Impact of Rare Alarms on Event Correlation

Anne Bouillard (1, 2, 3) , Aurore Junier (4) , Benoit Ronot (5)

1 LINCS - Laboratory of Information, Network and Communication Sciences
2 LIENS - Laboratoire d'informatique de l'école normale supérieure
3 DYOGENE - Dynamics of Geometric Networks
4 SUMO - SUpervision of large MOdular and distributed systems
5 Alcatel Lucent Bell Labs

Nowadays, telecommunication systems are growing more and more complex, generating a large amount of alarms that cannot be effectively managed by human operators. The problem is to detect significant combinations of alarms describing an issue in real-time. In this article, we present a powerful heuristic algorithm that constructs dependency graphs of alarm patterns. More precisely, it highlights patterns extracted from an alarm flow obtained from a learning process with a small footprint on network management system performance. This algorithm helps to detect issues in real-time by effectively delivering concise alarm patterns. Furthermore, it allows the proactive analysis of the functioning of a network by computing the general trends of this network. We evaluate our algorithm on an optical network alarm data set of an existing operator. We find similar results as the expert analysis performed for this operator by Alcatel-Lucent Customer Services.

Domaines: Réseaux et télécommunications [cs.NI]

Anne Bouillard, Aurore Junier, Benoit Ronot. Impact of Rare Alarms on Event Correlation. CNSM - 9th international Conference on Network and Service Management, Oct 2013, Zürich, Switzerland. ⟨hal-00920685⟩