Study and investigation of SARIMA-based traffic prediction models for the resource allocation in NFV networks with elastic optical interconnection
The paper investigates resource allocation problems in Network Function Virtualization (NFV) network architectures in which the datacenters are interconnected by an Elastic Optical Network and the offered traffic is predicted by a Seasonal Autoregressive Integrated Moving Average (SARIMA) model. We apply a procedure for deseasonalizing, eliminating the trend, estimating the parameters of the SARIMA model and forecasting real traffic values.