The Data Ocean Project

Martin Barisits; Fernando Barreiro; Thomas Beermann; Karan Bhatia; Kaushik De; Arnaud Dubreuil; Johannes Elmsheuser; Alexei Klimentov; Mario Lassnig; Peter Love; Tadashi Maeno; Andrea Manzi; Ruslan Mashinistov; Andy Murphy; Paul Nilsson; Sergey Panitkin; Tobias Wegner

doi:10.1051/epjconf/201921404020

All issues

Volume 214 (2019)

EPJ Web Conf., 214 (2019) 04020

Abstract

Open Access

Issue		EPJ Web Conf. Volume 214, 2019 23^rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)


Article Number		04020
Number of page(s)		8
Section		T4 - Data handling
DOI		https://doi.org/10.1051/epjconf/201921404020
Published online		17 September 2019

EPJ Web of Conferences 214, 04020 (2019)
https://doi.org/10.1051/epjconf/201921404020

An ATLAS and Google R&D collaboration

Martin Barisits¹, Fernando Barreiro², Thomas Beermann³, Karan Bhatia⁴, Kaushik De², Arnaud Dubreuil⁵, Johannes Elmsheuser⁶, Alexei Klimentov⁶, Mario Lassnig¹, Peter Love⁷, Tadashi Maeno⁶, Andrea Manzi¹, Ruslan Mashinistov⁶, Andy Murphy⁴, Paul Nilsson⁶, Sergey Panitkin⁶ and Tobias Wegner¹^,* on behalf of the ATLAS Collaboration

¹ European Organisation for Nuclear Research (CERN),
² University of Texas at Arlington (UTA),
³ University of Innsbruck,
⁴ Google LLC,
⁵ University of Geneva,
⁶ Brookhaven National Laboratory (BNL),
⁷ University of Lancaster,

Published online: 17 September 2019

Abstract

Transparent use of commercial cloud resources for scientific experiments is a hard problem. In this article, we describe the first steps of the Data Ocean R&D collaboration between the high-energy physics experiment ATLAS together with Google Cloud Platform, to allow seamless use of Google Compute Engine and Google Cloud Storage for physics analysis. We start by describing the three preliminary use cases that were identified at the beginning of the project. The following sections then detail the work done in the data management system Rucio and the workflow management systems PanDA and Harvester to interface Google Cloud Platform with the ATLAS distributed computing environment, and show the results of the integration tests. Afterwards, we describe the setup and results from a full ATLAS user analysis that was executed natively on Google Cloud Platform, and give estimates on projected costs. We close with a summary and and outlook on future work.

^*

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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