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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...
Autores principales: | , , , , , , , , , , , , , , , |
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Lenguaje: | eng |
Publicado: |
2013
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Materias: | |
Acceso en línea: | http://cds.cern.ch/record/1609060 |
_version_ | 1780931893454176256 |
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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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