Microservice Root Cause Analysis With Limited Observability Through Intervention Recognition in the Latent Space

Many failure root cause analysis (RCA) algorithms for microservices have been proposed with the widespread adoption of microservices systems. Existing algorithms generally focus on RCA with ranking single-level (e.g. metric-level or service-level) root cause candidates (RCCs) with comprehensive monitoring metrics. However, many heterogeneous RCCs exist with limited observability in real-world microservices systems. Further, we find that the limited observability may result in inaccurate RCA through real-world failures in eBay. In this paper, for the first time, we propose to "model RCCs as latent variables". The core idea is to infer the status of RCCs as latent variables with related monitoring metrics instead of directly extracting features from only the observable metrics. Based on this, we propose LatentScope, an unsupervised RCA framework with heterogeneous RCCs under limited observability. A dual-space graph is proposed to model both observable and unobservable variables, with many-to-many relationships between spaces. To achieve fast inference of latent variables and RCA, we propose the LatentRegressor algorithm, which includes Regression-based Latent-space Intervention Recognition (RLIR) to achieve intervention recognition-based RCA in latent space. LatentScope has been deployed in eBay's production environment and evaluated on both eBay's real-world failures and a testbed dataset. The evaluation results show that, compared with baseline algorithms, our model significantly improves the Top-1 recall by 9.7%-57.9%. The source code of LatentScope and the dataset are available at https://github.com/NetManAIOps/LatentScope.