Wanna Make Your TCP Scheme Great for Cellular Networks? Let Machines Do It for You!

TL;DR

DeepCC is a deep reinforcement learning plug-in designed to enhance existing TCP protocols in cellular networks, significantly improving their performance without replacing them.

Contribution

DeepCC introduces a novel DRL-based plug-in that boosts various TCP schemes' performance in cellular networks, outperforming many state-of-the-art protocols.

Findings

DeepCC significantly improves TCP performance in cellular networks.

DeepCC enables existing TCP schemes to outperform state-of-the-art protocols.

Extensive evaluations validate DeepCC's effectiveness in real-world scenarios.

Abstract

Can we instead of designing just another new TCP, design a TCP \textit{plug-in} which can boost the performance of the existing/future TCP designs in cellular networks? To answer this question, we introduce DeepCC plug-in. DeepCC leverages deep reinforcement learning (DRL), a modern decision-making tool, to steer TCP toward achieving applications' desired delay and high throughput in a highly dynamic network such as the cellular network. The fact that DeepCC does not try to reinvent/replace TCP but aims to boost the performance of it differentiates it from the most (if not all) of the existing reinforcement learning (RL) systems where RL systems are considered clean-slate alternative designs replacing the traditional ones. We used DeepCC plug-in to boost the performance of various old and new TCP schemes including TCP Cubic, Google's BBR, TCP Westwood, and TCP Illinois in cellular networks. Through both extensive trace-based evaluations and in-field tests, we show that not only DeepCC can significantly improve the performance of TCP, but also after accompanied by DeepCC, these schemes can outperform state-of-the-art TCP protocols including Aurora, Sprout, Verus, C2TCP, Copa, Indigo, Remy, PCC-Vivace, and LEDBAT in cellular networks.