Dealing With Alarms in Optical Networks Using an Intelligent System

Millions of alarms in the optical layer may appear in optical transport networks every month, which brings great challenges to network operation, administration and maintenance. In this paper, we deal with this problem and propose a method of alarm pre-processing and correlation analysis for this network. During the alarm pre-processing, we use the method of combined time series segmentation and time sliding window to extract the alarm transactions, and then we use the algorithm of combined K -means and back propagation neural network to evaluate the alarm importance quantitatively. During the alarm correlation analysis, we modify a classic rule mining algorithm, i.e., Apriori algorithm, into a Weighted Apriori to find the high-frequency chain alarm sets among the alarm transactions. Through the actual alarm data from the record in the optical layer of a provincial backbone of China Telecom, we conducted experiments and the results show that our method is able to perform effectively the alarm compressing, alarm correlating, and chain alarm mining. By parameter adjustment, the alarm compression rate is able to vary from 60% to 90% and the average fidelity of chain alarm mining keeps around 84%. The results show our approach and method is promising for trivial alarm identifying, chain alarm mining, and root fault locating in existing optical networks.