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 among others to the introduction of new services, exact prediction is not usually available, which leads to installing more capacity than that required thus, increasing network expenditures. To reduce expenses, in this paper we propose to increment 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 drops under a threshold. The INCA problem is mathematically modelled and a heuristic algorithm is proposed to solve the problem in practical times. In view of the INCA problem needs to access 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 incremental network capacity planning.

Peer Reviewed