Method and apparatus for detecting a monitoring gap for an information handling system

1. A method comprising:

obtaining, at a monitoring gap detection subsystem, monitoring results from a monitoring data repository;

analyzing, at the monitoring gap detection subsystem, the monitoring results to identify at least one monitoring gap;

providing, at the monitoring gap detection subsystem, a monitoring gap result identifying the at least one monitoring gap to a machine learning recommender;

performing, at the monitoring gap detection subsystem, machine learning to produce a recommendation to reduce the monitoring gap; and

providing the recommendation at a user interface.

2. The method of claim 1 , wherein the providing the recommendation at the user interface comprises displaying the recommendation on a display screen.

3. The method of claim 1 , further comprising:

obtaining, at the monitoring gap detection subsystem, customer input based on the recommendation; and

adjusting, at the monitoring gap detection subsystem, the machine learning in response to the customer input.

4. The method of claim 1 , wherein the analyzing, at the monitoring gap detection subsystem, the monitoring results to identify the at least one monitoring gap comprises:

analyzing, at an operating system service gap analyzer, the monitoring results to identify an operating system service monitoring gap.

5. The method of claim 4 , wherein the analyzing, at the operating system service gap analyzer, the monitoring results to identify an operating system service monitoring gap comprises:

using a term frequency-inverse document frequency (TF-IDF) technique to statistically analyze the monitoring results for a plurality of operating system services to identify the operating system service monitoring gap.

6. The method of claim 4 , wherein the analyzing, at the monitoring gap detection subsystem, the monitoring results to identify the at least one monitoring gap comprises:

analyzing, at a uniform resource locator (URL) gap analyzer, the monitoring results to identify a URL monitoring gap.

7. The method of claim 6 , wherein the analyzing, at the monitoring gap detection subsystem, the monitoring results to identify the at least one monitoring gap comprises:

analyzing, at an agent health gap analyzer, the monitoring results to identify an agent health monitoring gap.

8. An information handling system (IHS) comprising:

an analysis block configured to obtain monitoring results from a monitoring data repository, to analyze the monitoring results to identify at least one monitoring gap, and to provide a monitoring gap result identifying the at least one monitoring gap;

a machine learning recommender configured to receive the monitoring gap result, and to produce a recommendation to reduce the monitoring gap, wherein the analysis block is configured to provide the monitoring gap result to the machine learning recommender; and

a user interface configured to display the recommendation, wherein the machine learning recommender is configured to provide the recommendation to the user interface.

9. The IHS of claim 8 , wherein the user interface comprises:

a display screen upon which the recommendation is displayed.

10. The IHS of claim 8 further comprising:

a customer-input-based arbitrator configured to obtain customer input based on the recommendation and to adjust operation of the machine learning recommender in response to the customer input.

11. The IHS of claim 8 , wherein the analysis block comprises an operating system service gap analyzer configured to analyze monitoring results to identify an operating system service monitoring gap.

12. The IHS of claim 11 , wherein the operating system service gap analyzer is configured to use a term frequency-inverse document frequency (TF-IDF) technique to statistically analyze the monitoring results for a plurality of operating system services to identify the operating system service monitoring gap.

13. The IHS of claim 11 , wherein the analysis block comprises a URL gap analyzer configured to analyze the monitoring results to identify a uniform resource locator (URL) monitoring gap.

14. The IHS of claim 13 , wherein the analysis block comprises an agent health gap analyzer configured to analyze the monitoring results to identify an agent health monitoring gap.

15. A method comprising:

obtaining, at an analysis block, monitoring results from a monitoring data repository;

analyzing, at the analysis block, the monitoring results to identify at least one monitoring gap;

providing, at the analysis block, a monitoring gap result identifying the at least one monitoring gap to a machine learning recommender;

performing, at the machine learning recommender, machine learning to produce a recommendation to reduce the monitoring gap; and

providing the recommendation at a user interface.

16. The method of claim 15 , wherein the providing the recommendation at the user interface comprises displaying the recommendation on a display screen.

17. The method of claim 15 , further comprising:

obtaining, at a customer-input-based arbitrator, customer input based on the recommendation; and

adjusting, at the machine learning recommender, the machine learning in response to the customer input.

18. The method of claim 15 , wherein the analyzing, at the analysis block, the monitoring results to identify the at least one monitoring gap comprises:

analyzing, at an operating system service gap analyzer, the monitoring results to identify an operating system service monitoring gap.

19. The method of claim 18 , wherein the analyzing, at the analysis block, the monitoring results to identify the at least one monitoring gap comprises:

analyzing, at a uniform resource locator (URL) gap analyzer, the monitoring results to identify a URL monitoring gap.

20. The method of claim 19 , wherein the analyzing, at the analysis block, the monitoring results to identify the at least one monitoring gap comprises:

analyzing, at an agent health gap analyzer, the monitoring results to identify an agent health monitoring gap.