Scalable Capacity Estimation for Nonlinear Elastic All--Optical Core Networks

Routing and wavelength assignment (RWA) algorithms must strike a balance between finding routes with high quality of transmission (QoT) and finding routes that will not interfere with allocating future traffic. Too much emphasis on the first will concentrate traffic along major routes causing congestion whilst too much emphasis on the second will cause individual transceivers to operate below their capabilities increasing both cost and power consumption. This paper presents a low--complexity algorithm that shows that focusing on wavelength packing allows for greater overall traffic whilst giving only slight penalties for latency and required transceivers. Our algorithm comfortably outperforms kSP--FF routing for the same complexity and typically betters congestion aware routing whilst reducing complexity. We show these results on 4 simplified networks based on deployed topologies before replicating them on 2,000 artificially generated topologies based on real node locations in Germany and the USA. Capacity for each topology was found with an integer linear program to which our algorithm compares favorably suggesting it provides a scalable alternative to global optimization