Abstract
For the congestion problems in high-speed networks, a genetic based fuzzy Q-learning flow controller 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 Q-learning, which is independent of mathematic model, and prior-knowledge, has good performance. The fuzzy inference is introduced in order to facilitate generalization in large state space, and the genetic operators are used to obtain the consequent parts of fuzzy rules. 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.
Highlights
The growing interest on congestion problems in highspeed networks arise from the control of sending rates of traffic sources
High-speed networks must have an applicable flow control scheme to guarantee the quality of service (QoS) for the existing links and to achieve high system utilization
In order to overcome the above-mentioned difficulties, the flow control scheme with learning capability has been employed in flow control of high-speed network [1,2]
Summary
The growing interest on congestion problems in highspeed networks arise from the control of sending rates of traffic sources. The priori-knowledge of system to train the parameters in the controller is hard to achieve for high-speed networks. In this case, the reinforcement learning (RL) shows its particular superiority, which just needs very simple information such as estimable and critical information, “right” or “wrong” [3]. RL is independent of mathematic model and priori-knowledge of system It obtains the knowledge through trial-and-error and interaction with environment to improve its behavior policy. In [8], a Metropolis criterion based Q-learning controller is proposed to solve the problem of flow control in high-speed networks. A genetic based fuzzy Q-learning flow controller (GFQC) for high-speed networks is proposed. Simulation results show that the proposed controller can avoid the occurrence of congestion effectively with the features of high throughput, low PLR, low end-to-end delay, and high utilization
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