AUTHOR=Di Girolamo Alessandro , Legger Federica , Paparrigopoulos Panos , Schovancová Jaroslava , Beermann Thomas , Boehler Michael , Bonacorsi Daniele , Clissa Luca , Decker de Sousa Leticia , Diotalevi Tommaso , Giommi Luca , Grigorieva Maria , Giordano Domenico , Hohn David , Javůrek Tomáš , Jezequel Stephane , Kuznetsov Valentin , Lassnig Mario , Mageirakos Vasilis , Olocco Micol , Padolski Siarhei , Paltenghi Matteo , Rinaldi Lorenzo , Sharma Mayank , Tisbeni Simone Rossi , Tuckus Nikodemas TITLE=Preparing Distributed Computing Operations for the HL-LHC Era With Operational Intelligence JOURNAL=Frontiers in Big Data VOLUME=4 YEAR=2022 URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2021.753409 DOI=10.3389/fdata.2021.753409 ISSN=2624-909X ABSTRACT=

As a joint effort from various communities involved in the Worldwide LHC Computing Grid, the Operational Intelligence project aims at increasing the level of automation in computing operations and reducing human interventions. The distributed computing systems currently deployed by the LHC experiments have proven to be mature and capable of meeting the experimental goals, by allowing timely delivery of scientific results. However, a substantial number of interventions from software developers, shifters, and operational teams is needed to efficiently manage such heterogenous infrastructures. Under the scope of the Operational Intelligence project, experts from several areas have gathered to propose and work on “smart” solutions. Machine learning, data mining, log analysis, and anomaly detection are only some of the tools we have evaluated for our use cases. In this community study contribution, we report on the development of a suite of operational intelligence services to cover various use cases: workload management, data management, and site operations.