Methods for Correlation Analysis of Alarm Information in Multi-Microservice Application Environments

This research develops a comprehensive analytical framework utilizing data analysis, machine learning, and clustering technologies to address the correlation analysis of alarm information in multi-microservice application environments. By aggregating and preprocessing alarm logs from various security devices and analyzing their underlying logic, this framework generates correlation rules that assist network security personnel in understanding current network threats. Furthermore, the study employs advanced algorithms for anomaly detection within the metrics data of microservice logs, facilitating the direct identification of anomalies in business or IT systems, significantly reducing the reliance on manual threshold settings, and enhancing the accuracy of alerts. The findings demonstrate that this method not only bolsters the stability and reliability of microservice environments but also offers an efficient and systematic approach to analyzing alarm data.