| Issue |
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|
|
|---|---|---|
| Article Number | 02013 | |
| Number of page(s) | 10 | |
| Section | Distributed Computing, Data Management and Facilities | |
| DOI | https://doi.org/10.1051/epjconf/202125102013 | |
| Published online | 23 August 2021 | |
https://doi.org/10.1051/epjconf/202125102013
Methods of Data Popularity Evaluation in the ATLAS Experiment at the LHC
1 Lomonosov Moscow State University, Russian Federation
2 Plekhanov Russian University of Economics, Russian Federation
3 Brookhaven National Laboratory, USA
4 National Research Nuclear University MEPhI, Russian Federation
5 Bergische Universitaet Wuppertal, Germany
6 Université Côte d’Azur, France
7 CERN, Geneva, Switzerland
* e-mail: olga.chuchuk@cern.ch
** e-mail: maria.grigorieva@cern.ch
*** e-mail: andrea.sciaba@cern.ch
Published online: 23 August 2021
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.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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