Action recommendation to reduce server management errors

Patent metadata

Field	Value
Publication number	US-11500705-B2
Application number	US-201916673398-A
Country	US
Kind code	B2
Filing date	Nov 4, 2019
Priority date	Jun 22, 2016
Publication date	Nov 15, 2022
Grant date	Nov 15, 2022

Abstract

An actuator to execute on a server may be automatically selected based on risk of failure and damage to the server. Requirement specification and environment parameters may be received. A subset of actuators may be selected based on a risk threshold from an actuator catalog database storing actuator information and actuator risk metadata associated with a plurality of actuators. The actuator risk metadata may be augmented with risk information. A ranked list of the subset of actuators may be generated based on the actuator risk metadata associated with each actuator in the subset. An actuator in the ranked list may be executed on the server.

First claim

We claim:

1. A computer-implemented method, comprising:
receiving requirement specification and environment parameters, the requirement specification including at least a type of a command desired to be executed;
searching an actuator catalog database storing actuator information and actuator risk metadata associated with a plurality of actuators, for a set of actuators that meet the requirement specification and the environment parameters, the actuator information including at least usage data associated with an actuator and a number of times the actuator's execution has not helped solve an issue, the actuator risk metadata including at least a description of the actuator's command;
selecting from the set of actuators, a subset of actuators based on a risk threshold;
augmenting the actuator risk metadata with risk of failure information determined by performing design-time analysis at intervals; and
causing executing of an actuator in the subset of actuators on a server.

2. The method of claim 1 , wherein the actuator risk metadata comprises at least a risk probability metric per actuator and usage parameters, the risk probability metric computed by at least analyzing the actuators for detecting parameter values and options that has risk of failure.

3. The method of claim 2 , wherein the risk probability metric is further computed by analyzing the actuators for read-only and write operations.

4. The method of claim 3 , wherein the risk probability metric is further computed by analyzing the actuators against target runtime platforms that have risk of failure.

5. The method of claim 4 , wherein the risk probability metric is further computed by obtaining input from a user.

6. The method of claim 5 , wherein the risk probability metric is further computed based on aggregated results of the analyzing the actuators for detecting parameter values and options that has risk of failure, the analyzing the actuators for read-only and write operations, the analyzing the actuators against target runtime platforms that have risk of failure, and the input from the user.

7. The method of claim 2 , further comprising dynamically updating the risk probability metric based on usage results of the actuator.

8. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions readable by a device to cause the device to perform a method comprising:
receiving requirement specification and environment parameters, the requirement specification including at least a type of a command desired to be executed;
searching an actuator catalog database storing actuator information and actuator risk metadata associated with a plurality of actuators, for a set of actuators that meet the requirement specification and the environment parameters, the actuator information including at least usage data associated with an actuator and a number of times the actuator's execution has not helped solve an issue, the actuator risk metadata including at least a description of the actuator's command;
selecting from the set of actuators, a subset of actuators based on a risk threshold;
augmenting the actuator risk metadata with risk of failure information determined by performing design-time analysis at intervals; and
causing executing of an actuator in the subset of actuators on a server.

9. The computer program product of claim 8 , wherein the actuator risk metadata comprises at least a risk probability metric per actuator and usage parameters, the risk probability metric computed by at least analyzing the actuators for detecting parameter values and options that has risk of failure.

10. The computer program product of claim 9 , wherein the risk probability metric is further computed by analyzing the actuators for read-only and write operations.

11. The computer program product of claim 10 , wherein the risk probability metric is further computed by analyzing the actuators against target runtime platforms that have risk of failure.

12. The computer program product of claim 11 , wherein the risk probability metric is further computed by obtaining input from a user.

13. The computer program product of claim 12 , wherein the risk probability metric is further computed based on aggregated results of the analyzing the actuators for detecting parameter values and options that has risk of failure, the analyzing the actuators for read-only and write operations, the analyzing the actuators against target runtime platforms that have risk of failure, and the input from the user.

14. The computer program product of claim 9 , further comprising dynamically updating the risk probability metric based on usage results of the actuator.

15. A system comprising:
at least one hardware processor;
at least one storage device operatively connected to the at least one hardware processor, the at least one storage device storing an actuator catalog database storing actuator information and actuator risk metadata associated with a plurality of actuators, the actuator information including at least usage data associated with an actuator and a number of times the actuator's execution has not helped solve an issue, the actuator risk metadata including at least a description of the actuator's command;
the at least one hardware processor configured to at least:
receive requirement specification and environment parameters, the requirement specification including at least a type of a command desired to be executed;
search an actuator catalog database storing actuator information and actuator risk metadata associated with a plurality of actuators, for a set of actuators that meet the requirement specification and the environment parameters;
select from the set of actuators, a subset of actuators based on a risk threshold;
augment the actuator risk metadata with risk of failure information determined by performing design-time analysis at intervals; and
cause executing of an actuator in the subset of actuators on a server.

16. The system of claim 15 , wherein the actuator risk metadata comprises at least a risk probability metric per actuator and usage parameters, the risk probability metric computed by at least analyzing the actuators for detecting parameter values and options that has risk of failure.

17. The system of claim 16 , wherein the risk probability metric is further computed by analyzing the actuators for read-only and write operations.

18. The system of claim 17 , wherein the risk probability metric is further computed by analyzing the actuators against target runtime platforms that have risk of failure.

19. The system of claim 18 , wherein the risk probability metric is further computed based on aggregated results of at least the analyzing the actuators for detecting parameter values and options that has risk of failure, the analyzing the actuators for read-only and write operations, the analyzing the actuators against target runtime platforms that have risk of failure, and an input from a user.

20. The system of claim 16 , wherein the at least one processor is further operable to dynamically update the risk probability metric based on usage results of the actuator.

Assignees

• IBM

Inventors

• Adam Constantin M
• Bhamidipaty Anuradha
• Nallacherry Jayan
• Padhi Debasisha K
• Ruan Yaoping
• Wu Frederick Y -F

Classifications

• G06F11/004 Primary

  Error avoidance (G06F11/07 and subgroups take precedence) · CPC title