一句话：提出 FlowScope，通过方法级延迟分布估计与多约束迭代重建，将非侵入式分布式追踪准确率提升 51%。
随着微服务规模和复杂度增长，单个用户请求可能涉及数百个组件，运维与调试愈发困难。非侵入式分布式追踪无需修改应用代码，能显著提升故障排查便利性，但现有框架在追踪准确性上仍有缺陷：或依赖特定线程模型假设而限制适用性，或采用构造不当的因果延迟分布推断请求因果，导致调用链重建错误。为此，作者提出 FlowScope，通过互相关分析估计不同方法类型请求之间的因果延迟分布，并利用这些分布准确评估请求间因果关系的概率，再结合多重约束与迭代算法高效重建追踪。测试平台结果表明，FlowScope 相比 DeepFlow、TraceWeaver 等最先进框架将追踪准确率提升 51%，并已在多个真实场景中成功应用。
As the scale and complexity of microservices increase, their operation and debugging become increasingly challenging. A single user request may involve interactions among hundreds of components. In such complex systems, non-intrusive distributed tracing techniques, which require no modification of application code, significantly enhance the convenience of troubleshooting. However, existing non-intrusive tracing frameworks still exhibit critical shortcomings in achieving tracing accuracy: current approaches either rely on specific thread model assumptions, limiting their applicability, or employ incorrectly constructed causal delay distributions to infer request causality, leading to erroneous trace construction. To overcome these limitations, we propose FlowScope, a non-intrusive distributed tracing framework for microservices that optimizes trace reconstruction through method-level delay distribution estimation. This framework estimates the causal delay distribution between requests of different method types via cross-correlation analysis, then uses these delay distributions to accurately evaluate the probability of causal relationships between requests. Subsequently, it efficiently reconstructs traces by incorporating multiple constraints with an iterative algorithm. Our testbed results indicate that FlowScope improves tracing accuracy by 51% compared to state-of-the-art frameworks (e.g., DeepFlow and TraceWeaver) and has been successfully applied in multiple real-world use cases.
@inproceedings{paperbot11524,
title = {FlowScope: Non-Intrusive Distributed Tracing with Method-Level Delay Estimation for Microservices Troubleshooting},
author = {Yantao Geng and Han Zhang and Zhiheng Wu and Yahui Li and Jilong Wang and Xia Yin},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3744916.3764518}
}