Impact of Rare Alarms on Event Correlation

Anne Bouillard 1, 2, 3 Aurore Junier 4 Benoit Ronot 5
3 DYOGENE - Dynamics of Geometric Networks
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
4 SUMO - SUpervision of large MOdular and distributed systems
Inria Rennes – Bretagne Atlantique , IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL
Abstract : 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.
Type de document :
Communication dans un congrès
CNSM - 9th international Conference on Network and Service Management, Oct 2013, Zürich, Switzerland. 2013
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https://hal.inria.fr/hal-00920685
Contributeur : Anne Bouillard <>
Soumis le : jeudi 19 décembre 2013 - 08:58:41
Dernière modification le : mardi 16 janvier 2018 - 15:54:22

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  • HAL Id : hal-00920685, version 1

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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. 2013. 〈hal-00920685〉

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