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ATLAS Cloud Computing R&D project

The computing model of the ATLAS experiment was designed around the concept of grid computing and, since the start of data taking, this model has proven very successful. However, new cloud computing technologies bring attractive features to improve the operations and elasticity of scientific distrib...

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Detalles Bibliográficos
Autores principales: Panitkin, S, Barreiro Megino, F, Caballero Bejar, J, Benjamin, D, DiGirolamo, A, Gable, I, Hendrix, V, Hover, J, Kucharczuk, K, Medrano LLamas, R, Ohman, H, Paterson, M, Sobie, R, Taylor, R, Walker, R, Zaytsev, A
Lenguaje:eng
Publicado: 2013
Materias:
Acceso en línea:http://cds.cern.ch/record/1609060
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author Panitkin, S
Barreiro Megino, F
Caballero Bejar, J
Benjamin, D
DiGirolamo, A
Gable, I
Hendrix, V
Hover, J
Kucharczuk, K
Medrano LLamas, R
Ohman, H
Paterson, M
Sobie, R
Taylor, R
Walker, R
Zaytsev, A
author_facet Panitkin, S
Barreiro Megino, F
Caballero Bejar, J
Benjamin, D
DiGirolamo, A
Gable, I
Hendrix, V
Hover, J
Kucharczuk, K
Medrano LLamas, R
Ohman, H
Paterson, M
Sobie, R
Taylor, R
Walker, R
Zaytsev, A
author_sort Panitkin, S
collection CERN
description The computing model of the ATLAS experiment was designed around the concept of grid computing and, since the start of data taking, this model has proven very successful. However, new cloud computing technologies bring attractive features to improve the operations and elasticity of scientific distributed computing. ATLAS sees grid and cloud computing as complementary technologies that will coexist at different levels of resource abstraction, and two years ago created an R&D working group to investigate the different integration scenarios. The ATLAS Cloud Computing R&D has been able to demonstrate the feasibility of offloading work from grid to cloud sites and, as of today, is able to integrate transparently various cloud resources into the PanDA workload management system. The ATLAS Cloud Computing R&D is operating various PanDA queues on private and public resources and has provided several hundred thousand CPU days to the experiment. As a result, the ATLAS Cloud Computing R&D group has gained a significant insight into the cloud computing landscape and has identified points that still need to be addressed in order to fully utilize this technology.\nThis contribution will explain the cloud integration models that are being evaluated and will discuss ATLAS’ learning during the collaboration with leading commercial and academic cloud providers.
id cern-1609060
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2013
record_format invenio
spelling cern-16090602019-09-30T06:29:59Zhttp://cds.cern.ch/record/1609060engPanitkin, SBarreiro Megino, FCaballero Bejar, JBenjamin, DDiGirolamo, AGable, IHendrix, VHover, JKucharczuk, KMedrano LLamas, ROhman, HPaterson, MSobie, RTaylor, RWalker, RZaytsev, AATLAS Cloud Computing R&D projectDetectors and Experimental TechniquesThe computing model of the ATLAS experiment was designed around the concept of grid computing and, since the start of data taking, this model has proven very successful. However, new cloud computing technologies bring attractive features to improve the operations and elasticity of scientific distributed computing. ATLAS sees grid and cloud computing as complementary technologies that will coexist at different levels of resource abstraction, and two years ago created an R&D working group to investigate the different integration scenarios. The ATLAS Cloud Computing R&D has been able to demonstrate the feasibility of offloading work from grid to cloud sites and, as of today, is able to integrate transparently various cloud resources into the PanDA workload management system. The ATLAS Cloud Computing R&D is operating various PanDA queues on private and public resources and has provided several hundred thousand CPU days to the experiment. As a result, the ATLAS Cloud Computing R&D group has gained a significant insight into the cloud computing landscape and has identified points that still need to be addressed in order to fully utilize this technology.\nThis contribution will explain the cloud integration models that are being evaluated and will discuss ATLAS’ learning during the collaboration with leading commercial and academic cloud providers.ATL-SOFT-SLIDE-2013-827oai:cds.cern.ch:16090602013-10-10
spellingShingle Detectors and Experimental Techniques
Panitkin, S
Barreiro Megino, F
Caballero Bejar, J
Benjamin, D
DiGirolamo, A
Gable, I
Hendrix, V
Hover, J
Kucharczuk, K
Medrano LLamas, R
Ohman, H
Paterson, M
Sobie, R
Taylor, R
Walker, R
Zaytsev, A
ATLAS Cloud Computing R&D project
title ATLAS Cloud Computing R&D project
title_full ATLAS Cloud Computing R&D project
title_fullStr ATLAS Cloud Computing R&D project
title_full_unstemmed ATLAS Cloud Computing R&D project
title_short ATLAS Cloud Computing R&D project
title_sort atlas cloud computing r&d project
topic Detectors and Experimental Techniques
url http://cds.cern.ch/record/1609060
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