Root Cause Analysis of Anomalies Based on Graph Convolutional Neural Network

With the gradual increase of network complexity and network scale in the cloud environment, Root Cause Analysis (RCA) of node failures has become a systematic problem of great research significance. This paper proposes Graph-Attention-Sage (GASage) algorithm, which is a fault RCA algorithm and scheme. The algorithm solves the RCA by incorporating TOPK sampling and Attention-Aggregation with GraphSage algorithm in large-scale and complex microservice network environment. The GASage algorithm is based on graph convolutional neural network and graph attention mechanism, which profoundly combines the characteristics of network fault RCA problems. TOPK sampling mechanism is applied in GASage to select the neighboring nodes with the top