Proceedings of the 5th International Conference on Advanced Design and Manufacturing Engineering

Applying the Linear Neural Network to TCP Congestion Control

Authors
Niu Lei
Corresponding Author
Niu Lei
Available Online October 2015.
DOI
https://doi.org/10.2991/icadme-15.2015.113How to use a DOI?
Keywords
Linear Neural Network, Congestion Control, Decision Boundary, TCP Reno.
Abstract

The previous TCP protocol cannot predict congestion. Only when the sender receives more than three acknowledgements or the retransmission timer is out can it realize that congestion has occurred. We train the linear neural network by using round-trip time and current TCP throughput as its inputs. As a result, we get the decision boundary, which could predict whether the current network is in congestion or not. Simulation results show that, when applied to TCP congestion control, it can effectively predict the occurrence of congestion, so congestion window could make adjustments as soon as possible to reduce the probability of congestion collapse.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 5th International Conference on Advanced Design and Manufacturing Engineering
Series
Advances in Engineering Research
Publication Date
October 2015
ISBN
978-94-6252-113-1
ISSN
2352-5401
DOI
https://doi.org/10.2991/icadme-15.2015.113How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Niu Lei
PY  - 2015/10
DA  - 2015/10
TI  - Applying the Linear Neural Network to TCP Congestion Control
BT  - Proceedings of the 5th International Conference on Advanced Design and Manufacturing Engineering
PB  - Atlantis Press
SP  - 558
EP  - 562
SN  - 2352-5401
UR  - https://doi.org/10.2991/icadme-15.2015.113
DO  - https://doi.org/10.2991/icadme-15.2015.113
ID  - Lei2015/10
ER  -