GUIDE - GNN based Unified Incident Detection for Microservices Application Deployments

Microservices deployments in the real-world present significant challenges in detecting and localizing performance bottlenecks due to their scale, complexity, and dynamic interactions. This paper presents GUIDE, a GNN-based framework for unified incident detection and bottleneck localization, leveraging multisource telemetry and a customizable incident trigger warning mechanism. Specifically, GUIDE employs a novel integration of Graph Attention Networks, temporal embeddings, and an expert classifier to predict and localize bottlenecks efficiently in practice. Evaluation results on real-world traces collected from Observea live, cloud-native platform-show that GUIDE achieves an F1score of 87% for anomaly detection and 84% for bottleneck localization, outperforming existing baselines. Additionally, GUIDE's incident trigger warning mechanism achieves an F1-score of 85%, ensuring early and accurate detection of system failures.