  Predicting the bandwidth utilization on network links can be extremely useful
for detecting congestion in order to correct them before they occur. In this
paper, we present a solution to predict the bandwidth utilization between
different network links with a very high accuracy. A simulated network is
created to collect data related to the performance of the network links on
every interface. These data are processed and expanded with feature engineering
in order to create a training set. We evaluate and compare three types of
machine learning algorithms, namely ARIMA (AutoRegressive Integrated Moving
Average), MLP (Multi Layer Perceptron) and LSTM (Long Short-Term Memory), in
order to predict the future bandwidth consumption. The LSTM outperforms ARIMA
and MLP with very accurate predictions, rarely exceeding a 3\% error (40\% for
ARIMA and 20\% for the MLP). We then show that the proposed solution can be
used in real time with a reaction managed by a Software-Defined Networking
(SDN) platform.
