MicroTR: Transaction Reproduction Fault Diagnosis Framework for Microservice on Multi-Source Data

Root cause analysis (RCA) is crucial for the stability and reliability of large-scale microservice architectures. Existing multi-source RCA methods primarily rely on logs, traces, and metrics data to detect anomalies and identify abnormal services and root causes. And most multi-source methods focus only on service-level operations and inter-service dependencies, neglecting transactions and their dynamic changes. Furthermore, these approaches typically address only a subset of the RCA tasks, such as anomaly detection, root cause service localization, or root cause type determination. To address these limitations, we propose MicroTR, a transaction reproduction fault diagnosis framework for multi-source RCA in microservice environments. MicroTR deeply analyzes transaction execution logic and service states, utilizing multi-source data to reproduce the dynamic changes of transaction execution states in knowledge graph. This approach enables efficient and synchronized anomaly detection, root cause service localization, and root cause type determination. Experimental evaluations on two widely-adopted open-source microservice platforms demonstrate that MicroTR outperforms state-of-the-art multi-source methods, achieving an average F1 of 97.3% for anomaly detection, an average Hit@l of 90.8% for root cause service localization, and an average Hit@1 of 89.2% for root cause type determination. These results highlight the effectiveness of reproducing transaction execution states for RCA.