Automatically configuring network elements in multi-vendor and multi-domain topologies

A non-transitory computer-readable medium storing instructions which, when executed by a processing system including at least one processor, cause the processing system to perform operations, the operations comprising:

• determining a need of a communications network, where a topology of the communications network includes a plurality of network functions from at least two different vendors;
• predicting a subset of the plurality of network functions and respective configuration parameter values for network functions in the subset, that are expected to support the need of the communications network;
• and
• modifying the configurations of the network functions in the subset to reflect the respective configuration parameter values,
• wherein at least one of:
• the determining or the predicting is performed using a machine learning model that is trained to take as an input the need of the communications network and to generate as an output a prediction as to the subset of the plurality of network functions and the respective configuration parameter values for the network functions in the subset, wherein training of the machine learning model comprises:
• discovering the topology of the communications network, including the plurality of network functions and a plurality of communication links between the plurality of network functions;
• collecting data from each network function of the plurality of network functions, wherein the collecting comprises:
• selecting a route in the communications network, a source network function of the plurality of network functions for the route, and a destination network function of the plurality of network functions for the route;
• testing various combinations of a plurality of combinations of configuration parameter settings for the route;
• and
• repeating the selecting and the testing for other routes in the communications network;
• and
• training the machine learning model, using the data, to predict the need of the communications network and the respective configurations of the plurality of network functions that will support the need.

A system comprising:

• a processing system including at least one processor;
• and
• a computer-readable medium storing instructions which, when executed by the processing system, cause the processing system to perform operations, the operations comprising:
• determining a need of a communications network, where a topology of the communications network includes a plurality of network functions from at least two different vendors;
• predicting a subset of the plurality of network functions and respective configuration parameter values for network functions in the subset, that are expected to support the need of the communications network;
• and
• modifying the configurations of the network functions in the subset to reflect the respective configuration parameter values,
• wherein at least one of:
• the determining or the predicting is performed using a machine learning model that is trained to take as an input the need of the communications network and to generate as an output a prediction as to the subset of the plurality of network functions and the respective configuration parameter values for the network functions in the subset, wherein training of the machine learning model comprises:
• discovering the topology of the communications network, including the plurality of network functions and a plurality of communication links between the plurality of network functions;
• collecting data from each network function of the plurality of network functions, wherein the collecting comprises:
• selecting a route in the communications network, a source network function of the plurality of network functions for the route, and a destination network function of the plurality of network functions for the route;
• testing various combinations of a plurality of combinations of configuration parameter settings for the route;
• and
• repeating the selecting and the testing for other routes in the communications network;
• and
• training the machine learning model, using the data, to predict the need of the communications network and the respective configurations of the plurality of network functions that will support the need.