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Time-series Anomaly Detection Applied to Log-based Diagnostic System Using Unsupervised Machine Learning Approach

Francesco Minarini, Leticia Decker
<span title="2020-09-09">2020</span> <i title="Zenodo"> Zenodo </i> &nbsp;

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Annually, the Large Hadron Collider (LHC) demands a huge amount of computing resources to deal with petabytes of produced data. In the next years, a scheduled LHC upgrade will increase at least 10 times the computational workload on the Worldwide LHC Computing Grid (WLCG). As a consequence, an upgrade in the computing infrastructure that supports the physics experiments is also required. All WLCG computing centers are focused on the development of hardware and software solutions as machine
more &raquo; ... ing log-based predictive maintenance systems. This work presents an original general-purpose diagnosis system to identify critical activity periods of services solving a binary anomaly detection problem using an unsupervised support vector machine approach.
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Francesco Minarini, Leticia Decker. "Time-series Anomaly Detection Applied to Log-based Diagnostic System Using Unsupervised Machine Learning Approach." Zenodo (2020)