标题： 使用显式拥塞通知的智能主动队列管理 显示英文标题

标题： Intelligent Active Queue Management Using Explicit Congestion Notification

摘要： 随着越来越多的终端设备接入网络，互联网将变得更加拥塞。各种拥塞控制技术已在传输层或网络层上得到开发。主动队列管理（AQM）是一种旨在通过主动缓冲区控制来缓解网络层拥塞以避免溢出的范式。然而，由于网络的复杂性和动态性，找到合适的AQM方案参数具有挑战性。另一方面，显式拥塞通知（ECN）机制是一种解决方案，它使网络层上的潜在拥塞对传输层可见。在这项工作中，我们提议利用ECN信息通过应用机器学习技术来改进AQM算法。我们的智能方法使用人工神经网络预测拥塞，并基于强化学习的AQM参数调节器。评估结果显示，我们的解决方案可以利用现有的TCP拥塞控制机制增强部署的AQM性能。 显示英文摘要

摘要： As more end devices are getting connected, the Internet will become more congested. Various congestion control techniques have been developed either on transport or network layers. Active Queue Management (AQM) is a paradigm that aims to mitigate the congestion on the network layer through active buffer control to avoid overflow. However, finding the right parameters for an AQM scheme is challenging, due to the complexity and dynamics of the networks. On the other hand, the Explicit Congestion Notification (ECN) mechanism is a solution that makes visible incipient congestion on the network layer to the transport layer. In this work, we propose to exploit the ECN information to improve AQM algorithms by applying Machine Learning techniques. Our intelligent method uses an artificial neural network to predict congestion and an AQM parameter tuner based on reinforcement learning. The evaluation results show that our solution can enhance the performance of deployed AQM, using the existing TCP congestion control mechanisms.