Microservice Dependency Discovery Based on Spatio-Temporal Network Flow Behavior Modeling

The microservice architectural style offers significant application scalability and development advantages. Composing monolithic systems into loosely coupled, containerized services reduces deployment and development costs while enhancing the flexibility and resilience of the overall system. However, in large-scale internet applications involving multi-party collaboration and global deployment, the formation of complex microservice dependencies increases the risk of cascading failures. It complicates the process of identifying the source of a fault. Identifying such dependencies in uncontrolled conditions with limited observational data represents a significant challenge. This paper proposes Microservice Dependency Discovery based on Spatio-Temporal Network Flow Behaviour Modelling (Cross-MSDD). This method infers microservice dependencies by modeling spatiotemporal interactions of network traffic. The method employs network flow characteristics to mine frequent behavioral patterns, thereby inferring dependency chains without the necessity for additional tracing tools. The method utilizes network flow characteristics to mine frequent behavior patterns, inferring dependency chains without additional tracing tools, minimizing system interruptions, and protecting request content privacy. To verify the effectiveness of the proposed method, a semi-simulated experimental environment was set up using traffic data from typical microservice applications. The results demonstrate that the method attains an accuracy rate exceeding 96.3% in cross-domain dependency identification, markedly surpassing the performance of existing techniques. This facilitates the practical detection of faults and the mitigation of cascading failure risks, thereby ensuring system stability.