Virtual Topology Reconfiguration in Wavelength-Routed Optical Networks


Reconfiguration in a Wavelength-Routed Optical Network is a process of rearranging a virtual topology to meet traffic demands that change over a period of time. This dissertation studies a series of reconfigurations corresponding to a series of changes in traffic demand matrices. A change in a virtual topology is costly in terms of traffic disruption. However, without response to this change, the virtual topology would lose its optimality and might not serve the new traffic demand. Therefore; the reconfiguration problem is a trade-off between a performance objective and a cost objective. This research describes the reconfiguration problem from two perspectives. First, the reconfiguration problem is a multi-objective optimization such that a single-objective optimization method could not be applied. Second, the reconfiguration problem consists of a series of reconfigurations with corresponding traffic demands, thus reconfigurations that consider only the current traffic demand cannot guarantee the optimal average outcome. Therefore, sequential decision-making is required to optimize the average outcome from a series of reconfigurations. Since the reconfiguration objectives are conflicting there exists a Pareto front or a set of non-dominated solutions in all objectives. A Multi-Objective Evolutionary Algorithm (MOEA) is required to search the Pareto front and a decision-making process will pick one solution in the Pareto front accordingly. The major contribution of this research is a complete reconfiguration model applicable to any kind of traffic. It is a stochastic model consisting of two tasks: a reconfiguration process and a policy. For each reconfiguration in a series, the reconfiguration process finds a Pareto front and the policy picks a solution from the Pareto front to perform a reconfiguration operation. Our research presents the problem formulation mathematically and the design of the model is based on realistic SONET/SDH traffic streams. We use a MOEA called Strength Pareto Evolutionary Algorithm (SPEA) in the reconfiguration process and use a Markov Decision Process in the policy. A case study based on simulation experiments is conducted to illustrate the application and efficiency of the model. It shows that our model generates a higher average outcome than that of reconfigurations considering only the current traffic demand.