Alarms correlation in telecommunication networks

Anne Bouillard 1, 2, 3 Aurore Junier 4 Benoit Ronot 5
3 DYOGENE - Dynamics of Geometric Networks
CNRS - Centre National de la Recherche Scientifique : UMR8548, Inria Paris-Rocquencourt, DI-ENS - Département d'informatique de l'École normale supérieure
4 SUMO - SUpervision of large MOdular and distributed systems
IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL, Inria Rennes – Bretagne Atlantique
Abstract : Nowadays telecommunication systems are growing more and more complex, generating huge amount of alarms that cannot be effectively managed by human operators. The problematic is to detect in real-time significant combinations of alarms that describe an issue. In this article, we present a powerful heuristic algorithm that constructs alarm patterns dependency graphs. More precisely, it is able to highlight patterns extracted from an alarm flow learning process with a small footprint on network management system performance. This algorithm is first relevant to real-time issues detection by effectively delivering their concise alarm patterns. And secondly it allows the proactive analysis of network health by retrieving the general trends of a network. We challenge our algorithm to an optical network alarms data set of an existing operator. We find immediately similar results to the experts analysis performed for this operator by Alcatel-Lucent Customer Services.
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Anne Bouillard, Aurore Junier, Benoit Ronot. Alarms correlation in telecommunication networks. [Research Report] RR-8321, INRIA. 2013, pp.17. ⟨hal-00838969⟩

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