Can we instead of designing yet another new TCP algorithm, design a TCP plug-in that can enable machines to automatically boost the performance of the existing/future TCP designs in cellular networks? We answer this question by introducing DeepCC. DeepCC leverages advanced deep reinforcement learning (DRL) techniques to let machines automatically learn how to steer throughput-oriented TCP algorithms toward achieving applications’ desired delays in a highly dynamic network such as the cellular network. 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 real-world experiments, we show that not only DeepCC can significantly improve the performance of TCP schemes, but also after accompanied by DeepCC, these schemes can outperform state-of-the-art TCP protocols including new clean-slate machine learning-based designs and the ones designed solely for cellular networks.

中文翻译：

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想让您的 TCP 方案适用于蜂窝网络吗？让机器为你做！

我们是否可以不设计另一种新的 TCP 算法，而是设计一个 TCP 插件，使机器能够自动提高蜂窝网络中现有/未来 TCP 设计的性能？我们通过介绍 DeepCC 来回答这个问题。DeepCC 利用先进的深度强化学习 (DRL) 技术让机器自动学习如何引导面向吞吐量的 TCP 算法在高度动态的网络（如蜂窝网络）中实现应用程序所需的延迟。我们使用 DeepCC 插件来提升蜂窝网络中各种新旧 TCP 方案的性能，包括 TCP Cubic、Google 的 BBR、TCP Westwood 和 TCP Illinois。通过广泛的基于跟踪的评估和现实世界的实验，我们表明 DeepCC 不仅可以显着提高 TCP 方案的性能，