Conference Paper

Stable AQM Algorithm Based on AdaptiveWavelet Neural Network Controller

Dept. of Electr. & Electron. Eng., Yonsei Univ., Seoul
DOI: 10.1109/SICE.2006.314825 Conference: SICE-ICASE, 2006. International Joint Conference
Source: IEEE Xplore


In this paper, we present the adaptive wavelet neural network (WNN) controller, which is trained by adaptive learning rates (ALRs), as an active queue management(AQM) in end-to-end TCP network. In TCP network, AQM is important to regulate the queue length and short round trip time by passing or dropping the packets at the intermediate routers. RED and PI algorithms have been used for AQM formerly. But these algorithms show weakness in the detection and control of the congestion under dynamically changing network situations. In our method, the WNN controller using ALRs is designed to overcome the problems, which adaptively controls the dropping probability of the TCP network and is trained by gradient-descent algorithm. We apply Lyapunov theorem to verify the stability of our proposed WNN controller. The simulation results show that the performance of WNN controller using ALRs is superior to that of PI controller

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    ABSTRACT: Recently many active queue management (AQM) algorithms have been proposed to address performance degradations of end-to-end congestion control. However, these AQM algorithms show weaknesses to detect and control congestion under dynamically changing network situations. In this paper, we propose a predictive and robust AQM algorithm, called proportional-integral-derivative (PID)-controller, using PID feedback control the incipient as well as current congestion adaptively and proactively to dynamically changing network environments. A simulation study over a wide range of IP traffic conditions shows that PID-controller outperforms other AQM algorithms such as random early detection (RED) and proportional-integral (PI) controller in terms of the queue length dynamics, the packet loss rates, and the link utilization.
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