  This paper proposes a novel framework to predict traffic flows' bandwidth
ahead of time. Modern network management systems share a common issue: the
network situation evolves between the moment the decision is made and the
moment when actions (countermeasures) are applied. This framework converts
packets from real-life traffic into flows containing relevant features. Machine
learning models, including Decision Tree, Random Forest, XGBoost, and Deep
Neural Network, are trained on these data to predict the bandwidth at the next
time instance for every flow. Predictions can be fed to the management system
instead of current flows bandwidth in order to take decisions on a more
accurate network state. Experiments were performed on 981,774 flows and 15
different time windows (from 0.03s to 4s). They show that the Random Forest is
the best performing and most reliable model, with a predictive performance
consistently better than relying on the current bandwidth (+19.73% in mean
absolute error and +18.00% in root mean square error). Experimental results
indicate that this framework can help network management systems to take more
informed decisions using a predicted network state.
