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We describe two applications of machine learning in the context of IP/Opticalnetworks. The first one allows agile management of resources at a coreIP/Optical network by using machine learning for short-term and long-termprediction of traffic flows and joint global optimization of IP and opticallayers using colorless/directionless (CD) flexible ROADMs. Multilayercoordination allows for significant cost savings, flexible new services to meetdynamic capacity needs, and improved robustness by being able to proactivelyadapt to new traffic patterns and network conditions. The second application isimportant as we migrate our metro networks to Open ROADM networks, to allowphysical routing without the need for detailed knowledge of optical parameters.We discuss a proof-of-concept study, where detailed performance data forwavelengths on a current flexible ROADM network is used for machine learning topredict the optical performance of each wavelength. Both applications can beefficiently implemented by using a SDN (Software Defined Network) controller.

Two Use Cases of Machine Learning for SDN-Enabled IP/Optical Networks: Traffic Matrix Prediction and Optical Path Performance Prediction