Predictive system remediation

Patent metadata

Field	Value
Publication number	US-11288117-B2
Application number	US-201916532548-A
Country	US
Kind code	B2
Filing date	Aug 6, 2019
Priority date	Aug 6, 2019
Publication date	Mar 29, 2022
Grant date	Mar 29, 2022

Abstract

Techniques for predictive system remediation are disclosed. Based on attributes associated with applications of one or more system-selected remedial actions to one or more problematic system behaviors in a system (e.g., a database system), the system determines a predicted effectiveness of one or more future applications of a remedial action to a particular problematic system behavior, as of one or more future times. The system determines that the predicted effectiveness of the one or more future applications of the remedial action is positive but does not satisfy a performance criterion. Responsive to determining that the predicted effectiveness is positive but does not satisfy the performance criterion, the system generates a notification corresponding to the predicted effectiveness not satisfying the performance criterion. The system applies the remedial action to the particular problematic system behavior, despite already determining that the predicted effectiveness does not satisfy the one or more performance criteria.

First claim

What is claimed is:

1. One or more non-transitory machine-readable media storing instructions which, when executed by one or more processors, cause:
based at least on a plurality of attributes associated with a plurality of applications of one or more system-selected remedial actions to one or more problematic system behaviors in a database system: determining a first predicted effectiveness of one or more future applications of a first remedial action to a particular problematic system behavior in the database system as of one or more future times;
determining that the first predicted effectiveness of the one or more future applications of the first remedial action is positive but does not satisfy one or more performance criteria;
responsive to determining that the first predicted effectiveness of the one or more future applications of the first remedial action is positive but does not satisfy the one or more performance criteria:
generating a notification corresponding to the first predicted effectiveness of the one or more future applications of the first remedial action not satisfying the one or more performance criteria;
applying the first remedial action to the particular problematic system behavior despite already determining that the first predicted effectiveness of the one or more future applications of the first remedial action does not satisfy the one or more performance criteria.

2. The one or more media of claim 1 , further storing instructions which, when executed by one or more processors, cause:
monitoring the database system for one or more candidate problematic system behaviors;
automatically detecting an instance of the particular problematic system behavior while monitoring the database system, without receiving any user input to identify the instance of the particular problematic system behavior;
wherein applying the first remedial action to the particular problematic system behavior is performed automatically responsive to detecting the instance of the particular problematic system behavior while monitoring the database system, without receiving any user input about the first remedial action after automatically detecting the instance of the particular problematic system behavior.

3. The one or more media of claim 2 , wherein detecting the particular problematic system behavior comprises determining that a system behavior is problematic for a portion of a season in which the system behavior is detected, wherein the system behavior is determined to vary over time within a seasonal pattern that is determined to repeat over time.

4. The one or more media of claim 1 , further storing instruction which, when executed by one or more processors, cause:
monitoring the plurality of applications of the one or more system-selected remedial actions to the one or more problematic system behaviors in the database system, to obtain the plurality of attributes associated with the plurality of applications of the one or more system-selected remedial actions.

5. The one or more media of claim 1 , further storing instructions which, when executed by one or more processors, cause:
determining a second predicted effectiveness of one or more future applications of a second remedial action to the particular problematic system behavior in the database system;
determining, based on a comparison of the first predicted effectiveness and the second predicted effectiveness, that the first remedial action is a better candidate than the second remedial action to remediate the particular problematic system behavior.

6. The one or more media of claim 5 , further storing instructions which, when executed by one or more processors, cause:
determining that the second remedial action is compatible with the first remedial action, and applying both the first remedial action to the particular problematic system behavior and the second remedial action to the particular problematic system behavior.

7. The one or more media of claim 1 , further storing instructions which, when executed by one or more processors, cause:
receiving, via an administrative interface of the database system, user input to configure the one or more performance criteria by adjusting an expected amount of time needed to troubleshoot unsolved problematic system behavior;
configuring the one or more performance criteria based at least on the user input.

8. The one or more media of claim 1 , wherein the plurality of attributes associated with the plurality of applications of the one or more system-selected remedial actions comprise at least:
a first subset of the plurality of attributes associated with a first application of the first remedial action to the particular problematic system behavior; and
a second subset of the plurality of attributes associated with a second application of the first remedial action to the particular problematic system behavior.

9. The one or more media of claim 1 , wherein determining that the predicted effectiveness of the one or more future applications of the first remedial action is positive but does not satisfy the one or more performance criteria comprises:
predicting that a series of applications of the first remedial action to the particular problematic system behavior is effective but decreasing in effectiveness.

10. The one or more media of claim 1 , wherein determining that the predicted effectiveness of the one or more future applications of the first remedial action is positive but does not satisfy the one or more performance criteria comprises:
predicting that after the one or more future applications of the first remedial action, the problematic system behavior will return within a threshold amount of time.

11. The one or more media of claim 1 , wherein determining the first predicted effectiveness of the one or more future applications of the first remedial action to the particular problematic system behavior is further based on metadata describing one or more manual actions performed on the database system.

12. The one or more media of claim 11 , wherein the one or more manual actions comprise replacing a physical component of the database system.

13. The one or more media of claim 1 , wherein determining the first predicted effectiveness of one or more future applications of the first remedial action to the particular problematic system behavior in the database system comprises:
applying current system state data to a machine learning model configured to predict future effectiveness of remedial actions.

14. The one or more media of claim 13 , further storing instructions which, when executed by one or more processors, cause:
training the machine learning model, using the plurality of attributes associated with the plurality of applications of the one or more system-selected remedial actions to the one or more problematic system behaviors in the database system.

15. The one or more media of claim 1 , wherein the particular problematic system behavior is associated with increased database response time.

16. The one or more media of claim 1 , wherein the particular problematic system behavior is associated with increased processor utilization.

17. The one or more media of claim 1 , wherein the particular problematic system behavior is associated with reduced available database storage.

18. The one or more media of claim 1 , wherein the particular problematic system behavior is associated with an increased database request rate.

19. The one or more media of claim 1 , wherein the notification comprises a link to a graphic

Assignees

• Oracle Int Corp

Inventors

• Sutton Eric
• Garvey Dustin
• Salunke Sampanna Shahaji
• Shaft Uri

Classifications

• H02J7/70

  characterised by the mechanical construction · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06N3/092

  Reinforcement learning · CPC title

• G06N20/00

  Machine learning · CPC title