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

@article{Bachl2020LFQOL,
  title={LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement Learning},
  author={Maximilian Bachl and J. Fabini and T. Zseby},
  journal={2020 IEEE 45th Conference on Local Computer Networks (LCN)},
  year={2020},
  pages={417-420}
}
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… Expand
1 Citations
Optimizing Congestion Control Through Fair Queuing Detection

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