A class of Active Queue Management algorithm based on BP neural network

Author

Junxin, Wu ; Jianchang, Liu ; Zhe, Guo

Author_Institution

Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China

fYear

2009

fDate

17-19 June 2009

Firstpage

1580

Lastpage

1583

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.

Keywords

artificial intelligence; backpropagation; neurocontrollers; queueing theory; telecommunication congestion control; BLUE algorithm; BP neural network; RED algorithm; RLGD algorithm; active queue management algorithm; artificial intelligence theory; end-to-end congestion control mechanism; Artificial intelligence; Artificial neural networks; Engineering management; Internet; Neural networks; Packaging; Probability; Quality of service; Scheduling algorithm; Technology management; Active Queue Management; BP neural network; Congestion control; RED;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5192226

Filename

5192226