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Total Cost of Ownership and Evaluation of Google Cloud Resources for the ATLAS Experiment at the LHC

  • The ATLAS Collaboration
  • iThemba Labs
  • Department of Physics
  • University of South Africa
  • University of Zululand
  • Cadi Ayyad University
  • Departamento de Física Teórica y del Cosmos
  • University of Granada
  • CERN
  • Columbia University
  • Demokritos National Centre for Scientific Research
  • University of Sheffield
  • Harvard University
  • University of Bologna
  • National Institute for Nuclear Physics
  • University of Belgrade
  • University of Siegen
  • Heidelberg University 
  • Indiana University Bloomington
  • CAS - Institute of High Energy Physics
  • University of Science and Technology of China
  • Shanghai Jiao Tong University
  • University of Michigan, Ann Arbor
  • Shandong University
  • University of Arizona
  • Tsinghua University
  • Nanjing University
  • University of Illinois at Urbana-Champaign
  • SLAC National Accelerator Laboratory
  • University of California at Santa Cruz
  • University of Washington
  • Université Paris-Saclay
  • Lawrence Berkeley National Laboratory
  • University College London
  • The University of Tokyo
  • CNRS

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The ATLAS Google Project was established as part of an ongoing evaluation of the use of commercial clouds by the ATLAS Collaboration, in anticipation of the potential future adoption of such resources by WLCG grid sites to fulfil or complement their computing pledges. Seamless integration of Google cloud resources into the worldwide ATLAS distributed computing infrastructure was achieved at large scale and for an extended period of time, and hence cloud resources are shown to be an effective mechanism to provide additional, flexible computing capacity to ATLAS. For the first time a total cost of ownership analysis has been performed, to identify the dominant cost drivers and explore effective mechanisms for cost control. Network usage significantly impacts the costs of certain ATLAS workflows, underscoring the importance of implementing such mechanisms. Resource bursting has been successfully demonstrated, whilst exposing the true cost of this type of activity. A follow-up to the project is underway to investigate methods for improving the integration of cloud resources in data-intensive distributed computing environments and reducing costs related to network connectivity, which represents the primary expense when extensively utilising cloud resources.

Original languageEnglish
Article number2
JournalComputing and Software for Big Science
Volume9
Issue number1
DOIs
StatePublished - Dec 2025

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