Performance and impact of dynamic data placement in ATLAS

Thomas Maier; Thomas Beermann; Günter Duckeck; Mario Lassnig; Federica Legger; Matteo Magoni; Ilija Vukotic

doi:10.1051/epjconf/201921404025

Proceedings

Open Access

EPJ Web of Conferences 214, 04025 (2019)
https://doi.org/10.1051/epjconf/201921404025

Performance and impact of dynamic data placement in ATLAS

Thomas Maier¹^*, Thomas Beermann², Günter Duckeck¹, Mario Lassnig³, Federica Legger¹^,4, Matteo Magoni⁵ and Ilija Vukotic⁶ on behalf of the ATLAS Collaboration

¹ Ludwig-Maximilians-Universität München, Germany
² Universität Innsbruck, Austria
³ CERN, Geneva, Switzerland
⁴ INFN Turin, Italy
⁵ Politecnico di Torino, Italy
⁶ University of Chicago, USA

^* Corresponding author: thomas.maier@physik.uni-muenchen.de

Published online: 17 September 2019

Abstract

For high-throughput computing the efficient use of distributed computing resources relies on an evenly distributed workload, which in turn requires wide availability of input data that is used in physics analysis. In ATLAS, the dynamic data placement agent C3PO was implemented in the ATLAS distributed data management system Rucio which identifies popular data and creates additional, transient replicas to make data more widely and more reliably available. This proceedings presents studies on the performance of C3PO and the impact it has on throughput rates of distributed computing in ATLAS. Furthermore, results of a study on popularity prediction using machine learning techniques are presented.

© The Authors, published by EDP Sciences, 2019

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.