用图模型预测微服务尾延迟，速度快且精度高。
STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices
- 将调用链转为图结构，用线性注意力建模跨服务依赖
- 在真实数据上平均降低8.5%误差，32个跨度时提速12倍
- 适合需要快速响应的线上系统监控与SLO管理
精准的端到端尾延迟预测对微服务系统中主动SLA管理至关重要。然而，在保持大规模推理效率的同时，建模长程依赖传播及非平稳、突发性负载仍具挑战。我们提出STLGT（可扩展的基于追踪的线性图Transformer），一种针对每个API的预测器，将调用追踪编码为跨度图，用于多步p95尾延迟预测。STLGT采用结构感知的线性图Transformer，以与跨度图规模线性相关的推断时间传播跨服务依赖，并使用解耦的时间模块捕捉工作负载动态。在个性化教育微服务应用、DeathStarBench及阿里巴巴真实追踪数据上，STLGT相比PERT-GNN平均降低8.5% MAPE，且在N=32时达到最高12倍的CPU推理加速，对应预处理后阿里巴巴数据的最大跨度图规模。消融实验进一步验证了各组件的有效性，尤其在突发流量下表现优异。
原文摘要 · Abstract (English)
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, DeathStarBench, and Alibaba traces, STLGT improves forecasting accuracy over PERT-GNN by 8.5% MAPE on average and achieves up to 12x faster CPU inference at N=32, matching the maximum span graph size after preprocessing the Alibaba traces. Ablation studies further demonstrate the effectiveness of each component, especially under bursty traffic.
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