ARIMA-Based and Multiapplication Workload Prediction With Wavelet Decomposition and Savitzky-Golay Filter in Clouds

Current cloud data centers (CDCs) provide highly scalable, flexible, and cost-effective services to meet the performance needs of emerging applications. It is critical for CDC providers to predict future incoming workloads such that they can perform accurate resource provisioning in CDCs. Prediction accuracy is important and its improvement has been pursued in much existing work. This work adopts two different real-life Google data traces, based on which such prediction is conducted. Specifically, this work first gives a novel prediction mechanism that integrates wavelet decomposition, Savitzky-Golay (SG) filter, and autoregressive integrated moving average (ARIMA) to realize workload prediction in each time interval. The time series of the workload is smoothed with an SG filter and further divided into several components with wavelet decomposition. Then, an integrated approach is developed to predict statistical trends and their detail components. Real-life trace-driven experiments are done and the results suggest that the proposed method provides higher accuracy of prediction than its existing peers.