论文标题
LFQ：使用深厚的强化学习对每流排队政策的在线学习
LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement Learning
论文作者
论文摘要
不同，不兼容的拥塞控制算法的数量增加导致公平排队的部署增加。公平排队会隔离每个网络流，因此即使流量的拥塞控制不公平，也可以保证每个流量的公平性。到目前为止，公平排队系统中的每个队列要么具有固定的静态最大尺寸，要么由类似于CODEL的活动队列管理（AQM）算法管理。在本文中，我们设计了一种AQM机制（学习eff QDISC（LFQ）），该机制根据指定的在线奖励功能动态学习每个流量的最佳缓冲尺寸。我们表明，基于深度学习的算法可以根据其拥塞控制，延迟和带宽动态地分配最佳队列大小。与竞争性的公平AQM调度程序相比，它提供了相同或更高的吞吐量的同时提供的队列明显较小。
The increasing number of different, incompatible congestion control algorithms has led to an increased deployment of fair queuing. Fair queuing isolates each network flow and can thus guarantee fairness for each flow even if the flows' congestion controls are not inherently fair. So far, each queue in the fair queuing system either has a fixed, static maximum size or is managed by an Active Queue Management (AQM) algorithm like CoDel. In this paper we design an AQM mechanism (Learning Fair Qdisc (LFQ)) that dynamically learns the optimal buffer size for each flow according to a specified reward function online. We show that our Deep Learning based algorithm can dynamically assign the optimal queue size to each flow depending on its congestion control, delay and bandwidth. Comparing to competing fair AQM schedulers, it provides significantly smaller queues while achieving the same or higher throughput.
