Real-time detection algorithm of abnormal data for cloud platform based on microservice architecture
Rundong Gan, Wei Wei, Jie Tang, Qidi Hu, Xingchuan Wang · 2024
Aiming at the traditional framework model of cloud platform with microservice architecture which usually only deals with single-source data and suffers from high latency and poor privacy, this paper proposes a real-time detection algorithm of anomalous data for cloud platform based on microservice architecture. On the cloud platform of microservice architecture, through real-time detection and analysis of point anomaly data, context anomaly data and collective anomaly data, potential problems can be discovered and solved in time, avoiding system failure or crash, thus guaranteeing the stable operation of the system. Microservice architecture splits the system into multiple independent microservices, each of which can be independently deployed, upgraded and extended, reducing the complexity of the system. At the same time, the real-time detection algorithm of abnormal data based on microservice architecture can be flexibly integrated into the existing monitoring system to realize unified monitoring management. Experimenting on a company's cloud platform monitoring dataset, we used manually labeled sample data and verified the accuracy and effectiveness of the method on this dataset. Eventually, we successfully achieved an anomaly detection accuracy of 97.57%.