System and method for measuring end-to-end channel capacity entropy

A computer device comprising:

• a memory configured to store instructions;
• and
• a processor configured to execute the instructions to:
• store class definitions for a network condition,
• identify multiple input features to correlate with the class definitions,
• generate a multiclass classification model that produces an importance score for each of the multiple input features, wherein the importance score reflects the contribution of an input feature to the network condition,
• select two or more of the multiple input features with highest importance scores as influential features,
• predict the behavior of the influential features to identify a current class, from the class definitions, for the network condition over an end-to-end communication channel, and
• send an estimated network condition, based on the current class, to a device for traffic optimization.

A system, comprising:

• a computing device configured to:
• store class definitions for a network condition,
• identify multiple input features to correlate with the class definitions,
• generate a multiclass classification model that produces an importance score for each of the multiple input features, wherein the importance score reflects the contribution of an input feature to the network condition,
• select two or more of the multiple input features with highest importance scores as influential features,
• predict the behavior of the influential features to identify a current class, from the class definitions, for the network condition over an end-to-end communication channel, and
• send an estimated network condition, based on the current class, to a network device for traffic optimization.