Root cause analysis of network fault based on random forest

Abstract

Artificial intelligence (AI) has become an important means of network anomaly detection and fault root cause analysis (RCA), but most applications are only for a certain segment of the network. In the process of our research on the end-to-end experience analysis of mobile Internet services, we have summarized a set of network end-to-end root cause analysis methods, mainly using non-orthogonal random forest modeling method, AI-based dynamic threshold adjustment and indicator feature extraction, classification modeling and cross-validation of the poor quality of the entire network and the poor quality of the cells. This method has been verified in practice in the production network. The results of root cause analysis are consistent with the actual situation of the production network up to 96%. Practice has proved that this method has played a positive role in supporting the network operation, and greatly improved the production efficiency.