STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices
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. STLGT uses a structure-aware linear graph Transformer to propagate cross-service dependencies with inference time linear in span graph size, and a decoupled temporal module to capture workload dynamics. Across a personalized education microservice application, D...
| Authors | Yongliang Ding, Qigong Bi, Peng Pu |
|---|---|
| Primary category | cs.LG |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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