate diverse user behavior regarding the estimation of jobs runtime. Results demonstrate that the accuracy of users runtime estimates influences the waiting time of jobs. Under our proposed policy, in a scenario where users improve their estimates, waiting time of users with high accuracy can be up to 2.43 times lower than users with the lowest accuracy.
format Objeto de conferencia
Objeto de conferencia
author Rocchetti, Néstor
Iturriaga, Santiago
Nesmachnow, Sergio
author_facet Rocchetti, Néstor
Iturriaga, Santiago
Nesmachnow, Sergio
author_sort Rocchetti, Néstor
title Including accurate user estimates in HPC schedulers: ban empirical analysis
title_short Including accurate user estimates in HPC schedulers: ban empirical analysis
title_full Including accurate user estimates in HPC schedulers: ban empirical analysis
title_fullStr Including accurate user estimates in HPC schedulers: ban empirical analysis
title_full_unstemmed Including accurate user estimates in HPC schedulers: ban empirical analysis
title_sort including accurate user estimates in hpc schedulers: ban empirical analysis
publishDate 2015
url http://sedici.unlp.edu.ar/handle/10915/50190
work_keys_str_mv AT rocchettinestor includingaccurateuserestimatesinhpcschedulersbanempiricalanalysis
AT iturriagasantiago includingaccurateuserestimatesinhpcschedulersbanempiricalanalysis
AT nesmachnowsergio includingaccurateuserestimatesinhpcschedulersbanempiricalanalysis
bdutipo_str Repositorios
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