# LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2007.02735 · HTML: https://mlsanity.com/paper/2007.02735/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents](https://mlsanity.com/paper/2205.12009/) (2022)

## Abstract (arXiv)

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.