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 eeciency in ATM networks. Transmission ef-ciency 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 (EGA) to predict the bandwidth-demand patterns in VPs. The eeciency of this approach, quantiied in terms of the Degree of Learning (DoL), is evaluated through simulation and the results are presented.