  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.
