Multi-objective routing and wavelength converter allocation under uncertain traffic

Uncertain traffic plays an important role in the design of long-term optical networks. The performance of these networks is highly dependent on the way the wavelength converter allocation and routing problems are solved. In order to achieve a good performance for long time periods, both problems should be dealt with together, considering at the same time, uncertain traffic. To this aim, this work proposes a joint optimization approach where uncertainty is modelled through scenarios and the allocations of converters and paths for routing are simultaneously calculated. A multi-objective evolutionary algorithm is proposed to simultaneously minimize the number of wavelength converters, as well as the expectation and unfairness of the blocking probability. The proposed algorithm calculates an approximation to the optimal set of trade-off solutions called Pareto set. A benefit-cost study on the approximate Pareto set is proposed in order to help in the decision concerning the number of converters to be installed. The work also calculates the relation between the number of converters and the hyper-volume metric of expected blocking probability and the maximum unfairness through all scenarios, recommending that, the best number of converters to be strategically allocated ranges from 20% to 60% of the total number of nodes in a typical optical network.