Short-Term Performance Metrics Forecasting for Virtual Machine to Support Anomaly Detection Using Hybrid ARIMA-WNN Model

摘要

Anomaly detection is a significant functionality in most cloud monitoring applications. Time-series forecasting model could be easily used for predicting the values of the performance metrics which could be used for representing the performance status of the cloud environment. The proposed hybrid model combines both Autoregressive Integrated Moving Average (ARIMA) and Wavelet Neural Network (WNN) models. Firstly, ARIMA model is employed to firstly predict the linear component and then WNN model is used for the nonlinear residual component prediction. Finally, the results of the two parts are combined into the final prediction value of the performance metric. Finally the experimental results show that the hybrid model could produce more accurate short-term prediction than other models.