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With the emergence of new technologies, computer networks are becoming morestructurally complex, diverse and heterogenous. The increasing discrepancy(among the interconnected networks) in data rates, delays, packet loss, andtransmission scenarios, influence significantly the dynamics of congestioncontrol (CC) parametrization. In contrast to the traditional endto-end CCalgorithms that rely on strict rules, new approaches aim to involve machinelearning in order to continuously adapt the CC to real-time networkrequirements. However, due to the high computational complexity and memoryconsumption, the feasibility of these schemes may still be questioned. Thispaper surveys selected machine-learning based approaches to CC and proposes aroadmap to their implementation in computer systems, by using dataflowcomputing and Gallium Arsenide (GaAs) chips.

Machine Learning in Congestion Control: A Survey on Selected Algorithms and a New Roadmap to their Implementation