A class of active queue management algorithm based on BP neural network

Authors:
Wu Junxin;Liu Jianchang;Guo Zhe
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang;School of Information Science and Engineering, Northeastern University, Shenyang;School of Information Science and Engineering, Northeastern University, Shenyang
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Year:
2009

Citing 2
Cited 0

Random early detection gateways for congestion avoidance

IEEE/ACM Transactions on Networking (TON)
Tuning RED for Web traffic

Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication

Quantified Score

Hi-index	0.00

Visualization

Abstract

As an end-to-end congestion control mechanism, Active Queue Management (AQM) technology maintains smaller queue length and higher link utilization through discarding packets in intermediate network nodes. This paper discussed some previous AQM algorithms, RED, BLUE and RLGD, and found out shortcomings in which by comparing with them. On the Basis of Artificial Intelligence (AI) theory and technology, a new AQM algorithm based on BP neural network is proposed. In the end, the implement of the new Active Queue Management algorithm is presented, and the convergence is proved.