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Communication Dans Un Congrès Année : 2013

Scalable and Fast Root Cause Analysis Using Inter Cluster Inference

L. Bennacer
  • Fonction : Auteur
CIR
A. A. Chibani
  • Fonction : Auteur
Yacine Y. Amirat
  • Fonction : Auteur
  • PersonId : 16807
  • IdHAL : lab-lissi
A Mellouk
  • Fonction : Auteur
CIR

Résumé

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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Dates et versions

hal-01676592 , version 1 (05-01-2018)

Identifiants

  • HAL Id : hal-01676592 , version 1

Citer

L. Bennacer, L. Ciavaglia, S. Ghamri‐doudane, A. A. Chibani, Yacine Y. 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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