Model for series of reconfigurations in wavelength-routed optical networks

When a traffic demand is changed, a virtual topology could be reconfigured to serve that traffic and to retain high performance. We purpose a reconfiguration model that minimizes costly changes in a virtual topology and maximizes network performance for a series of reconfigurations in the long term. The model includes the reconfiguration process and the policy. The reconfiguration process finds a set of non-dominated solutions using the Multi-Objective Evolutionary Algorithm (MOEA) that optimizes two objectives by using the concept of Pareto Optimal. The policy picks a solution from the set of solutions above using the Markov Decision Process (MDP). A case study based on simulation experiments is conducted to illustrate the application of our model.