Automated Network Fault Management
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With the recent growth of telecommunication networks, fault management has gained much importance. Since it is difficult for humans to manage large networks, automation of many of these functions has attracted much attention. Some of the ideas proposed for automating such functions include the use of artificial intelligence techniques. Neural help to analyze large volumes of numerical data. Expert systems help to analyze observed symptoms and identify the cause using a rule-based approach. However, research in artificial intelligence has shown that when either of these two methods is used alone, several weaknesses are observed in the resulting system. Thus, some other methodology would be required for tackling such large problems.
In this thesis, an approach involving the use of a hybrid system involving both neural networks and expert systems for performing automated network fault management is investigated. Data networks using the X.25 protocol are considered. A minimum cost routing scheme is used for re-routing future calls given the occurrence of a fault. A method for partitioning the data (obtained from the X.25 network) between the neural network and the expert system is suggested. Radial basis function networks are used as the neural network architecture for performing fault classification using performance data. Queries are provided for the expert system to determine the type of fault that occurred using the results of the neural network, together with alarms, SNMP traps, and X.25 SNMP statistics.