一句话：提出 TraceContrast，结合对比序列模式挖掘与频谱分析，从 trace 中高效定位微服务性能问题的多维根因。
现代微服务系统因动态复杂的交互和运行时环境而日益复杂，容易由运行时环境、通信、协调或服务实现等多种原因引发性能问题。Trace 记录了请求在系统中的详细执行过程，已被广泛用于性能问题诊断。通过识别异常 trace 中常见而正常 trace 中罕见的执行过程与属性值组合，工程师可将根因定位到更小范围，但 trace 结构复杂且属性组合数量庞大，导致搜索空间巨大。本文提出 TraceContrast，一种基于 trace 的多维度根因定位方法。它用序列表示描述 trace 的复杂结构及每个 span 的属性，并在此基础上结合对比序列模式挖掘与频谱分析，高效定位多维根因。在广泛使用的微服务基准上的实验表明，TraceContrast 在多维和实例维度根因定位上均显著优于现有方法，且效率高，并行执行可进一步提升效率。
Modern microservice systems have become increasingly complicated due to the dynamic and complex interactions and runtime environment. It leads to the system vulnerable to performance issues caused by a variety of reasons, such as the runtime environments, communications, coordinations, or implementations of services. Traces record the detailed execution process of a request through the system and have been widely used in performance issues diagnosis in microservice systems. By identifying the execution processes and attribute value combinations that are common in anomalous traces but rare in normal traces, engineers may localize the root cause of a performance issue into a smaller scope. However, due to the complex structure of traces and the large number of attribute combinations, it is challenging to find the root cause from the huge search space. In this paper, we propose TraceContrast, a trace-based multi-dimensional root cause localization approach. TraceContrast uses a sequence representation to describe the complex structure of a trace with attributes of each span. Based on the representation, it combines contrast sequential pattern mining and spectrum analysis to localize multi-dimensional root causes efficiently. Experimental studies on a widely used microservice benchmark show that TraceContrast outperforms existing approaches in both multi-dimensional and instance-dimensional root cause localization with significant accuracy advantages. Moreover, Trace-Contrast is efficient and its efficiency can be further improved by parallel execution.
@inproceedings{paperbot11250,
title = {Trace-based Multi-Dimensional Root Cause Localization of Performance Issues in Microservice Systems},
author = {Chenxi Zhang and Zhen Dong and Xin Peng and Bicheng Zhang and Miao Chen},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3639088}
}