Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LCN48667.2020.9314771 | 2020 IEEE 45th Conference on Local Computer Networks (LCN) |
Keywords | DocType | ISSN |
Active Queue Management algorithm,AQM mechanism,Fair Qdisc,LFQ,optimal buffer size,deep learning based algorithm,optimal queue size,competing fair AQM schedulers,online Learning,per-flow queuing policies,Deep reinforcement Learning,incompatible congestion control algorithms,network flow,fair queuing system,static maximum size | Conference | 0742-1303 |
ISBN | Citations | PageRank |
978-1-7281-7159-3 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maximilian Bachl | 1 | 11 | 3.09 |
| Joachim Fabini | 2 | 91 | 19.96 |
| Tanja Zseby | 3 | 199 | 36.35 |