Intelligent Root Cause Localization in MicroService Systems: A Survey and New Perspectives
Nan Fu, Guang Cheng, Yue Teng, Guangye Dai, Shui Yu, Zihan Chen · ACM Computing Surveys · 2025
Root cause localization is the process of monitoring system behavior and analyzing fault patterns from behavioral data. It is applicable in software development, network operations, and cloud computing. However, with the advent of microservice architectures and cloud-native technologies, root cause localization becomes an arduous task. Frequent updates in systems result in large-scale data and complex dependencies. Traditional analysis methods relying on manual experience and predefined rules have limited data processing and cannot learn new fault patterns from historical knowledge. Artificial Intelligence techniques have emerged as powerful tools to leverage historical knowledge and are now widely used in root cause localization. In this article, we provide a structured overview and a qualitative analysis of root cause localization in microservice systems. To begin with, we review the literature in this area and abstract a workflow of root cause localization, including multimodal data collection, intelligent root cause analysis, and performance evaluation. In particular, we highlight the role played by Artificial Intelligence techniques. Finally, we discuss some open challenges and research directions and propose an end-to-end framework from a new perspective, providing insights for future works.