ZAlert: A Real Time Prediction Framework For Network Alert.

With the rapid development of enterprise digital transformation and the widespread use of cloud-native architecture applications, the complexity of enterprise-level systems is getting higher and higher. This requires the engineers to be able to locate and repair alert incidents accurately and quickly to ensure the stability of complex network equipment. In this paper, we propose a novel alert prediction framework, ZAlert, which can predict the occurrence of future alerts and locate the equipment that may cause alerts based on real-time alarm data. First, ZAlert extracts text and statistical features from alarm data to build a high-performance comprehensive learning model to predict the category of alert that may occur in the future with an average performance of 0.81 on F1-score. Then, we use the knowledge graph based on the CMDB(Configuration Management Database) to search for alarm devices. In this way, engineers can quickly find devices that may cause alarms, and improve the the overall efficiency of engineers’ operation and maintenance.