Methods of Data Popularity Evaluation in the ATLAS Experiment at the LHC
The ATLAS Experiment at the LHC generates petabytes of data that is distributed among 160 computing sites all over the world and is processed continuously by various central production and user analysis tasks. The popularity of data is typically measured as the number of accesses and plays an import...
Main Authors: | , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2021-01-01
|
Series: | EPJ Web of Conferences |
Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_02013.pdf |
id |
doaj-a8d33560ddc24360b0e4ae5eb8e675f4 |
---|---|
record_format |
Article |
spelling |
doaj-a8d33560ddc24360b0e4ae5eb8e675f42021-08-26T09:27:32ZengEDP SciencesEPJ Web of Conferences2100-014X2021-01-012510201310.1051/epjconf/202125102013epjconf_chep2021_02013Methods of Data Popularity Evaluation in the ATLAS Experiment at the LHCBeermann Thomas0Chuchuk OlgaDi Girolamo Alessandro1Grigorieva MariaKlimentov Alexei2Lassnig Mario3Schulz Markus4Sciaba Andrea5Tretyakov EugenyBergische Universitaet WuppertalCERNBrookhaven National LaboratoryCERNCERNCERNThe ATLAS Experiment at the LHC generates petabytes of data that is distributed among 160 computing sites all over the world and is processed continuously by various central production and user analysis tasks. The popularity of data is typically measured as the number of accesses and plays an important role in resolving data management issues: deleting, replicating, moving between tapes, disks and caches. These data management procedures were still carried out in a semi-manual mode and now we have focused our efforts on automating it, making use of the historical knowledge about existing data management strategies. In this study we describe sources of information about data popularity and demonstrate their consistency. Based on the calculated popularity measurements, various distributions were obtained. Auxiliary information about replication and task processing allowed us to evaluate the correspondence between the number of tasks with popular data executed per site and the number of replicas per site. We also examine the popularity of user analysis data that is much less predictable than in the central production and requires more indicators than just the number of accesses.https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_02013.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Beermann Thomas Chuchuk Olga Di Girolamo Alessandro Grigorieva Maria Klimentov Alexei Lassnig Mario Schulz Markus Sciaba Andrea Tretyakov Eugeny |
spellingShingle |
Beermann Thomas Chuchuk Olga Di Girolamo Alessandro Grigorieva Maria Klimentov Alexei Lassnig Mario Schulz Markus Sciaba Andrea Tretyakov Eugeny Methods of Data Popularity Evaluation in the ATLAS Experiment at the LHC EPJ Web of Conferences |
author_facet |
Beermann Thomas Chuchuk Olga Di Girolamo Alessandro Grigorieva Maria Klimentov Alexei Lassnig Mario Schulz Markus Sciaba Andrea Tretyakov Eugeny |
author_sort |
Beermann Thomas |
title |
Methods of Data Popularity Evaluation in the ATLAS Experiment at the LHC |
title_short |
Methods of Data Popularity Evaluation in the ATLAS Experiment at the LHC |
title_full |
Methods of Data Popularity Evaluation in the ATLAS Experiment at the LHC |
title_fullStr |
Methods of Data Popularity Evaluation in the ATLAS Experiment at the LHC |
title_full_unstemmed |
Methods of Data Popularity Evaluation in the ATLAS Experiment at the LHC |
title_sort |
methods of data popularity evaluation in the atlas experiment at the lhc |
publisher |
EDP Sciences |
series |
EPJ Web of Conferences |
issn |
2100-014X |
publishDate |
2021-01-01 |
description |
The ATLAS Experiment at the LHC generates petabytes of data that is distributed among 160 computing sites all over the world and is processed continuously by various central production and user analysis tasks. The popularity of data is typically measured as the number of accesses and plays an important role in resolving data management issues: deleting, replicating, moving between tapes, disks and caches. These data management procedures were still carried out in a semi-manual mode and now we have focused our efforts on automating it, making use of the historical knowledge about existing data management strategies. In this study we describe sources of information about data popularity and demonstrate their consistency. Based on the calculated popularity measurements, various distributions were obtained. Auxiliary information about replication and task processing allowed us to evaluate the correspondence between the number of tasks with popular data executed per site and the number of replicas per site. We also examine the popularity of user analysis data that is much less predictable than in the central production and requires more indicators than just the number of accesses. |
url |
https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_02013.pdf |
work_keys_str_mv |
AT beermannthomas methodsofdatapopularityevaluationintheatlasexperimentatthelhc AT chuchukolga methodsofdatapopularityevaluationintheatlasexperimentatthelhc AT digirolamoalessandro methodsofdatapopularityevaluationintheatlasexperimentatthelhc AT grigorievamaria methodsofdatapopularityevaluationintheatlasexperimentatthelhc AT klimentovalexei methodsofdatapopularityevaluationintheatlasexperimentatthelhc AT lassnigmario methodsofdatapopularityevaluationintheatlasexperimentatthelhc AT schulzmarkus methodsofdatapopularityevaluationintheatlasexperimentatthelhc AT sciabaandrea methodsofdatapopularityevaluationintheatlasexperimentatthelhc AT tretyakoveugeny methodsofdatapopularityevaluationintheatlasexperimentatthelhc |
_version_ |
1721195816947810304 |