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A Genetic Based Fuzzy Q-Learning Flow Controller for High-Speed Networks
College of Information Science and Engineering, Northeastern University, Shenyang, China
College of Information Science and Engineering, Northeastern University, Shenyang, China
College of Information Science and Engineering, Northeastern University, Shenyang, China
College of Information Science and Engineering, Northeastern University, Shenyang, China
- 1 College of Information Science and Engineering, Northeastern University, Shenyang, China
- 2 College of Information Science and Engineering, Northeastern University, Shenyang, China
- 3 College of Information Science and Engineering, Northeastern University, Shenyang, China
- 4 College of Information Science and Engineering, Northeastern University, Shenyang, China
International Journal of Communications, Network and System Sciences·Volume 02 (2009)·Pages 84–89·Published 26 February 2009·DOI10.4236/ijcns.2009.21010
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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.
KeywordsHigh-Speed NetworkFlow ControlFuzzy Q-learningGenetic Operator
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