Fault Management Based On Event Correlation And Data Mining

As one of the five greatest Network Management Function Areas, Fault Management takes charge of the detection, diagnosis and restoration of network fault, the effectiveness and power of it correlate with the availability and reliability of managed network. Early in Fault Management, by assisting operators in analyzing the alarm information and pinpointing the nature and location of faults, network downtime, which can be very costly, can be significantly reduced. Considering it, AI techniques will be imported to realize the fault management automatization.At present, it is general to apply a single Al intelligent technology to fault diagnosis, the actual state cannot meet the whole demands of the exact fault identification and diagnosis. With regard to it, fault diagnosis is divided into two parts in this thesis: fault localization and fault cause diagnosis, different Al techniques can be applied to these two parts. Moreover, a fault diagnosis technology based on event correlation and data mining has been worked out by the writer.A distributed event correlation model based on CBR/MBR is presented in this thesis to implement fault localization. It can reduce the amount of information presented to network operator by filtering out unnecessary or irrelevant events. Simultaneously, the semantic content of the information presented can be increased by the correlation process, hence helping to establish the underlying problem or condition which produced the events.For the second part, a fault diagnosis technology based on CBR has been brought forth. This technology which combines Decision Trees and K Nearest Neighbor, can mine the relative similarity of latent-item in the course of case search. This algorithm will settle the bottleneck to a certain extent.For evaluating the new technology in study, the writer tests algorithms in real network environment. They show good performance of fault diagnosis.