ReconRCA: Root Cause Analysis in Microservices with Incomplete Metrics
Zekun Zhang, Jian Wang, Bing Li, Liuxiaoxiao Zhang · 2025
With the widespread adoption of microservice architectures, system complexity has increased significantly, making fault root cause localization a critical issue in system operations. Under resource constraints or load pressure, the monitoring metrics that reflect system status often exhibit incompleteness, negatively impacting operational accuracy and system stability. To address this challenge, we propose ReconRCA, which consists of an offline reconstruction stage and an online localization stage. During reconstruction, ReconRCA leverages historical data, along with spatio-temporal models and attention mechanisms, to reconstruct missing metrics. In the online stage, it captures both intra and inter-metric correlations of microservices to achieve precise root cause localization. Experiments show that ReconRCA outperforms existing methods with both incomplete and complete data, achieving average top-1 hit rates at 33.28% and 39.74%, respectively.