  As more end devices are getting connected, the Internet will become more
congested. Various congestion control techniques have been developed either on
transport or network layers. Active Queue Management (AQM) is a paradigm that
aims to mitigate the congestion on the network layer through active buffer
control to avoid overflow. However, finding the right parameters for an AQM
scheme is challenging, due to the complexity and dynamics of the networks. On
the other hand, the Explicit Congestion Notification (ECN) mechanism is a
solution that makes visible incipient congestion on the network layer to the
transport layer. In this work, we propose to exploit the ECN information to
improve AQM algorithms by applying Machine Learning techniques. Our intelligent
method uses an artificial neural network to predict congestion and an AQM
parameter tuner based on reinforcement learning. The evaluation results show
that our solution can enhance the performance of deployed AQM, using the
existing TCP congestion control mechanisms.
