Seamless Optical Path Restoration with Just-in-Time Resource Allocation Leveraging Machine Learning

We present an experimental proof-of-concept on just-in-time resource allocation in elastic optical networks to provide seamless path restoration. Our method relies on state of polarization monitoring via standard coherent receiver paired with machine learning for proactive fiber cut detection.