Predicting dataset popularity for the CMS experiment

The CMS experiment at the LHC accelerator at CERN relies on its computing infrastructure to stay at the frontier of High Energy Physics, searching for new phenomena and making discoveries. Even though computing plays a significant role in physics analysis we rarely use its data to predict the system...

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Detalles Bibliográficos
Autores principales: Kuznetsov, Valentin, Li, Ting, Giommi, Luca, Bonacorsi, Daniele, Wildish, Tony
Lenguaje:eng
Publicado: 2016
Materias:
Other Fields of Physics
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/762/1/012048
http://cds.cern.ch/record/2134225
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author Kuznetsov, Valentin
Li, Ting
Giommi, Luca
Bonacorsi, Daniele
Wildish, Tony
author_facet Kuznetsov, Valentin
Li, Ting
Giommi, Luca
Bonacorsi, Daniele
Wildish, Tony
author_sort Kuznetsov, Valentin
collection CERN
description The CMS experiment at the LHC accelerator at CERN relies on its computing infrastructure to stay at the frontier of High Energy Physics, searching for new phenomena and making discoveries. Even though computing plays a significant role in physics analysis we rarely use its data to predict the system behavior itself. A basic information about computing resources, user activities and site utilization can be really useful for improving the throughput of the system and its management. In this paper, we discuss a first CMS analysis of dataset popularity based on CMS meta-data which can be used as a model for dynamic data placement and provide the foundation of data-driven approach for the CMS computing infrastructure.
id cern-2134225
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2016
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spelling cern-21342252023-03-14T17:50:02Zdoi:10.1088/1742-6596/762/1/012048http://cds.cern.ch/record/2134225engKuznetsov, ValentinLi, TingGiommi, LucaBonacorsi, DanieleWildish, TonyPredicting dataset popularity for the CMS experimentOther Fields of PhysicsThe CMS experiment at the LHC accelerator at CERN relies on its computing infrastructure to stay at the frontier of High Energy Physics, searching for new phenomena and making discoveries. Even though computing plays a significant role in physics analysis we rarely use its data to predict the system behavior itself. A basic information about computing resources, user activities and site utilization can be really useful for improving the throughput of the system and its management. In this paper, we discuss a first CMS analysis of dataset popularity based on CMS meta-data which can be used as a model for dynamic data placement and provide the foundation of data-driven approach for the CMS computing infrastructure.The CMS experiment at the LHC accelerator at CERN relies on its computing infrastructure to stay at the frontier of High Energy Physics, searching for new phenomena and making discoveries. Even though computing plays a significant role in physics analysis we rarely use its data to predict the system behavior itself. A basic information about computing resources, user activities and site utilization can be really useful for improving the throughput of the system and its management. In this paper, we discuss a first CMS analysis of dataset popularity based on CMS meta-data which can be used as a model for dynamic data placement and provide the foundation of data-driven approach for the CMS computing infrastructure.arXiv:1602.07226oai:cds.cern.ch:21342252016-02-23
spellingShingle Other Fields of Physics
Kuznetsov, Valentin
Li, Ting
Giommi, Luca
Bonacorsi, Daniele
Wildish, Tony
Predicting dataset popularity for the CMS experiment
title Predicting dataset popularity for the CMS experiment
title_full Predicting dataset popularity for the CMS experiment
title_fullStr Predicting dataset popularity for the CMS experiment
title_full_unstemmed Predicting dataset popularity for the CMS experiment
title_short Predicting dataset popularity for the CMS experiment
title_sort predicting dataset popularity for the cms experiment
topic Other Fields of Physics
url https://dx.doi.org/10.1088/1742-6596/762/1/012048
http://cds.cern.ch/record/2134225
work_keys_str_mv AT kuznetsovvalentin predictingdatasetpopularityforthecmsexperiment
AT liting predictingdatasetpopularityforthecmsexperiment
AT giommiluca predictingdatasetpopularityforthecmsexperiment
AT bonacorsidaniele predictingdatasetpopularityforthecmsexperiment
AT wildishtony predictingdatasetpopularityforthecmsexperiment

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