Network Fault Alarm Correlation Study Based On Data Mining

The task of telecommunication networks management focuses on monitoring the status of network, which ensures the network to run realiablely and efficiently. As the modern telecom network becomes large scale and its construction goes complex, it is much more important to analysis the alram correlation. Because the result can help the network administrators locate the fault to ensure the network running smoothly. However, traditional alarm correlation analysis methods can hardly work well when networks are complex and changeful due to its relying on expert knowledge, the data mining method can overcome the shortage of traditional methods. With the development of telecommunication networks, it is key issue for network management to extract the alarm correlation rules from massive alarm data to help network administrator to handle the fault.In this thesis, we apply data mining technology to network fault alarm correlation analysis and study mining methods in frequently and non-frequently alarm sequence. The research and innovations are described in details as follows:1. Alarm sequence pattern mining (high-frequency alarm sequence) We research alarm sequence pattern mining in telecom network. Sequence pattern mining is developed based on association rules mining. Nowadays, most sequence pattern mining methods are based on WINEPI algorithm frame. But WINEPI algorithm needs to traversal the database for many times so that it is not very efficiently. In order to overcome this shortage, we proposed a sequence pattern mining FSPM-FP algorithm, which is based on FP-Tree, and then we proposed two increments mining algorithm which are SFSPM-FP algorithm based on the minimal support changed and DFSPM-FP algorithm based on the database changed. Experimental results demonstrate The validity of the algorithm.2. low-frequence alarm association rules miningCurrently those algorithms to mine the alarm association rules are limited to the minimal support, so that they can only obtain the association rules among the frequently occurring alarm events based on high support and high confidence. In the thesis the alarm association rules mining algorithm AARSC is presented, which is used high correlativity and the high confidence. At the same time, we proposed its improved algorithm UAARSC to adapt the database increasing. Experimental result shows AARSC algorithm and UAARSC algorithm can discovery both high-frequency and low-frequency alarm association ruls so that it can make the rules are much more completly and correctly.3,Alarm pattern visualizationTelecom network often changes as the service changes. Alarm visualization can help administrators maintain network devices, so that they can discovery the alarm pattern efficiently and locate and predict the alarm and fault. A new ACASG algorithm is introduced, which is based on spectral graph theory. The algorithms discover the underlying mapping structure lying on a low-dimensional structure based on spectral graph theory and mine alarm pattern by analyzing point construction similarity. Experimental results demonstrate that the algorithm not only discovered correlation among alarms but also acquire the fault in the telecommunications network based on the spectral graph transformation.