Increased Network Monitoring Support through Topic Modelling

Main Article Content

Joe Steinhauer https://orcid.org/0000-0003-2949-4123
Anders Åhlén https://orcid.org/0000-0002-9671-7676
Tove Helldin
Alexander Karlsson https://orcid.org/0000-0003-2973-3112
Gunnar Mathiason https://orcid.org/0000-0001-7106-0025

Keywords

Topic Modelling, Exploratory Data Analysis, Anomaly Detection, Root Cause Detection, Telecommunication Networks, Network Performance Monitoring

Abstract

To ensure that a wireless telecommunication system is reliably functioning at all times, root-causes of potential network failures need to be identified and remedied, ideally before a noticeable network performance degradation occurs. Network operators are today observing a multitude of key performance indicators (KPIs) and are notified of possible network problems through alarms issued by different parts of the network. However, the number of cascading alarms together with the number of observable KPIs are easily overwhelming the operator’s cognitive capacity. In this paper we show how exploratory data analysis and machine learning, in particular topic modelling, can assist the operator when monitoring network performance and identifying anomalous network behaviour as well as supporting the operator’s analysis of the anomaly and identification of its root-cause.

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