CERN Accelerating science

Article
Report number	arXiv:1710.00100 ; FERMILAB-PUB-17-092-CD
Title	HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation
Author(s)	Holzman, Burt (Fermilab) ; Bauerdick, Lothar A.T. (Fermilab) ; Bockelman, Brian (Nebraska U.) ; Dykstra, Dave (Fermilab) ; Fisk, Ian (New York U.) ; Fuess, Stuart (Fermilab) ; Garzoglio, Gabriele (Fermilab) ; Girone, Maria (CERN) ; Gutsche, Oliver (Fermilab) ; Hufnagel, Dirk (Fermilab) ; Kim, Hyunwoo (Fermilab) ; Kennedy, Robert (Fermilab) ; Magini, Nicolo (Fermilab) ; Mason, David (Fermilab) ; Spentzouris, Panagiotis (Fermilab) ; Tiradani, Anthony (Fermilab) ; Timm, Steve (Fermilab) ; Vaandering, Eric W. (Fermilab)
Publication	2017-09-29
Imprint	2017-09-29
Number of pages	15
Note	* Temporary entry * * Temporary entry * 15 pages, 9 figures
In:	Comput. Softw. Big Sci. 1 (2017) 1
DOI	10.1007/s41781-017-0001-9 (publication)
Subject category	physics.comp-ph ; Other Fields of Physics ; cs.DC ; Computing and Computers
Accelerator/Facility, Experiment	CERN LHC ; CMS
Abstract	Historically, 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.
Copyright/License	preprint: (License: arXiv-1.0)

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