Congestion control is a fundamental mechanism for TCP protocol, which has been extensively studied in the past three decades. However, our experimental evaluations show that the state-of-art congestion control algorithms such as Cubic and BBR are far from optimal: they have unresolved issues such as insufficient usage of available bandwidth, inadaptable to dynamic bandwidth variants, and compromising on one or more performance dimensions. To address these challenges, we propose a novel congestion control mechanism called NeuRoc that coordinately uses online changepoint detection and deep reinforcement learning (DRL) technique to generate the optimal congestion control policy, which allows TCP operating at Kleinrock ’s optimal operation point to achieve fully bandwidth usage and low latency. To address the practical issues of deploying the deep learning based congestion control mechanism, we propose a cold-started training and deployment framework to reduce the cost of bootstrap. We implement NeuRoc on an emulation platform which connects to the Linux network protocol stack through virtual network interfaces. Extensive experiments show that NeuRoc achieves the best throughput-latency tradeoff compared with the state-of-the-arts in a variety of scenarios.

中文翻译：

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TCP-NeuRoc：具有在线变化点检测的神经自适应 TCP 拥塞控制


拥塞控制是 TCP 协议的基本机制，在过去三十年中得到了广泛的研究。然而，我们的实验评估表明，最先进的拥塞控制算法（例如 Cubic 和 BBR）远非最佳：它们还存在未解决的问题，例如可用带宽使用不足、不适应动态带宽变化以及对一个或多个方面的妥协性能维度。为了应对这些挑战，我们提出了一种称为 NeuRoc 的新型拥塞控制机制，它协调使用在线变化点检测和深度强化学习（DRL）技术来生成最佳拥塞控制策略，这使得 TCP 运行在 Kleinrock 的最佳操作点上完全实现带宽使用和低延迟。为了解决部署基于深度学习的拥塞控制机制的实际问题，我们提出了一种冷启动训练和部署框架，以降低引导成本。我们在仿真平台上实现 NeuRoc，该平台通过虚拟网络接口连接到 Linux 网络协议栈。大量实验表明，与最先进的技术相比，NeuRoc 在各种场景下实现了最佳的吞吐量-延迟权衡。