Detecting Anomalies in Cluster System Using Hybrid Deep Learning Model

Anomaly detection is of great importance for data centers. It could help operations discover system failures and perform root cause analysis. In recent years, deep learning has achieved great results in many fields. Therefore, people begin to pay attention to applying deep learning for automatic anomaly detection. Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) are two classical structures in deep learning, which could effectively detect anomalies from system logs. However, the existing CNN-based and LSTM-based models have their shortcomings.