Bandwidth-demand prediction in virtual path in ATM networks using genetic algorithms

The concept of Virtual Paths (VP) is a powerful technique to improve the transmission efficiency in ATM networks. Transmission efficiency can be improved by dynamically changing the bandwidths of the VPs, based on the demand. Intelligent controllers, which predict bandwidth-demand patterns to enable better VP management, have the potential to revolutionize ATM network performance. We present a scheme based on the Evolutionary Genetic Approach to predict the bandwidth-demand patterns in VPs. The efficiency of this approach, quantified in terms of the Degree of Learning (DoL), is evaluated through simulation and the results are presented.

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