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Microservice architectures bring many benefits, e.g., faster delivery, improved scalability, and greater autonomy, so they are widely adopted to develop and operate Internet-based applications. How to effectively diagnose the faults of applications with lots of dynamic microservices has become a key to guarantee applications’ performance and reliability. As a microservice performs various behaviors in different workflows of processing requests, existing approaches often cannot accurately locate the root cause of an application with interactive microservices in a dynamic deployment environment. We propose a workflow-aware automatic fault diagnosis approach for microservice-based applications with statistics. We characterize traces across microservices with calling trees, and then learn trace patterns as baselines. For the faults affecting the workflows of processing requests, we estimate the workflows’ anomaly …