Abstract
The main difficulty arising in designing an e.cient congestion control scheme lies in the large propagation delay in data transfer which usually leads to a mismatch between the network resources and the amount of admitted traffic. To attack this problem, this paper describes a novel congestion control scheme that is based on a Back Propagation (BP) neural network technique. We consider a general computer communication model with multiple sources and one destination node. The dynamic bu.er occupancy of the bottleneck node is predicted and controlled by using a BP neural network. The controlled best-effort traffic of the sources uses the bandwidth, which is left over by the guaranteed traffic. This control mechanism is shown to be able to avoid network congestion efficiently and to optimize the transfer performance both by the theoretic analyzing procedures and by the simulation studies.
This research has been supported by National Natural Science Foundation of China under Grant No. 90104005 and by the Key Project of Natural Science Foundation of Hubei Province under Grant No. 2003ABA047
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He, Y., Xiong, N., Yang, Y. (2004). Data Transmission Rate Control in Computer Networks Using Neural Predictive Networks. In: Cao, J., Yang, L.T., Guo, M., Lau, F. (eds) Parallel and Distributed Processing and Applications. ISPA 2004. Lecture Notes in Computer Science, vol 3358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30566-8_101
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DOI: https://doi.org/10.1007/978-3-540-30566-8_101
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