Predictive Failure Analysis and Fault Recovery in Microservice Mesh Networks Using Temporal Graph Neural Networks

Microservice architectures deployed on cloud platforms rely heavily on service mesh networks to facilitate dynamic routing, observability, and security. However, the high operational complexity and fine-grained inter-service dependencies make microservice ecosystems susceptible to cascading failures, where a degradation in a single downstream service rapidly propagates across the system. Traditional monitoring approaches rely on static thresholds or reactive fault detection, which often trigger after service level objectives (SLOs) have already been violated. In this paper, we propose a novel predictive failure analysis and autonomous fault recovery framework based on Temporal Graph Neural Networks (TGNNs) tailored for microservice mesh networks. By modeling the service call graph as a dynamic, time-varying attributed graph, our framework captures both topological spatial dependencies and time-series telemetry metrics, including HTTP status rates, latency distribution, and compute resource utilization. The TGNN model predicts service degradation up to 45 seconds prior to failure with an F1-score of 0.94. Integrated with a closed-loop control plane via Istio sidecar proxies, the framework automatically enacts proactive traffic shifting, adaptive circuit breaking, and targeted pod scaling. Experimental evaluations on benchmark microservice environments demonstrate a 68% reduction in Mean Time to Recovery (MTTR) and a 91% prevention rate of cascading outages compared to baseline reactive recovery systems.