一句话：提出基于 trace 与图神经网络的大规模微服务根因分析系统 Sleuth，应对规模导致的 RPC 依赖复杂与经验规则失效问题。
云微服务规模持续增长，导致 RPC 依赖图日益复杂，并加剧 tail-of-scale 效应，使许多检测端到端性能问题根因的经验规则变得不可靠；同时，现有开源微服务基准规模过小，难以在生产规模（数百甚至数千服务与 RPC）上评估性能调试算法。本文提出 Sleuth，一个基于 trace 的根因分析系统，并利用图神经网络对大规模微服务追踪与调用依赖进行建模，以定位端到端性能问题的根因。其核心目标是在大规模微服务场景下实现更可靠的根因分析，并回应现有基准难以支撑生产级评估的问题。
Cloud microservices are being scaled up due to the rising demand for new features and the convenience of cloud-native technologies. However, the growing scale of microservices complicates the remote procedure call (RPC) dependency graph, exacerbates the tail-of-scale effect, and makes many of the empirical rules for detecting the root cause of end-to-end performance issues unreliable. Additionally, existing open-source microservice benchmarks are too small to evaluate performance debugging algorithms at a production-scale with hundreds or even thousands of services and RPCs.
@inproceedings{paperbot10116,
title = {Sleuth: A Trace-Based Root Cause Analysis System for Large-Scale Microservices with Graph Neural Networks},
author = {Yu Gan and Guiyang Liu and Xin Zhang and Qi Zhou and Jiesheng Wu and Jiangwei Jiang},
booktitle = {ASPLOS},
year = {2023},
doi = {10.1145/3623278.3624758}
}