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

TL;DR

This paper surveys machine learning approaches for congestion control in complex networks and proposes a new implementation roadmap using advanced hardware like dataflow computing and GaAs chips.

Contribution

It provides a comprehensive survey of ML-based congestion control algorithms and introduces a novel roadmap for their practical implementation with specialized hardware.

Findings

ML-based CC adapts better to network heterogeneity

Hardware acceleration can mitigate computational challenges

Roadmap facilitates deployment of intelligent CC systems

Abstract

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.