EPJ Web Conf.
Volume 245, 202024th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|Number of page(s)||8|
|Section||3 - Middleware and Distributed Computing|
|Published online||16 November 2020|
Operational Intelligence for Distributed Computing Systems for Exascale Science
CERN, Geneva, Switzerland
2 INFN Turin, Italy
3 Cornell University, USA
4 Moscow State University, Moscow, Russia
6 Brookhaven National Laboratory (BNL), USA
7 University of Nebraska-Lincoln, Lincoln, NE, USA
8 University of Bologna, Bologna, Italy
9 INFN Bologna, Italy
10 INFN-CNAF Bologna, Italy
11 Moscow Center of Fundamental and Applied Mathematics, Moscow, Russia
Published online: 16 November 2020
In the near future, large scientific collaborations will face unprecedented computing challenges. Processing and storing exabyte datasets require a federated infrastructure of distributed computing resources. The current systems have proven to be mature and capable of meeting the experiment goals, by allowing timely delivery of scientific results. However, a substantial amount of interventions from software developers, shifters and operational teams is needed to efficiently manage such heterogeneous infrastructures. A wealth of operational data can be exploited to increase the level of automation in computing operations by using adequate techniques, such as machine learning (ML), tailored to solve specific problems. The Operational Intelligence project is a joint effort from various WLCG communities aimed at increasing the level of automation in computing operations. We discuss how state-of-the-art technologies can be used to build general solutions to common problems and to reduce the operational cost of the experiment computing infrastructure.
© The Authors, published by EDP Sciences, 2020
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