Enhancing fault localization in microservices systems through span-level using graph convolutional networks

In the domain of cloud computing and distributed systems, microservices architecture has become preeminent due to its scalability and flexibility. However, the distributed nature of microservices systems introduces significant challenges in maintaining operational reliability, especially in fault localization. Traditional methods for fault localization are insufficient due to time-intensive and prone to error. Addressing this gap, we present SpanGraph, a novel framework employing graph convolutional networks (GCN) to achieve efficient span-level fault localization. SpanGraph constructs a directed graph from system traces to capture invocation relationships and execution times. It then utilizes GCN for edge representation learning to detect anomalies. Experimental results demonstrate that SpanGraph outperforms all baseline approaches on both the Sockshop and TrainTicket datasets. We also conduct incremental experiments on SpanGraph using unseen traces to validate its generalizability and scalability. Furthermore, we perform an ablation study, sensitivity analysis, and complexity analysis for SpanGraph to further verify its robustness, effectiveness, and flexibility. Finally, we validate SpanGraph’s effectiveness in anomaly detection and fault location using real-world datasets.