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It is estimated that 5G and the Internet of Things (IoT) will impact traffic, both in volume and dynamicity, at unprecedented rates. Thus, to cost-efficiently accommodate these challenging requirements, optical networks must become more responsive to changes impacting the traffic and network state as well as operate more closely to optimality. In this context, knowledge-defined networking (KDN) promises to play a paramount role in improving network flexibility and automation. KDN is a solution that introduces reasoning processes and machine learning techniques into the control plane of the network, enabling it to operate autonomously and faster. One of the key aspects in this environment is the accurate validation of lightpaths. Accurate lightpath validation demands running computationally intensive performance models, which can be time-consuming and impact time-critical applications (e.g., optical channel …