An Artificial Intelligence Approach to Price Design for Improving AQM Performance

Active queue management (AQM) mechanism is a powerful method, which aims to assist the TCP congestion control and to improve the trade-off between queuing delay and link utilization. Traditional price-based AQM algorithms suffer from sluggish response, poor robustness, and lack adequate adaptability against dynamic traffics. To improve AQM performance, this paper introduces artificial intelligence methods to design a sophisticated AQM algorithm. In particular, a fuzzy neuron price is developed for congestion detection. Hebbian learning rule and fuzzy logic theory are employed to configure the control parameters automatically for better adaptability and robustness. Simulation results demonstrate that our proposed scheme is stable, responsive and performs robustly against time-varying network dynamics. It is superior to other peer AQM algorithms in various performance indicators, such as stability and jitter of queue length as well as packet loss.