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Forecasting methods for cloud hosted resources, a comparison

The emergence of cloud management systems, and the adoption of elastic cloud services enable dynamic adjustment of cloud hosted resources and provisioning. In order to effectively provision for dynamic workloads presented on cloud platforms, an accurate forecast of the load on the cloud resources is required. In this paper, we investigate various forecasting methods presented in recent research, identify and adapt evaluation metrics used in literature and compare forecasting methods on prediction performance. We investigate the performance gain of ensemble models when combining three of the best performing models into one model. We find that our 30th order Auto-regression model and Feed-Forward Neural Network method perform the best when evaluated on Google's Cluster dataset and using the provision specific metrics identified. We also show an improvement in forecasting accuracy when evaluating two ensemble models.