002291124 001__ 2291124
002291124 005__ 20241220052635.0
002291124 0247_ $$2DOI$$9arXiv$$a10.1007/s41781-017-0001-9$$qpublication
002291124 0248_ $$aoai:cds.cern.ch:2291124$$pcerncds:FULLTEXT$$pcerncds:CERN:FULLTEXT$$pcerncds:CERN
002291124 037__ $$9arXiv$$aarXiv:1710.00100$$ccs.DC
002291124 037__ $$aFERMILAB-PUB-17-092-CD
002291124 035__ $$9arXiv$$aoai:arXiv.org:1710.00100
002291124 035__ $$9Inspire$$aoai:inspirehep.net:1628463$$d2024-12-19T19:44:09Z$$h2024-12-20T03:09:28Z$$mmarcxml$$ttrue$$uhttps://inspirehep.net/api/oai2d
002291124 035__ $$9Inspire$$a1628463
002291124 041__ $$aeng
002291124 100__ $$aHolzman, Burt$$iINSPIRE-00090844$$jORCID:0000-0001-5235-6314$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 245__ $$9arXiv$$aHEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation
002291124 269__ $$c2017-09-29
002291124 260__ $$c2017-09-29
002291124 300__ $$a15 p
002291124 500__ $$a* Temporary entry *
002291124 500__ $$9Inspire$$a* Temporary entry *
002291124 500__ $$9arXiv$$a15 pages, 9 figures
002291124 520__ $$9Springer$$aHistorically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized both local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. In addition, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.
002291124 520__ $$9arXiv$$aHistorically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing nterest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized both local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. In addition, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.
002291124 540__ $$3preprint$$aarXiv-1.0$$uhttp://arxiv.org/licenses/nonexclusive-distrib/1.0/
002291124 65017 $$2arXiv$$aphysics.comp-ph
002291124 65017 $$2SzGeCERN$$aOther Fields of Physics
002291124 65017 $$2arXiv$$acs.DC
002291124 65017 $$2SzGeCERN$$aComputing and Computers
002291124 690C_ $$aCERN
002291124 690C_ $$aARTICLE
002291124 693__ $$aCERN LHC$$eCMS
002291124 700__ $$aBauerdick, Lothar A.T.$$iINSPIRE-00065219$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aBockelman, Brian$$iINSPIRE-00009547$$jORCID:0000-0003-2981-3809$$tGRID:grid.24434.35$$uNebraska U.$$vNebraska U. - Lincoln - NE - USA
002291124 700__ $$aDykstra, Dave$$iINSPIRE-00308686$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aFisk, Ian$$iINSPIRE-00081515$$tGRID:grid.430264.7$$uNew York U.$$vSimons Foundation - New York - NY - USA
002291124 700__ $$aFuess, Stuart$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aGarzoglio, Gabriele$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aGirone, Maria$$iINSPIRE-00282515$$tGRID:grid.9132.9$$uCERN$$vCERN - Geneva - Switzerland
002291124 700__ $$aGutsche, Oliver$$iINSPIRE-00319172$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aHufnagel, Dirk$$iINSPIRE-00042121$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aKim, Hyunwoo$$iINSPIRE-00006423$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aKennedy, Robert$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aMagini, Nicolo$$iINSPIRE-00310399$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aMason, David$$iINSPIRE-00105472$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aSpentzouris, Panagiotis$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aTiradani, Anthony$$iINSPIRE-00208578$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aTimm, Steve$$iINSPIRE-00275893$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 700__ $$aVaandering, Eric W.$$iINSPIRE-00133143$$tGRID:grid.417851.e$$uFermilab$$vFermi National Accelerator Laboratory - Batavia - IL - USA
002291124 773__ $$c1$$mpublication$$pComput. Softw. Big Sci.$$v1$$xComput Softw Big Sci (2017) 1:1$$y2017
002291124 8564_ $$uhttp://lss.fnal.gov/archive/2017/pub/fermilab-pub-17-092-cd.pdf$$yFermilab Accepted Manuscript
002291124 8564_ $$81364130$$s1057805$$uhttps://cds.cern.ch/record/2291124/files/arxiv:1710.00100.pdf
002291124 8564_ $$81366950$$s902386$$uhttps://cds.cern.ch/record/2291124/files/fermilab-pub-17-092-cd.pdf$$yFulltext
002291124 960__ $$a13
002291124 980__ $$aARTICLE