Dynamic routing and wavelength assignment using learning automata technique

Abstract

Dynamic routing and wavelength assignment RWA is one of the most important issues in wavelength routed all optical networks. We introduce the learning automata technique for dynamic RWA in WDM networks without conversion under different load conditions. Learning automata are used to choose the shortest route from source to destination if more than one shortest route exist. Furthermore, learning automata are used to select which wavelength is to be used on the chosen route. We compare our wavelength assignment technique with some exhaustive wavelength assignment algorithms that scan all wavelengths on a predetermined shortest route. The use of the learning automata wavelength assignment technique reduces the call setup time by pursuing a small number of wavelengths. In addition, the technique is used to achieve fairness among different source/destination pairs. Simulation results are presented which indicate the benefits of using the learning automata technique for dynamic routing and wavelength assignment in WDM networks.