Discovery of port-to-port connectivity across network layers using machine learning

A non-transitory computer-readable medium configured to store computer logic having instructions that, when executed, cause one or more processing devices to

• obtain input data related to a plurality of Network Elements (NEs) operating in a plurality of layers within a network having a cross-layer topology of one or more cross-layer port-to-port connectivity links therebetween, and
• utilize a Machine Learning (ML) model and the input data to discover the cross-layer topology including the one or more cross-layer port-to-port connectivity links between pairs of NEs operating in different layers of the plurality of layers within the network,
• wherein the ML model is trained utilizing one or more ML processes with multi-variate time-series data including a known cross-layer topology with known cross-layer port-to-port connectivity links therein.

A method comprising the steps of

• obtaining input data related to a plurality of Network Elements (NEs) operating in a plurality of layers within a network having a cross-layer topology of or more cross-layer port-to-port connectivity links therebetween, and
• utilizing a Machine Learning (ML) model and the input data to discover the cross-layer topology including the one or more cross-layer port-to-port connectivity links between pairs of NEs operating in different layers of the plurality of layers within the network,
• wherein the ML model is trained utilizing one or more ML processes with multi-variate time-series data including a known cross-layer topology with known cross-layer port-to-port connectivity links therein.