학술논문
Operational Intelligence for Distributed Computing Systems for Exascale Science.
Document Type
Article
Author
Doglioni, C.; Kim, D.; Stewart, G.A.; Silvestris, L.; Jackson, P.; Kamleh, W.; Di Girolamo, Alessandro; Legger, Federica; Paparrigopoulos, Panos; Klimentov, Alexei; Schovancová, Jaroslava; Kuznetsov, Valentin; Lassnig, Mario; Clissa, Luca; Rinaldi, Lorenzo; Sharma, Mayank; Bakhshiansohi, Hamed; Zvada, Marian; Bonacorsi, Daniele; Rossi Tisbeni, Simone
Source
Subject
*DISTRIBUTED computing
*ARTIFICIAL intelligence
*COMPUTER software development
*MACHINE learning
*DATA analysis
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Language
ISSN
2101-6275
Abstract
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. [ABSTRACT FROM AUTHOR]