  With the emergence of new technologies, computer networks are becoming more
structurally complex, diverse and heterogenous. The increasing discrepancy
(among the interconnected networks) in data rates, delays, packet loss, and
transmission scenarios, influence significantly the dynamics of congestion
control (CC) parametrization. In contrast to the traditional endto-end CC
algorithms that rely on strict rules, new approaches aim to involve machine
learning in order to continuously adapt the CC to real-time network
requirements. However, due to the high computational complexity and memory
consumption, the feasibility of these schemes may still be questioned. This
paper surveys selected machine-learning based approaches to CC and proposes a
roadmap to their implementation in computer systems, by using dataflow
computing and Gallium Arsenide (GaAs) chips.
