On-demand incremental capacity planning in optical transport networks

Incremental planning is performed periodically to decide how a backbone optical network has to be updated to serve the forecast traffic during the next planning period. Based on reliable traffic prediction, new equipment is installed and its capacity is ready to be used. Nonetheless, due to the introduction of new services among other reasons, exact prediction is not usually available. This leads to the installation of more capacity than required, thus increasing network expenditures. In this paper, we propose to reduce expenses by incrementing the capacity of the network as soon as it is required to meet the target performance. Hence, performance metrics are monitored and the incremental capacity (INCA) planning problem is solved on-demand when some metrics drop under a threshold. The INCA problem is mathematically modeled and a heuristic algorithm is proposed to solve the problem in practical computation times. Since solving the INCA problem requires access to both operation and inventory databases, an architecture to support on-demand network planning as well as a model for the inventory is proposed. Exhaustive simulation results, together with its experimental assessment, validate the proposed on-demand INCA planning.