A Metropolis Criterion Based Fuzzy Markov Game Flow Controller for High-Speed Networks

For the congestion problems in high-speed networks, a Metropolis criterion based fuzzy Markov game flow controller (MFMC) is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for high-speed networks. In this case, the fuzzy Markov game, which is independent of mathematic model, and prior-knowledge, has good performance. It offers a promising platform for robust control in the presence of the bounded external disturbances. The Metropolis criterion can cope with the balance between exploration and exploitation in action selecting. Simulation results show that 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, and can avoid the occurrence of congestion effectively.