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Scalable and Fast Root Cause Analysis Using Inter Cluster Inference

L. Bennacer 1, 2 L. Ciavaglia 3 S. Ghamri‐doudane 3 A. Chibani 1 Yacine Amirat 1 A. Mellouk 2
1 SIRIUS
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
2 CIR
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
Abstract : The capability to diagnose the root cause of an observed problem precisely and quickly is a desirable feature for large communication networks. However, the design of a technique that is at the same time fast, scalable and accurate is a challenging task. In this paper, we propose a novel method based on inter-cluster inference to overcome the usual limits of fault diagnosis techniques. The approach is based on two important concepts: a cluster decomposition of the dependency graph in order to ensure scalability, and the introduction of duplicated nodes aiming at preserving the end-to-end network view. The evaluation of the proposed approach has demonstrated a significant reduction in the complexity and the computation time of the root cause analysis, since it is based on a set of small-scale dependency graphs.
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https://hal.archives-ouvertes.fr/hal-01676592
Contributor : Yacine Amirat <>
Submitted on : Friday, January 5, 2018 - 5:44:42 PM
Last modification on : Friday, October 4, 2019 - 1:28:06 AM

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  • HAL Id : hal-01676592, version 1

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L. Bennacer, L. Ciavaglia, S. Ghamri‐doudane, A. Chibani, Yacine Amirat, et al.. Scalable and Fast Root Cause Analysis Using Inter Cluster Inference. Proc. Of the IEEE International Conference on Communications, ICC 2013, Jun 2013, Budapest, Hungary. pp.1-6. ⟨hal-01676592⟩

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