Fuzzy Q-learning flow control for high-speed networks

For the congestion problems in high-speed networks, a flow controller based on fuzzy Q-learning is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for high-speed networks. The Q-learning algorithm, which is independent of mathematic model, improves its behavior policy through interaction with the environment. The fuzzy inference is introduced to facilitate generalization in the state space. By means of learning procedures, the proposed controller can learn to take the best action to regulate source flow with the features of high throughput and low packet loss ratio. Simulation results show that the proposed method can promote the performance of the networks and avoid the occurrence of congestion effectively.