Wavelength assignment in optical burst switching networks using neuro-dynamic programming
All-optical networks are the most promising architecture for building large-size, hugebandwidth transport networks that are required for carrying the exponentially increasing Internet traffic. Among the existing switching paradigms in the literature, the optical burst switching is intended to leverage the attractive properties of optical communications, and at the same time, take into account its limitations. One of the major problems in optical burst switching is high blocking probability that results from one-way reservation protocol used. In this thesis, this problem is solved in wavelength domain by using smart wavelength assignment algorithms. Two heuristic wavelength assignment algorithms prioritizing available wavelengths according to reservation tables at the network nodes are proposed. The major contribution of the thesis is the formulation of the wavelength assignment problem as a continuous-time, average cost dynamic programming problem and its solution based on neuro-dynamic programming. Experiments are done over various traffic loads, burst lengths, and number of wavelength converters with a pool structure. The simulation results show that the wavelength assignment algorithms proposed for optical burst switching networks in the thesis perform better than the wavelength assignment algorithms in the literature that are developed for circuit-switched optical networks.