A SOM-Based of Fault Diagnosis for WAN

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 fai...

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Bibliographic Details
Published in2009 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
LanguageEnglish
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
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Summary: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.
ISBN:0769536182
9780769536187
DOI:10.1109/IIS.2009.114