Automatic discovery of IP-optical links with multi-layer filtering and traffic mapping using neural networks

A method of identifying IP-optical links in a network having a plurality of nodes, the method comprising:

• grouping network nodes into discovery groups;
• for each discovery group:
• filtering ports of the nodes in the discovery group;
• producing class IDs for each filtered port using a machine learning model, wherein producing the class IDs for each filtered port includes:
• collecting a sequence of time-sampled data from all of the ports and forming an image using the collected data;
• assigning different class IDs to all of the Tx ports;
• training the machine learning model to learn the class ID for each of the Tx ports using the images formed for the Tx ports;
• and
• inputting the images for all of the Rx ports into the machine learning model to assign a class ID to each of the Rx ports;
• matching IP ports to optical ports from the filtered ports using the class IDs of each port to identify IP-optical links;
• and
• verifying the identified IP-optical links.

A software defined network (SDN) controller for identifying IP-optical links in a network having a plurality of nodes, the SDN controller comprising:

• a memory;
• and
• a processor connected to the memory, wherein the processor is configured to:
• group network nodes into discovery groups;
• for each discovery group:
• filter ports of the nodes in the discovery group;
• produce class IDs for each filtered port using a machine learning model, wherein the machine learning model matches each of a plurality of Tx ports to a respective Rx port tagged with a unique class ID;
• match IP ports to optical ports from the filtered ports using the class IDs of each port to identify IP-optical links;
• and
• verify identified IP-optical links.

A software defined network (SDN) controller for identifying IP-optical links in a network having a plurality of nodes, the SDN controller comprising:

• a memory;
• and
• a processor connected to the memory, wherein the processor is configured to:
• group network nodes into discovery groups;
• for each discovery group:
• filter ports of the nodes in the discovery group;
• produce class IDs for each filtered port using a machine learning model, wherein producing the class IDs for each filtered port includes:
• collecting a sequence of time-sampled data from all of the ports and forming an image using the collected data;
• assigning different class IDs to all of the Tx ports;
• training the machine learning model to learn the class ID for each of the Tx ports using the images formed for the Tx ports;
• and
• inputting the images for all of the Rx ports into the machine learning model to assign a class ID to each of the Rx ports;
• match IP ports to optical ports from the filtered ports using the class IDs of each port to identify IP-optical links;
• and
• verify identified IP-optical links.

A non-transitory machine-readable storage medium encoded with instructions for execution by a software defined network controller, the non-transitory machine-readable storage medium comprising:

• instructions for grouping network nodes into discovery groups;
• for each discovery group:
• instructions for filtering ports of the nodes in the discovery group;
• instructions for producing class IDs for each filtered port using a machine learning model, wherein the machine learning model matches each of a plurality of Tx ports to a respective Rx port tagged with a unique class ID;
• instructions for matching IP ports to optical ports from the filtered ports using the class IDs of each port to identify IP-optical links;
• and
• instructions for verifying identified IP-optical links.