STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices

ArXi:2604.26422v1 Announce Type: cross Accurate end-to-end tail-latency forecasting is critical for proactive SLO management in microservice systems. However, modeling long-range dependency propagation and non-stationary, bursty workloads while maintaining inference efficiency at scale remains challenging. We present STLGT (Scalable Trace-based Linear Graph Transformer), a per-API predictor that encodes traces as span graphs for multi-step p95 tail-latency forecasting.