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New Graph Transformer models improve microservice tail latency prediction

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 3 sources

Two new research papers propose advanced methods for predicting tail latency in microservice systems. The first, STLGT, uses a graph transformer to model service dependencies and a temporal module for workload dynamics, showing improved accuracy and speed over existing methods. The second, USRFNet, employs a dual-stream learning approach to separate traffic and resource metrics, incorporating a gradient modulation strategy to address training imbalances and achieving significant reductions in prediction error. AI

Summary written by gemini-2.5-flash-lite from 3 sources. How we write summaries →

IMPACT These new models offer improved accuracy and efficiency for predicting microservice tail latency, aiding proactive SLO management and system reliability.

RANK_REASON Two academic papers published on arXiv present novel methods for tail latency prediction in microservices.

Read on arXiv cs.LG →

COVERAGE [3]

  1. arXiv cs.AI TIER_1 · Yongliang Ding, Qigong Bi, Peng Pu ·

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

    arXiv:2604.26422v1 Announce Type: cross Abstract: 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 ef…

  2. arXiv cs.AI TIER_1 · Peng Pu ·

    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 …

  3. arXiv cs.LG TIER_1 · Wenzhuo Qian, Hailiang Zhao, Jiayi Chen, Ziqi Wang, Tianlv Chen, Zhiwei Ling, Xinkui Zhao, Kingsum Chow, Albert Y. Zomaya, Shuiguang Deng ·

    Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation

    arXiv:2508.01635v2 Announce Type: replace Abstract: Microservice architectures enable scalable cloud-native applications; however, the distributed nature of these systems complicates the maintenance of strict Service Level Objectives. Accurately predicting window-level P95 tail l…

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