Network design under demand uncertainty

Abstract

A methodology for network design under demand uncertainty is proposed in this dissertation. The uncertainty is caused by the dynamic nature of the IP-based traffic which is expected to betransported directly over the optical layer in the future. Thus, there is a need to incorporate the uncertainty into a design modelexplicitly. We assume that each demand can be represented as a random variable, and then develop an optimization model to minimizethe cost of routing and bandwidth provisioning. The optimization problem is formulated as a nonlinear Multicommodity Flow problemusing Chance-Constrained Programming to capture both the demand variability and levels of uncertainty guarantee. Numerical work ispresented based on a heuristic solution approach using a linear approximation to transform the nonlinear problem to a simpler linearprogramming problem. In addition, the impact of the uncertainty on a two-layer network is investigated. This will determine how theChance-Constrained Programming based scheme can be practically implemented. Finally, the implementation guidelines for developingan updating process are provided.