GADaM on the road - Smart Approach to Multi-Access Networks: Analytical and Practical Evaluation in Various Urban Mobile Environments

Multipath scheduling has been a hot research topic in recent years thanks to the development of multipath transport protocols (MP-TCP and MP-QUIC) and the wide deployment of 5G infrastructures. These schedulers take advantage of multiple physical interfaces on end-user devices, thus improving performance and reliability. While Peekaboo, the state-of-the-art scheduler, seems to provide the best performance in some specific environments, some evidence is that other existing schedulers may outperform it in more realistic network conditions. Based on this observation, we proposed GADaM, stands for Generic Adaptive Deep-learning based Multipath scheduler selector. GADaM’s role is to select the most appropriate scheduler under specific network conditions dynamically. The proposed paradigm proved its effectiveness in simulations, but its behavior in more realistic (typically mobile) environments remains largely unknown. This paper presents an experimental evaluation of GaDAM in real-world scenarios performed in an urban area near Paris, France. We design and implement a framework to serve this purpose, which takes mobility and fairness into consideration. Our results confirm the advantage of GaDAM’s approach compared to the deterministic scheduler selection approach.