  Active Queue Management (AQM) aims to prevent bufferbloat and serial drops in
router and switch FIFO packet buffers that usually employ drop-tail queueing.
AQM describes methods to send proactive feedback to TCP flow sources to
regulate their rate using selective packet drops or markings. Traditionally,
AQM policies relied on heuristics to approximately provide Quality of Service
(QoS) such as a target delay for a given flow. These heuristics are usually
based on simple network and TCP control models together with the monitored
buffer filling. A primary drawback of these heuristics is that their way of
accounting flow characteristics into the feedback mechanism and the
corresponding effect on the state of congestion are not well understood. In
this work, we show that taking a probabilistic model for the flow rates and the
dequeueing pattern, a Semi-Markov Decision Process (SMDP) can be formulated to
obtain an optimal packet-dropping policy. This policy-based AQM, named PAQMAN,
takes into account a steady-state model of TCP and a target delay for the
flows. Additionally, we present an inference algorithm that builds on TCP
congestion control in order to calibrate the model parameters governing
underlying network conditions. Using simulation, we show that the prescribed
AQM yields comparable throughput to state-of-the-art AQM algorithms while
reducing delays significantly.
