Hybrid task prediction based on wavelet decomposition and ARIMA model in cloud data center

With the development of Information and Communication Technology (ICT), the service provided by cloud data centers has become a new pattern of Internet services. The prediction of the number of arriving tasks plays a crucial role in resource allocation and optimization for cloud data center providers. This work proposes a hybrid method that combines wavelet decomposition and autoregressive integrated moving average (ARIMA) to predict it at the next time interval. In this approach, the task time series is smoothed by Savitzky-Golay filtering, and then the smoothed time series is decomposed into multiple components via wavelet decomposition. An ARIMA model is established for the statistical characteristics of the trend and components, respectively. Finally, their prediction results are reconstructed via wavelet reduction and the predicted number of arriving tasks is obtained. Experimental results demonstrate that the hybrid method achieves better prediction results compared with some typical prediction methods including ARIMA and neural networks.