ImpactTracer: Root Cause Localization in Microservices Based on Fault Propagation Modeling
Ru Xie, Jing Yang, Jingying Li, Liming Wang · 2023
Microservice architecture is embraced by a growing number of enterprises due to the benefits of modularity and flexibility. However, being composed of numerous interdependent microservices, it is prone to cascading failures and afflicted by the arising problem of troubleshooting, which entails arduous efforts to identify the root cause node and ensure service availability. Previous works use call graph to characterize causality relation-ships of microservices but not completely or comprehensively, leading to an insufficient search of potential root cause nodes and consequently poor accuracy in culprit localization. In this paper, we propose ImpactTracer to address the above problems. ImpactTracer builds impact graph to provide a com-plete view of fault propagation in microservices and uses a novel backward tracing algorithm that exhaustively traverses the impact graph to identify the root cause node accurately. Extensive experiments on a real-world dataset demonstrate that ImpactTracer is effective in identifying the root cause node and outperforms the state-of-the-art methods by at least 72%, significantly facilitating troubleshooting in microservices.