Lightpath Scheduling and Routing for Traffic Adaptation

We study the benefits and trade-off of using scheduled lightpaths for traffic adaption. We propose a network planning model, which allows lightpaths to slide within its desired timing window with no penalty on the optimization objective, and to slide beyond its desired timing window with a decreasing tolerance level. Our model quantitatively measures the timing satisfaction or violation. We apply the Lagrangian Relaxation and Subgradient Method to the formulated optimization problem. Our method demonstrated great computational efficiency when compared with other existing algorithms. Our simulation results show how timing flexibility improves network resource utilization and reduces rejections.