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Anomaly detection in the CERN cloud infrastructure / Giordano, Domenico (CERN) ; Paltenghi, Matteo (CERN) ; Metaj, Stiven (CERN) ; Dvorak, Antonin (Prague, Inst. Phys.)
Anomaly detection in the CERN OpenStack cloud is a challenging task due to the large scale of the computing infrastructure and, consequently, the large volume of monitoring data to analyse. The current solution to spot anomalous servers in the cloud infrastructure relies on a threshold-based alarming system carefully set by the system managers on the performance metrics of each infrastructure’s component. [...]
2021 - 10 p. - Published in : EPJ Web Conf. 251 (2021) 02011 Fulltext: PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.02011

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Preparing Distributed Computing Operations for the HL-LHC Era With Operational Intelligence / Di Girolamo, Alessandro (CERN) ; Legger, Federica (INFN, Turin) ; Paparrigopoulos, Panos (CERN) ; Schovancová, Jaroslava (CERN) ; Beermann, Thomas (Wuppertal U.) ; Boehler, Michael (Freiburg U.) ; Bonacorsi, Daniele (Bologna U. ; INFN, Bologna) ; Clissa, Luca (Bologna U. ; INFN, Bologna) ; Decker de Sousa, Leticia (Bologna U. ; INFN, Bologna) ; Diotalevi, Tommaso (Bologna U. ; INFN, Bologna) et al.
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 experiment goals, by allowing timely delivery of scientific results. [...]
2022 - 10 p. - Published in : Front. Big Data 4 (2022) 753409 Fulltext: PDF;

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Time Series Anomaly Detection for CERN Large-Scale Computing Infrastructure / Paltenghi, Matteo
Anomaly Detection in the CERN Data Center is a challenging task due to the large scale of the computing infrastructure and the large volume of data to monitor [...]
CERN-THESIS-2020-282 - Milano : Politecnico di Milano, 2020-10-02. - 119 p.

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