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Semantic feature selection for network telemetry event description

Abstract : Model driven telemetry (MDT) enables the real-time collection of hundreds of thousands of counters on large-scale networks, with contextual information to each counter provided in the telemetry data structure definition. Explaining network events in such datasets implies substantial analysis by a domain expert. This paper presents an semantic feature selection method, to find the most important counters which describe a given event in a telemetry dataset, and facilitate the explanation process. This paper proposes a metric for estimating the importance of features in a dataset with descriptive feature names, to find those that are most meaningful to a human. With this estimation, this paper presents a cross-entropy based metric describing the quality of a selection of counters, which is combined with the data behavior to define an optimization goal. The computation of optimal selections distills intelligible and precise selections of counters with adjustable verbosity, and describes events with a few selected counters outlining the root cause of network events.
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Contributor : Thomas Heide Clausen Connect in order to contact the contributor
Submitted on : Wednesday, March 17, 2021 - 12:18:53 PM
Last modification on : Friday, March 19, 2021 - 3:09:34 AM
Long-term archiving on: : Monday, June 21, 2021 - 8:56:30 AM


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Thomas Feltin, Parisa Foroughi, Wenqin Shao, Frank Brockners, Thomas Heide Clausen. Semantic feature selection for network telemetry event description. 2020 IEEE/IFIP Network Operations and Management Symposium (NOMS 2020), Apr 2020, Budapest, Hungary. ⟨10.1109/NOMS47738.2020.9110382⟩. ⟨hal-03171973⟩



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