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Operational Intelligence for Distributed Computing Systems for Exascale Scienceстатья
Информация о цитировании статьи получена из
Web of Science
Дата последнего поиска статьи во внешних источниках: 8 сентября 2021 г.
- Авторы: 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, Giommi Luca, Decker de Sousa Leticia, Diotalevi Tommaso, Grigorieva Maria, Padolski Sergey
- Сборник: EPJ Web Conf
- Том: 245
- Год издания: 2020
- Номер статьи: 03017
- DOI: 10.1051/epjconf/202024503017
- Аннотация: 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.
- Добавил в систему: Григорьева Мария Александровна