Abstract
In this paper, queue length in the buffer is considered as a scale to evaluate the quality of service of communication network. To realize that the system is stable and avoid the data congestion under complex environments, the neural networks predictor and controller are designed, which can predict the bursty available bandwidth for ABR traffic effectively and force the queue level in the buffer to the desired region, respectively. The fairness of different connections is achieved through fair algorithm. The simulations verify the effectiveness of the proposed scheme.
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© 2006 Springer-Verlag Berlin Heidelberg
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Liu, Z., Guan, X., Wu, H. (2006). Bandwidth Prediction and Congestion Control for ABR Traffic Based on Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_30
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DOI: https://doi.org/10.1007/11760191_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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