| ATLAS Slides | |
| Report number | ATL-SOFT-SLIDE-2018-421 |
| Title | ATLAS & Google - The Data Ocean Project |
| Author(s) |
Lassnig, Mario (European Laboratory for Particle Physics, CERN) ; Klimentov, Alexei (Brookhaven National Laboratory (BNL)) ; De, Kaushik (The University of Texas at Arlington) ; Barreiro Megino, Fernando Harald (The University of Texas at Arlington) ; Elmsheuser, Johannes (Brookhaven National Laboratory (BNL)) ; Panitkin, Sergey (Brookhaven National Laboratory (BNL)) ; Wegner, Tobias Thomas (European Laboratory for Particle Physics, CERN) ; Barisits, Martin-Stefan (European Laboratory for Particle Physics, CERN) ; Beermann, Thomas Alfons (Institut fuer Astro- und Teilchenphysik der Leopold-Franzens-Universitaet Innsbruck) ; Mashinistov, Ruslan (P.N. Lebedev Physical Institute) ; Love, Peter (Lancaster University, Department of Physics) ; Dubreuil, Arnaud (Departement de Physique Nucleaire et Corpusculaire, Universite de Geneve) ; Maeno, Tadashi (Brookhaven National Laboratory (BNL)) ; Nilsson, Paul (Brookhaven National Laboratory (BNL)) |
| Corporate author(s) | The ATLAS collaboration |
| Submitted to | 23rd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2018, Sofia, Bulgaria, 9 - 13 Jul 2018 |
| Submitted by | [email protected] on 28 Jun 2018 |
| Subject category | Particle Physics - Experiment |
| Accelerator/Facility, Experiment | CERN LHC ; ATLAS |
| Free keywords | data management ; cloud computing ; industry collaboration |
| Abstract | With the LHC High Luminosity upgrade the workload and data management systems are facing new major challenges. To address those challenges ATLAS and Google agreed to cooperate on a project to connect Google Cloud Storage and Compute Engine to the ATLAS computing environment. The idea is to allow ATLAS to explore the use of different computing models, to allow ATLAS user analysis to benefit from the Google infrastructure, and to give Google real science use cases to improve their cloud platform. Making the output of a distributed analysis from the grid quickly available to the analyst is a difficult problem. Redirecting the analysis output to Google Cloud Storage can provide an alternative, faster solution for the analyst. First, Google's Cloud Storage will be connected to the ATLAS Data Management System Rucio. The second part aims to let jobs run on Google Compute Engine, accessing data from either ATLAS storage or Google Cloud Storage. The third part involves Google implementing a global redirection between their regions to expose Google Cloud Storage as a single global entity. The last part will deal with the economic model necessary for sustainable cloud resource usage, including Google Cloud Storage costs, network costs, and peering costs with ESnet. |
| Related document | Conference Paper ATL-SOFT-PROC-2018-034 |
Záznam vytvorený 2018-06-28, zmenený 2019-11-12
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