In-network Round-Trip Time estimation for TCP flows

Citation
2nd Workshop on Machine Learning & Netwoking (MaLeNe 2023)
Contribution to Conference
2nd Workshop on Machine Learning & Netwoking, MaLeNe 2023  
Publisher Link
https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/109653
Transmission Control Protocol (TCP)’s tendency to fill up buffers in the network results in long standing queues. Active Queue Management (AQM) tries to solve this issue but typically requires manual tuning as the optimal parameters depend on the Round-Trip Time (RTT) of the flow, which is unknown to the AQM. In this paper, we use network simulation and supervised learning to train a neural network to infer the RTT of a TCP flow from its queuing behavior. We transfer our model into a real-time network emulator and show that it is able to estimate the RTT with an error of only a few milliseconds.
Subjects
MLE@TUHH
DDC Class
004: Computer Sciences