一句话：提出基于对比学习与图神经网络的微服务追踪表示学习方法，保留服务调用拓扑信息以提升下游分析性能。
微服务追踪分析（异常检测、故障诊断、尾部采样等）普遍采用机器学习方法，但其预处理通常以 ad-hoc 方式把追踪的结构化特征映射为向量，容易丢失服务操作间的拓扑依赖等信息。本文提出 TraceCRL：对每条追踪构造“操作调用图”，节点为服务操作，边为操作调用并携带调用状态与相关指标的预定义特征；在此基础上用对比学习方法训练图神经网络模型以获得追踪表示，并设计六种追踪数据增强策略以缓解对比学习中的类冲突（class collision）与表示均匀性问题。实验表明，TraceCRL 能显著提升追踪异常检测与离线追踪采样的性能，并验证了增强策略的有效性与方法的效率。
Due to the large amount and high complexity of trace data, microservice trace analysis tasks such as anomaly detection, fault diagnosis, and tail-based sampling widely adopt machine learning technology. These trace analysis approaches usually use a preprocessing step to map structured features of traces to vector representations in an ad-hoc way. Therefore, they may lose important information such as topological dependencies between service operations. In this paper, we propose TraceCRL, a trace representation learning approach based on contrastive learning and graph neural network, which can incorporate graph structured information in the downstream trace analysis tasks. Given a trace, TraceCRL constructs an operation invocation graph where nodes represent service operations and edges represent operation invocations together with predefined features for invocation status and related metrics. Based on the operation invocation graphs of traces TraceCRL uses a contrastive learning method to train a graph neural network-based model for trace representation. In particular, TraceCRL employs six trace data augmentation strategies to alleviate the problems of class collision and uniformity of representation in contrastive learning. Our experimental studies show that TraceCRL can significantly improve the performance of trace anomaly detection and offline trace sampling. It also confirms the effectiveness of the trace augmentation strategies and the efficiency of TraceCRL.
DOI 原文 ·
@inproceedings{paperbot7029,
title = {TraceCRL: contrastive representation learning for microservice trace analysis},
author = {Chenxi Zhang and Xin Peng and Tong Zhou and Chaofeng Sha and Zhenghui Yan and Yiru Chen and Hong Yang},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3549146}
}