Intelligent device role discovery

1. A method of modeling services in a network, comprising:

identifying externally mapped attributes in a vendor neutral yang model of a network service;

assigning an attribute type to one or more of the externally mapped attributes, wherein assigning includes determining, by a learning system, the attribute type of one or more of the externally mapped attributes based on an analysis of attribute types assigned to the externally mapped attributes of other network services;

constructing a separate dependency graph for each of the externally mapped attributes; and

building a separate hidden service model for each of the externally mapped attributes, wherein the externally mapped attribute for each hidden service model is a merge attribute for that hidden service model.

2. The method of claim 1 , wherein assigning an attribute type to one or more of the externally mapped attributes includes:

determining if the attribute type assigned to each attribute was correct; and

retraining the learning system when the attribute type assigned to one or more of the externally mapped attributes was not the correct.

3. The method of claim 1 , wherein identifying externally mapped attributes in a vendor neutral yang model includes:

identifying referrers for each configuration attribute in the vendor neutral yang model, wherein identifying referrers includes identifying possible matches for the referrers;

identifying leafref instances;

marking one of the leafref instances as leaf; and

isolating the leaf from the referrers.

4. The method of claim 1 , wherein identifying externally mapped attributes in a vendor neutral yang model includes identifying shared attributes.

5. The method of claim 1 , wherein building a separate hidden service model for each of the externally mapped attributes includes storing attributes for each hidden service model as key value pairs.

6. The method of claim 1 , wherein building a separate hidden service model for each of the externally mapped attributes includes storing attributes for each hidden service model as key value pairs, wherein each key value pair further includes a flag indicating the attribute type assigned to the attribute associated with the key value pair.

7. The method of claim 6 , wherein the attribute type is selected from a group of attribute types including a service level attribute, an endpoint attribute and a hardcoded mapping attribute.

8. The method of claim 1 , wherein the method further comprises:

identifying device roles associated with each hidden service model instance; and

modeling different types of endpoints based on the identified device roles.

9. The method of claim 1 , wherein the method further comprises:

receiving a request to create a hidden service model;

replying to the request with prompts including one or more possible hidden service models;

receiving a request to customize one or more of the hidden service models sent with the prompts; and

storing the one or more customized hidden service models as a user-defined network service model.

10. A network management system, comprising:

a network interface;

a user interface;

memory; and

one or more processors connected to the memory, wherein the memory includes instructions that, when executed by the one or more processors, cause the processors to:

train a learning module to identify attribute types;

identify externally mapped attributes in a vendor neutral yang model;

assign an attribute type to each of the externally mapped attributes, wherein the attribute type is assigned by the learning module;

construct a separate dependency graph for each of the externally mapped attributes; and

build a separate hidden service model for each of the externally mapped attributes, wherein the externally mapped attribute for each hidden service model is a merge attribute for that hidden service model.

11. The system of claim 10 , wherein the instructions that cause the processors to assign an attribute type to each of the externally mapped attributes include instructions that, when executed by the one or more processors, cause the processors to:

determine if the attribute type assigned to each attribute was correct; and

retrain the learning module if the attribute type assigned to each attribute was not correct.

12. The system of claim 10 , wherein the instructions that cause the processors to identify externally mapped attributes in a vendor neutral yang model include instructions that, when executed by the one or more processors, cause the processors to:

identify referrers for each configuration attribute in the vendor neutral yang model, wherein identifying referrers includes identifying possible matches for the referrers.

13. The system of claim 10 , wherein the instructions that cause the processors to identify externally mapped attributes in a vendor neutral yang model include instructions that, when executed by the one or more processors, cause the processors to identify shared attributes.

14. The system of claim 10 , wherein the instructions that cause the processors to build a separate hidden service model for each of the externally mapped attributes include instructions that, when executed by the one or more processors, cause the processors to store attributes for each hidden service model as key value pairs, wherein each key value pair includes a flag indicating the attribute type assigned to the attribute associated with the key value pair.

15. The system of claim 10 , wherein the memory further includes instructions that, when executed by the one or more processors, cause the processors to:

identify device roles associated with each service model instance; and

model different types of endpoints based on the identified device roles.

16. A non-transitory computer-readable storage medium storing instructions that, when executed, cause one or more processors to:

train a learning module to identify attribute types;

identify externally mapped attributes in a vendor neutral yang model;

assign an attribute type to each of the externally mapped attributes, wherein the attribute type is assigned by the learning module;

construct a separate dependency graph for each of the externally mapped attributes; and

build a separate hidden service model for each of the externally mapped attributes, wherein the externally mapped attribute for each hidden service model is a merge attribute for that hidden service model.

17. The computer-readable storage medium of claim 16 , wherein the instructions include instructions that, when executed by the one or more processors, cause the processors to:

determine if the attribute type assigned to each externally mapped attribute was correct; and

if the attribute type assigned to one or more of the externally mapped attributes was incorrect, retrain the learning module.

18. The computer-readable storage medium of claim 16 , wherein the instructions include instructions that, when executed by the one or more processors, cause the processors to merge two or more of the hidden service models.

19. The computer-readable storage medium of claim 16 , wherein the instructions that cause the processors to build a separate hidden service model for each of the externally mapped attributes include instructions that, when executed by the one or more processors, cause the processors to store attributes for each hidden service model as key value pairs, wherein each key value pair further includes a flag indicating the attribute type assigned to the attribute associated with the key value pair.

20. The computer-readable storage medium of claim 16 , wherein the instructions include instructions that, when executed by the one or more processors, cause the processors to:

identify device roles associated with each service model instance; and

model different types of endpoints based on the identified device roles.