Mobile edge computing provides users with low response time and avoids unnecessary data transmission. Due to the deployment of 5G, the emerging edge systems can provide gigabit bandwidth. However, network protocols have not evolved together. In TCP, the initial congestion window (IW) is such a low value that most short flows still stay in slow start phase when finishing, and do not fully utilize available bandwidth. Naively increasing IW may result in congestion, which causes long latency. Moreover, since the network environment is dynamic, we have a challenging problem—how to adaptively adjust IW such that flow completion time is optimized, while congestion is minimized. In this paper, we propose an adaptive online decision method to solve the problem, which learns the best policy using deep reinforcement learning stably and fast. In addition, we propose an approach to further improve the performance by supervised learning, using data collected during online learning. We also propose to adopt SDN to address the challenges in implementing our method in MEC systems. To evaluate our method, we build an MEC simulator based on ns3. Our simulations demonstrate that our method performs better than existing methods. It can effectively reduce FCT with little congestion caused.

中文翻译：

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基于深度强化学习的 5G 移动边缘计算初始拥塞窗口自适应在线决策方法

移动边缘计算为用户提供低响应时间并避免不必要的数据传输。由于 5G 的部署，新兴的边缘系统可以提供千兆带宽。然而，网络协议并没有一起发展。在 TCP 中，初始拥塞窗口 (IW) 非常低，以至于大多数短流在完成时仍停留在慢启动阶段，并没有充分利用可用带宽。天真地增加 IW 可能会导致拥塞，从而导致长延迟。此外，由于网络环境是动态的，我们面临一个具有挑战性的问题——如何自适应地调整 IW，以便优化流完成时间，同时最小化拥塞。在本文中，我们提出了一种自适应在线决策方法来解决该问题，该方法使用深度强化学习稳定快速地学习最佳策略。此外，我们提出了一种通过监督学习进一步提高性能的方法，使用在线学习期间收集的数据。我们还建议采用 SDN 来解决在 MEC 系统中实施我们的方法的挑战。为了评估我们的方法，我们构建了一个基于 ns3 的 MEC 模拟器。我们的模拟表明我们的方法比现有方法表现更好。它可以有效地减少FCT，并且几乎不会造成拥塞。