LTFP: Lead-Time-Aware Failure Prediction Based on Service GNNs for AIOps

Failures in cloud-native systems can disrupt service availability and system reliability, while their early symptoms are often weak and dispersed across metrics, logs, traces, and interdependent services. Existing methods commonly flatten heterogeneous telemetry or model monitoring variables without preserving service identities. We propose LTFP, a lead-time-aware failure-prediction framework whose graph nodes represent services. LTFP uses modality-specific temporal encoders and gated fusion to form service states, as well as an edge-weight-aware Graph Attention Network to propagate these states over a sparse hybrid graph constructed from known dependencies and training-fitted correlations. Joint graph-level and node-level heads predict whether a failure will occur within a configured future window and rank likely responsible services. We evaluate LTFP on seven subsets from three representative cloud-native systems. Comparisons with representative source-code baselines are reported at the pipeline level, with each method retaining its original learning objective and input configuration. At the 600 s prediction-window setting, LTFP obtains a macro-average window-level precision, recall, and F1 of 92.0%, 90.4%, and 90.5%, respectively. Together with the localization and ablation results, these findings support the effectiveness of service-centered multimodal modeling under the evaluated protocol.