Systems and methods for proactive network operations

1. A non-transitory computer-readable medium comprising instructions that, when executed, cause a processor to perform the steps of:

obtaining telemetry data associated with a network having a plurality of network elements;

presenting a list of ongoing issues and predicted issues based on the telemetry data, on a display;

responsive to a selection of an issue that is one of the ongoing issues and the predicted issues in the list, presenting a root cause analysis of the issue including multiple diagnoses associated with multiple, respective root causes of the issue, each of the multiple diagnoses indicating the associated root cause and including an associated predictability factor, displayed with the root cause analysis, indicating a degree of confidence that the associated root cause is an actual root cause of the issue, the predictability factor included in each of the diagnoses collectively forming a plurality of predictability factors;

presenting a list of prescriptive actions on the display to address the issue based on the root cause analysis, the list of prescriptive actions including a mechanism for a user that causes execution of any of the prescriptive actions;

receiving a selection of one or more of the prescriptive actions from the user; and

responsive to the selection and implementation of the one or more of the prescriptive actions and an impact based on the implementation of the one or more of the prescriptive actions, updating a particular predictability factor of the plurality of predictability factors, the particular predictability factor indicating how reliable the associated diagnosis is, and storing the updated predictability factor for future diagnosis.

2. The non-transitory computer-readable medium of claim 1 , the instructions further comprising

managing a plurality of issue files each for a predetermined issue, wherein the root cause analysis, the particular predictability factor, and the list of prescriptive actions for the selected issue is in a corresponding issue file.

3. The non-transitory computer-readable medium of claim 2 , the instructions further comprising

responsive to feedback, including the selection of the one or more of the prescriptive actions from the user, updating multiple diagnoses in the corresponding issue file.

4. The non-transitory computer-readable medium of claim 2 , wherein the root cause analysis identifies multiple diagnoses each having a different list of prescriptive actions.

5. The non-transitory computer-readable medium of claim 2 , the instructions further comprising

prior to the obtaining, creating the plurality of issue files utilizing historical data and associated patterns and trends therein.

6. The non-transitory computer-readable medium of claim 2 , wherein the particular predictability factor is below a threshold, the instructions further comprising:

quarantining the issue for review; and

responsive to manual review of the issue, updating the corresponding issue files based on the manual review, to improve the particular predictability factor.

7. The non-transitory computer-readable medium of claim 1 , wherein the selected issue is a predicted issue, the instructions further comprising creating a new ticket in a ticketing system for the predicted issue.

8. The non-transitory computer-readable medium of claim 1 , wherein the root cause analysis and the prescriptive actions are based on machine learning techniques.

9. An apparatus comprising:

a network interface;

a processor communicatively coupled to the network interface; and

memory storing instructions that, when executed, cause the processor to

obtain telemetry data associated with a network having a plurality of network elements,

present a list of ongoing issues and predicted issues based on the telemetry data, for a display,

responsive to a selection of an issue that is one of the ongoing issues and the predicted issues in the list, present a root cause analysis of the issue including multiple diagnoses associated with multiple, respective root causes of the issue, each of the multiple diagnoses indicating the associated root cause and including an associated predictability factor, displayed with the root cause analysis, indicating a degree of confidence that the associated root cause is an actual root cause of the issue, the predictability factor included in each of the diagnoses collectively forming a plurality of predictability factors;

present a list of prescriptive actions on the display to address the issue based on the root cause analysis, the list of prescriptive actions including a mechanism for a user that causes execution of any of the prescriptive actions;

receive a selection of one or more of the prescriptive actions from the user; and

responsive to the selection and implementation of the one or more of the prescriptive actions and an impact based on the implementation of the one or more of the prescriptive actions, update a particular predictability factor of the plurality of predictability factors, the particular predictability factor indicating how reliable the associated diagnosis is, and storing the updated predictability factor for future diagnosis.

10. The apparatus of claim 9 , wherein the memory storing instructions that, when executed, further cause the processor to

manage a plurality of issue files each for a predetermined issue, wherein the root cause analysis, the particular predictability factor, and the list of prescriptive actions for the selected issue is in a corresponding issue file.

11. The apparatus of claim 10 , wherein the memory storing instructions that, when executed, further cause the processor to

responsive to feedback, including the selection of the one or more of the prescriptive actions from the user, update the multiple diagnoses in the corresponding issue file.

12. The apparatus of claim 10 , wherein the root cause analysis identifies multiple diagnoses each having a different list of prescriptive actions.

13. The apparatus of claim 10 , wherein the memory storing instructions that, when executed, further cause the processor to

prior to the telemetry data being obtained, create the plurality of issue files utilizing historical data and associated patterns and trends therein.

14. The apparatus of claim 10 , wherein the particular predictability factor is below a threshold, and wherein the memory storing instructions that, when executed, further cause the processor to

quarantine the issue for review, and

responsive to manual review of the issue, update the corresponding issue files based on the manual review, to improve the particular predictability factor.

15. The apparatus of claim 10 , wherein the selected issue is a predicted issue, and wherein the memory storing instructions that, when executed, further cause the processor to

create a new ticket in a ticketing system for the predicted issue.

16. The apparatus of claim 10 , wherein the root cause analysis and the prescriptive actions are based on machine learning techniques.

17. A method comprising:

obtaining telemetry data associated with a network having a plurality of network elements;

presenting a list of ongoing issues and predicted issues based on the telemetry data, on a display;

responsive to a selection of an issue that is one of the ongoing issues and the predicted issues in the list, presenting a root cause analysis of the issue including multiple diagnoses associated with multiple, respective root causes of the issue, each of the multiple diagnoses indicating the associated root cause and including an associated predictability factor, displayed with the root cause analysis, indicating a degree of confidence that the associated root cause is an actual root cause of the issue, the predictability factor included in each of the diagnoses collectively forming a plurality of predictability factors;

presenting a list of prescriptive actions on the display to address the issue based on the root cause analysis, the list of prescriptive actions including a mechanism for a user that causes execution of any of the prescriptive actions;

receiving a selection of one or more of the prescriptive actions from the user; and

responsive to the selection and implementation of the one or more of the prescriptive actions and an impact based on the implementation of the one or more of the prescriptive actions, updating a particular predictability factor of the plurality of predictability factors, the particular predictability factor indicating how reliable the associated diagnosis is, and storing the updated predictability factor for future diagnosis.