Transport protocols use congestion control to avoid overloading a network. Nowadays, different congestion control variants exist that influence performance. Studying their use is thus relevant, but it is hard to identify which variant is used. While passive identification approaches exist, these require detailed domain knowledge and often also rely on outdated assumptions about how congestion control operates and what data is accessible. We present DeePCCI, a passive, deep learning-based congestion control identification approach which does not need any domain knowledge other than training traffic of a congestion control variant. By only using packet arrival data, it is also directly applicable to encrypted (transport header) traffic. DeePCCI is therefore more easily extendable and can also be used with QUIC.
@article{arxiv.1907.02323,
title = {DeePCCI: Deep Learning-based Passive Congestion Control Identification},
author = {Constantin Sander and Jan Rüth and Oliver Hohlfeld and Klaus Wehrle},
journal= {arXiv preprint arXiv:1907.02323},
year = {2019}
}