TopoRCA: A Lightweight Root Cause Analysis System Based on Application Topology

The advent of the cloud-native era, driven by technologies like cloud computing, big data, and virtualization, has promoted the widespread adoption of microservices architecture in application development. The complex network environment and componentized distributed deployments have introduced new challenges in application performance monitoring. This paper introduces TopoRCA, a system that leverages application topology to identify the root causes of issues in microservices applications. TopoRCA utilizes lightweight machine learning models for anomaly detection, locating faults within the system. It also efficiently and intelligently infers the root cause behind system failures by correlating instance node metrics and service call metrics. We use a microservices dataset consisting of 10 service instances in our experiment. Our experimental results demonstrate that 42% of the root causes are ranked first, 61% in the top three, and 80% in the top five by TopoRCA.