Automatically configuring network elements in multi-vendor and multi-domain topologies
Devices, computer-readable media, and methods for automatically configuring network elements in multi-vendor and multi-domain topologies. In one example, a method includes 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.
1 . A method comprising:
determining, by a processing system including at least one processor, a predicted change in a network traffic demand on a communications network, wherein a topology of the communications network includes a plurality of network functions from at least two different vendors;
predicting, by the processing system, 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 predicted change in the network traffic demand, wherein the predicting is performed using a machine learning model that is trained to take as an input the predicted change in the network traffic demand on 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, and wherein training of the machine learning model comprises:
discovering, by the processing system, 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, by the processing system, data from each network function of the plurality of network functions; and
training, by the processing system, the machine learning model, using the data, to use the predicted change in the network traffic demand on the communications network to predict the respective configuration parameter values of the subset of the plurality of network functions that will support the predicted change in the network traffic demand; and
modifying, by the processing system, configurations of the network functions in the subset to reflect the respective configuration parameter values.
2 . The method of claim 1 , wherein the plurality of network functions includes a plurality of virtual network functions, a plurality of physical network functions, and a plurality of cloud-native network functions.
3 . The method of claim 1 , wherein the plurality of network functions includes network functions having different features and different parameters that are configurable.
4 . The method of claim 1 , wherein the plurality of network functions includes network functions that utilize different proprietary data models and schema definitions.
5 . The method of claim 1 , wherein the communications network further includes at least two domains.
6 . The method of claim 1 , wherein the determining is also performed using the machine learning model.
7 . The method of claim 1 , wherein the machine learning model is a generalized net model.
8 . The method of claim 1 , wherein the discovering, the collecting, and the training are repeated in response to a change in the topology of the communications network.
9 . The method of claim 1 , wherein the data is stored in a database for use in identifying at least one trend in a usage of the communications network.
10 . The method of claim 1 , wherein the subset includes network functions that originate with at least two different vendors of the at least two different vendors.
11 . The method of claim 1 , wherein the processing system obtains an approval from a human administrator prior to the modifying.
12 . The method of claim 11 , wherein the processing system presents an estimated monetary cost of the modifying to the human administrator prior to obtaining the approval.
13 . The method of claim 11 , wherein the processing system presents an estimated impact of the modifying on operation of the communications network to the human administrator prior to obtaining the approval.
14 . The method of claim 13 , wherein the estimated impact of the modifying on operation of the communications network comprises a limitation on an operational capacity of the communications network while the modifying occurs.
15 . The method of claim 1 , wherein the modifying is performed during a scheduled maintenance window for the communications network.
16 . 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 predicted change in a network traffic demand on a communications network, wherein 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 predicted change in the network traffic demand, wherein the predicting is performed using a machine learning model that is trained to take as an input the predicted change in the network traffic demand on 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, and 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; and
training the machine learning model, using the data, to use the predicted change in the network traffic demand on the communications network to predict the respective configuration parameter values of the subset of the plurality of network functions that will support the predicted change in the network traffic demand; and
modifying configurations of the network functions in the subset to reflect the respective configuration parameter values.
17 . 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 predicted change in a network traffic demand on a communications network, wherein 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 predicted change in the network traffic demand, wherein the predicting is performed using a machine learning model that is trained to take as an input the predicted change in the network traffic demand on 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, and 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; and
training the machine learning model, using the data, to use the predicted change in the network traffic demand on the communications network to predict the respective configuration parameter values of the subset of the plurality of network functions that will support the predicted change in the network traffic demand; and
modifying configurations of the network functions in the subset to reflect the respective configuration parameter values.
18 . The system of claim 17 , wherein the plurality of network functions includes a plurality of virtual network functions, a plurality of physical network functions, and a plurality of cloud-native network functions.
19 . The system of claim 17 , wherein the plurality of network functions includes network functions having different features and different parameters that are configurable.
20 . The system of claim 17 , wherein the plurality of network functions includes network functions that utilize different proprietary data models and schema definitions.