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Lee SJ, Hou CL. A neural-fuzzy system for congestion control in ATM networks. IEEE Trans Syst Man Cybern B Cybern. 2000;30(1):2-9doi: 10.1109/3477.826942.
Lee, S. J., & Hou, C. L. (2000). A neural-fuzzy system for congestion control in ATM networks. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, 30(1), 2-9. https://doi.org/10.1109/3477.826942
Lee, S J, and Hou, C L. "A neural-fuzzy system for congestion control in ATM networks." IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society vol. 30,1 (2000): 2-9. doi: https://doi.org/10.1109/3477.826942
Lee SJ, Hou CL. A neural-fuzzy system for congestion control in ATM networks. IEEE Trans Syst Man Cybern B Cybern. 2000;30(1):2-9. doi: 10.1109/3477.826942. PMID: 18244724.
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IEEE Trans Syst Man Cybern B Cybern. 2000;30(1):2-9. doi: 10.1109/3477.826942.

A neural-fuzzy system for congestion control in ATM networks.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society

S J Lee, C L Hou

Affiliations

  1. Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung.

PMID: 18244724 DOI: 10.1109/3477.826942

Abstract

We propose the use of a neural-fuzzy scheme for rate-based feedback congestion control in asynchronous transfer mode (ATM) networks. Available bit rate (ABR) traffic is not guaranteed quality of service (QoS) in the setup connection, and it can dynamically share the available bandwidth. Therefore, congestion can be controlled by regulating the source rate, to a certain degree, according to the current traffic flow. Traditional methods perform congestion control by monitoring the queue length. The source rate is decreased by a fixed rate when the queue length is greater than a prespecified threshold. However, it is difficult to get a suitable rate according to the degree of traffic congestion. We employ a neural-fuzzy mechanism to control the source rate. Through learning, membership values can be generated and cell loss can be predicted from the status of the queue length. Then, an explicit rate is calculated and the source rate is controlled appropriately. Simulation results have shown that our method is effective compared with traditional methods.

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