Learning SDN traffic flow accurate models to enable queue bandwidth dynamic optimization

Software Defined Network (SDN) architectures decouple control and forwarding functionalities by enabling the network devices to be remotely configurable/programmable run-time by a controller. As a direct consequence identifying an accurate model of a network and forwarding devices is crucial in order to apply advanced control techniques to optimize the network performance. An enabling factor in this direction is given by recent results that appropriately combine System Identification and Machine Learning techniques to obtain predictive models using historical data retrieved from a network. In this paper we propose a novel methodology to learn, starting from historical data and appropriately combining ARX identification with Regression Trees and Random Forests, an accurate model of the dynamical input-output behavior of a network device that can be directly and efficiently used to optimally and dynamically control the bandwidth of the queues of switch ports, within the SDN paradigm. We compare our predictive model with Neural Network predictors and demonstrate the benefits in terms of Packet Losses reduction and Bandwidth savings in the Mininet network emulator environment.