Anomaly detection using Unsupervised Machine Learning for Grid computing site operation

A Grid computing site is composed of various services including Grid middleware, such as Computing Element and Storage Element. Text logs produced by the services provide useful information for understanding the status of the services. However, it is a time-consuming task for site administrators to...

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Bibliographic Details
Main Authors: Kishimoto, Tomoe, Tnaka, Junichi, Mashimo, Tetsuro, Sawada, Ryu, Terashi, Koji, Kaneda, Michiru, Saito, Masahiko, Matsui, Nagataka
Format: Conference Proceeding
Language:English
Subjects:
Anomalies
Computational grids
Distributed processing
Machine learning
Middleware
Unsupervised learning
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Summary:A Grid computing site is composed of various services including Grid middleware, such as Computing Element and Storage Element. Text logs produced by the services provide useful information for understanding the status of the services. However, it is a time-consuming task for site administrators to monitor and analyze the service logs every day. Therefore, a support framework has been developed to ease the site administrator’s work. The framework detects anomaly logs using Machine Learning techniques and alerts site administrators. The framework has been examined using real service logs at the Tokyo Tier2 site, which is one of the Worldwide LHC Computing Grid sites. In this paper, a method of the anomaly detection in the framework and its performances at the Tokyo Tier2 site are reported.
ISSN:2100-014X
2101-6275
2100-014X
DOI:10.1051/epjconf/202024507016