Efficient Unsupervised Latency Culprit Ranking in Distributed Traces with GNN and Critical Path Analysis

Microservices offer the benefits of scalable flexibility and rapid deployment, making them a preferred architecture in today's IT industry. However, their dynamic nature increases their susceptibility to failures, highlighting the need for effective troubleshooting strategies. Current methods for pinpointing issues in microservices often depend on impractical supervision or rest on unrealistic assumptions. We propose a novel approach using graph unsupervised neural networks and critical path analysis to address these limitations. Our experiments on four open-source microservice benchmarks show significant results, with top-1 accuracy ranging from 86.4% to 96%, over 6% enhancement compared to existing methods. Moreover, our approach reduces training time by 5.6 times compared to similar works on the same datasets.