A Robust AQM Algorithm Based on Fuzzy-Inference

Active queue management (AQM) is an effective mechanism for congestion control problem which can achieve high quality of service (QoS) by making a better tradeoff between high throughput and low delay. While nowadays most AQM algorithms based on control theory have less robustness and queue stability under the complex network environment with uncertainties since those algorithms are more sensitive to the variations of network parameters. In this paper, a novel fuzzy AQM controller based on the relative changes of queue length and link rate is presented, which introduces two relative errors as congestion notifications and also as the inputs of fuzzy inference system, and then an appropriate dropping probability at router is determined by a set of fuzzy rules. Simulation results show that this proposed algorithm has better performance on queue stability and less delay, at the same time it has good robustness for nonlinearity and load variation.