Distributed Monitoring Framework for Large-Scale Microservice Systems

Abstract

With the rapid development of cloud computing technologies, microservice-based architectures have become the mainstream solution for building large-scale distributed applications. However, the high degree of service decoupling and dynamic deployment characteristics introduce significant challenges for system monitoring and performance management. Traditional centralized monitoring mechanisms are often insufficient to accurately capture complex dependency relationships and runtime behaviors. This paper proposes a distributed monitoring framework for cloud-native microservice environments. The framework integrates service dependency analysis, multi-source data collection, and hierarchical aggregation mechanisms to achieve comprehensive system visibility. A lightweight data processing module is deployed at each service node to perform local analysis and anomaly pre-screening, while a centralized coordination platform is responsible for global status assessment and policy management. Extensive experiments conducted on a simulated production environment demonstrate that the proposed framework effectively improves fault detection accuracy and reduces monitoring overhead. The results indicate that the proposed approach provides reliable technical support for the stable operation of large-scale microservice systems.