KPI Data Anomaly Detection Strategy for Intelligent Operation and Maintenance Under Cloud Environment

摘要

In the complex and changeable cloud environment, monitoring and anomaly detection of the cloud platform is very important. In the cloud environment, because of the complex structure of the system, the characteristics of the monitoring data are constantly changing. In order to adapt to the change of the data characteristics, the operators need to adjust the anomaly detection model to solve the problem of dynamic KPI anomaly detection, this paper transforms the adjustment process of anomaly detection model into a general Markov decision process by means of reinforcement learning technology, which cloud reduce the human cost caused by anomaly detection model adjustment, and improve the effective detection rate of the anomaly detection model. Comparing the three typical KPI curves with other optimization strategies, and finally verify the effectiveness of the strategy used in this paper.