Learning and classifying workloads powered by enterprise infrastructure

1. A computer-implementable method for performing a workload classification and analysis operation within an information technology (IT) infrastructure, comprising:

receiving workload data from a data source, the data source comprising an information handling system executing within the IT infrastructure;

generating a neural network model from the workload data via a workload classification and analysis system, the workload classification and analysis system performing the workload classification and analysis operation, the workload classification and analysis operation automatically classifying workloads executing on the information handling system within the IT infrastructure;

defining a plurality of workload signatures via the workload classification and analysis system, the plurality of workload signatures defining a set of characteristics associated with a particular type of workload at a particular point in time;

identifying particular workloads using the plurality of workload signatures and the neural network model via the workload classification and analysis system, the particular workloads corresponding to respective kinds of applications executing on the information handling system executing within the IT infrastructure; and,

providing information regarding the particular workloads to a user, the information being provided by the workload classification and analysis system and being presented via a workload classification user interface of a user device.

2. The method of claim 1 , further comprising:

performing a similarity scoring operation to generate a similarity score on identified component details; and,

ranking the list of workload signatures based upon the similarity score.

3. The method of claim 1 , wherein:

the data source comprises an external unstructured data source.

4. The method of claim 1 , wherein:

the data source comprises an internal data source.

5. The method of claim 1 , wherein:

the neural network model comprises a doc2vec type neural network model.

6. The method of claim 1 , wherein:

the doc2vec type neural network model comprises a distributed bag of words type doc2vec model.

7. A system comprising:

a processor;

a data bus coupled to the processor; and

a non-transitory, computer-readable storage medium embodying computer program for performing a workload classification and analysis operation within an information technology (IT) infrastructure code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for:

receiving workload data from a data source, the data source comprising an information handling system executing within the IT infrastructure;

generating a neural network model from the workload data via a workload classification and analysis system, the workload classification and analysis system performing the workload classification and analysis operation, the workload classification and analysis operation automatically classifying workloads executing on the information handling system within the IT infrastructure;

defining a plurality of workload signatures via the workload classification and analysis system, the plurality of workload signatures defining a set of characteristics associated with a particular type of workload at a particular point in time;

identifying particular workloads using the plurality of workload signatures and the neural network model via the workload classification and analysis system, the particular workloads corresponding to respective kinds of applications executing on information handling system executing within the IT infrastructure; and,

providing information regarding the particular workloads to a user, the information being provided by the workload classification and analysis system and being presented via a workload classification user interface of a user device.

8. The system of claim 7 , wherein the instructions executable by the processor are further configured for:

performing a similarity scoring operation to generate a similarity score on identified component details; and,

ranking the list of workload signatures based upon the similarity score.

9. The system of claim 7 , wherein the instructions executable by the processor are further configured for:

the data source comprises an external unstructured data source.

10. The system of claim 7 , wherein:

the data source comprises an internal data source.

11. The system of claim 7 , wherein the instructions executable by the processor are further configured for:

the neural network model comprises a doc2vec type neural network model.

12. The system of claim 11 , wherein:

the doc2vec type neural network model comprises a distributed bag of words type doc2vec model.

13. A non-transitory, computer-readable storage medium embodying computer program code for performing a workload classification and analysis operation within an information technology (IT) infrastructure, the computer program code comprising computer executable instructions configured for:

receiving workload data from a data source receiving workload data from a data source, the data source comprising an information handling system executing within the IT infrastructure;

generating a neural network model from the workload data via a workload classification and analysis system, the workload classification and analysis system performing the workload classification and analysis operation, the workload classification and analysis operation automatically classifying workloads executing on the information handling system within the IT infrastructure;

defining a plurality of workload signatures via the workload classification and analysis system, the plurality of workload signatures defining a set of characteristics associated with a particular type of workload at a particular point in time;

identifying particular workloads using the plurality of workload signatures and the neural network model via the workload classification and analysis system, the particular workloads corresponding to respective kinds of applications executing on information handling system executing within the IT infrastructure; and,

providing information regarding the particular workloads to a user, the information being provided by the workload classification and analysis system and being presented via a workload classification user interface of a user device.

14. The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are further configured for:

performing a similarity scoring operation to generate a similarity score; and,

ranking the list of workload signatures based upon the similarity score.

15. The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are further configured for:

the data source comprises an external unstructured data source.

16. The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are further configured for:

the data source comprises an internal data source.

17. The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are further configured for:

the neural network model comprises a doc2vec type neural network model.

18. The non-transitory, computer-readable storage medium of claim 17 , wherein:

the doc2vec type neural network model comprises a distributed bag of words type doc2vec model.

19. The non-transitory, computer-readable storage medium of claim 13 , wherein:

the computer executable instructions are deployable to a client system from a server system at a remote location.

20. The non-transitory, computer-readable storage medium of claim 13 , wherein:

the computer executable instructions are provided by a service provider to a user on an on-demand basis.