SGW-SCN: An integrated machine learning approach for workload forecasting in geo-distributed cloud data centers

Abstract

Nowadays, a large number of cloud services have been published and hosted by geo-distributed cloud data centers (Geo-2DCs). In spite of numerous benefits, those Geo-2DCs face significant challenges such as dynamic resource scaling where workload forecasting plays a crucial role in addressing such a challenge. High accuracy and fast learning are key indicators for workload forecasting and the literature has witnessed a lot of efforts. This work proposes an integrated forecasting method, equipped with noise filtering and data frequency representation, named Savitzky-Golay and Wavelet-supported Stochastic Configuration Networks (SGW-SCN), to predict the amount of workload in future time slots. In this approach, the workload time series is first smoothed by a Savitzky-Golay filter and then decomposed into multiple components via wavelet decomposition. With stochastic configuration networks, an integrated model is established to characterize statistical characteristics of both trend and detail components. Extensive results have demonstrated that the proposed method achieves higher forecasting accuracy and faster learning speed than typical forecasting methods.