A SOM-Based of Fault Diagnosis for WAN

• Published in: 2009 International Conference on Industrial and Information Systems pp. 207 - 210
• Main Authors: Pan, Zhi-Song, Wang, Qiong, Ni, Gui-Qiang, Hu, Gu-Yu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2009
• Subjects: Artificial intelligence; Artificial neural networks; Bayesian methods; Computer network management; Fault detection; Fault diagnosis; Humans; Network topology; Self organizing feature maps; Self-organizing; WAN; Wide area networks
• DOI: 10.1109/IIS.2009.114
• ISBN: 0769536182; 9780769536187

As computer networks continue to grow in size and complexity, fault management in todaypsilas high speed telecommunications networks is becoming ever more difficult. As a kernel aspect of network fault management, fault diagnosis by performance data is a process of deducing the exact source of a failure from a set of performance data and fault symptoms. In this paper some existing approach for network fault diagnosis are firstly discussed. It concentrates on analyzing the alarm propagation in the wide area network and a model of fault diagnosis is then proposed. The model is composed of two parts: Self-organizing maps training by historical performance data and online fault diagnosis. From our two simulation experimental results with network performance data, our model achieves 96.64 percent detection rate for four kinds of fault types. The performance analysis carried out shows SOM to be a fast and efficient method for fault diagnosis in WAN.