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Space-time pattern extraction in alarm logs for network diagnosis

Abstract : Increasing size and complexity of telecommunication networks make troubleshooting and network management more and more critical. As analyzing a log is cumbersome and time consuming, experts need tools helping them to quickly pinpoint the root cause when a problem arises. A structure called DIG-DAG able to store chain of alarms in a compact manner according to an input log has recently been proposed. Unfortunately, for large logs, this structure may be huge, and thus hardly readable for experts. To circumvent this problem, this paper proposes a framework allowing to query a DIG-DAG in order to extract patterns of interest, and a full methodology for end-to-end analysis of a log.
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Submitted on : Thursday, April 2, 2020 - 6:22:21 PM
Last modification on : Friday, July 30, 2021 - 2:46:00 PM


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Achille Salaün, Anne Bouillard, Marc-Olivier Buob. Space-time pattern extraction in alarm logs for network diagnosis. MLN 2019: 2nd IFIP International Conference on Machine Learning for Networking, Dec 2019, Paris, France. pp.134-153, ⟨10.1007/978-3-030-45778-5_10⟩. ⟨hal-02484330v2⟩



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