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Title: HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

Journal Article · · Computing and Software for Big Science
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  1. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  2. Univ. of Nebraska, Lincoln, NE (United States)
  3. Simons Foundation, New York, NY (United States)
  4. European Organization for Nuclear Research (CERN), Geneva (Switzerland)

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 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. Additionally, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.

Research Organization:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
Grant/Contract Number:
AC02-07CH11359
OSTI ID:
1418149
Report Number(s):
arXiv:1710.00100; FERMILAB-PUB-17-092-CD; 1628463; TRN: US1801245
Journal Information:
Computing and Software for Big Science, Vol. 1, Issue 1; ISSN 2510-2036
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (1)

Towards General Distributed Resource Selection preprint January 2018

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