An active queue management scheme based on neuron learning

Congestion control problem of the intermediate nodes in the Internet has received extensively attention in networking and control community. In this paper, an improved adaptive active queue management scheme based on neuron gradient learning is presented. Both of queue length and link rate are used as congestion notification to determine an appropriate drop/mark probability, and the parameters of neuron-based AQM controller are tuned adaptively according to the time-varying network environment so that the stability of queue dynamics and robustness for fluctuation of TCP loads are guaranteed. This scheme is easy to be implemented with simple structure. Simulation results via NS-2 simulator show the effectiveness of the proposed scheme.

[1]  Bo Li,et al.  LRED: A Robust and Responsive AQM Algorithm Using Packet Loss Ratio Measurement , 2007 .

[2]  Ahmed Mehaoua,et al.  A fuzzy logic-based AQM for real-time traffic over internet , 2007, Comput. Networks.

[3]  Steven H. Low,et al.  REM: active queue management , 2001, IEEE Netw..

[4]  Vishal Misra,et al.  Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED , 2000, SIGCOMM.

[5]  QUTdN QeO,et al.  Random early detection gateways for congestion avoidance , 1993, TNET.

[6]  Donald F. Towsley,et al.  On designing improved controllers for AQM routers supporting TCP flows , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[7]  Sally Floyd,et al.  Promoting the use of end-to-end congestion control in the Internet , 1999, TNET.