Grace: Interpretable Root Cause Analysis by Graph Convolutional Network for Microservices

With the development of cloud applications, large monolithic services have been replaced by loosely-coupled and single-purpose microservices. To improve localization performance, deep learning techniques have been widely used for root cause analysis. Existing research focuses on performance, yet the lack of interpretability creates key barriers to applying deep learning models in practice. This paper presents Grace, an interpretable root cause localization framework. Our work has three aims. First, to more accurately localize root causes using a Spatial-Temporal Graph Convolutional Network (STGCN). To the best of our knowledge, we are the first to apply an STGCN in the domain. Second, to design an interpreter that helps engineers to understand the system decision (beyond the simple binary black-box result). Third, we apply our interpreter to two real cases, which replaces expert knowledge to help build prior knowledge for fault type diagnosis. Our results show that Grace consistently achieves improvements over other state-of-the-art models by 4%-140%.