Context-aware maintenance window identification

Patent metadata

Field	Value
Publication number	US-11921735-B2
Application number	US-202016824337-A
Country	US
Kind code	B2
Filing date	Mar 19, 2020
Priority date	Mar 19, 2020
Publication date	Mar 5, 2024
Grant date	Mar 5, 2024

Abstract

Systems and methods are provided for context-aware maintenance window identification. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: estimate a completion time of a maintenance operation; predict future usage of the IHS; identify a time window for the maintenance operation based upon the estimation and the prediction.

First claim

The invention claimed is:

1. An Information Handling System (IHS), comprising:
a processor; and
a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to:
collect utilization data comprising data measuring utilization of the processor;
categorize the collected processor utilization data according to a plurality of usage levels, wherein a lowest of the usage levels corresponds to intervals suitable for IHS maintenance operations;
based on the collected utilization data, generate a time series of predicted processor utilization;
identify predicted transitions between the plurality of usage levels in the time series of predicted processor utilization;
identify a predicted idle interval without predicted transitions above the lowest of the usage levels; and
determine when a maintenance operation can be completed in the predicted idle interval.

2. The IHS of claim 1 , wherein the maintenance operation comprises a firmware update operation.

3. The IHS of claim 1 , wherein to determine an expected duration required to complete the maintenance operation, the program instructions, upon execution, further cause the IHS to add a first completion time of a first maintenance operation to a second completion time of a second maintenance operation.

4. The IHS of claim 3 , wherein the expected duration required to complete the maintenance operation may add a time buffer to the first and second completion times.

5. The IHS of claim 1 , wherein to generate the time series of predicted processor utilization, the program instructions, upon execution, further cause the IHS to use a multivariate time series analysis.

6. The IHS of claim 5 , wherein the multivariate time series analysis comprises a probabilistic weighted fuzzy time series.

7. The IHS of claim 6 , wherein to generate the time series of predicted processor utilization, the probabilistic weighted fuzzy time series utilizes weights that are selected based upon user context information.

8. The IHS of claim 7 , wherein the user context information comprises a distance of a user from the IHS.

9. The IHS of claim 7 , wherein the user context information comprises a posture in which the user has physically configured the IHS.

10. The IHS of claim 7 , wherein the user context information comprises a hinge angle.

11. The IHS of claim 5 , wherein to use the multivariate time series analysis, the program instructions, upon execution, further cause the IHS to use one or more parameters selected from the group consisting of: external input/output (I/O) attributes, internal I/O attributes, processor usage statistics, and memory consumption statistics.

12. The IHS of claim 1 , wherein the predicted transitions between the plurality of usage levels comprise predictions whether the processor utilization will transition between different usage levels.

13. The IHS of claim 12 , wherein the identification of predicted transitions between the plurality of usage levels comprises removal of one or more spikes from the time series of predicted processor utilization.

14. The IHS of claim 1 , wherein the utilization data further comprises internal I/O data generated based on internal IHS communications and further comprises external I/O data generated based on external IHS communications.

15. A memory storage device having program instructions stored thereon that, upon execution by one or more processors of an Information Handling System (IHS), cause the IHS to:
collect utilization data comprising data measuring utilization of the processor;
categorize the collected processor utilization data according to a plurality of usage levels, wherein a lowest of the usage levels corresponds to an interval suitable for IHS maintenance operations;
based on the collected utilization data, generate a time series of predicted processor utilization;
identify predicted transitions between the plurality of usage levels in the time series of predicted processor utilization;
identify a predicted idle interval without predicted transitions above the lowest of the usage levels; and
determine when a maintenance operation can be completed in the predicted idle interval.

16. The memory storage device of claim 15 , wherein the identification of predicted transitions between the plurality of usage levels comprises removal of one or more spikes from the time series of predicted processor utilization.

17. A method, comprising:
collecting utilization data comprising data measuring utilization of the processor;
categorizing the collected processor utilization data according to a plurality of usage levels,
wherein a lowest of the usage levels corresponds to intervals suitable for Information Handling System (IHS) maintenance operations;
based on the collected utilization data, generating a time series of predicted processor utilization;
identifying predicted transitions between the plurality of usage levels in the time series of predicted processor utilization;
identifying a predicted idle interval without predicted transitions above the lowest of the usage levels; and
determining when a maintenance operation can be completed in the predicted idle interval.

18. The method of claim 17 , wherein the time series of predicted processor utilization is generated through multivariate time series.

Assignees

• Dell Products Lp

Inventors

• Vishwakarma Rahul Deo
• G Vaideeswaran
• Prasad Parmeshwr
• Gaikwad Hemant Ramesh

Classifications

• G06F16/2477 Primary

  Temporal data queries · CPC title

• G06F8/65 Primary

  Updates (security arrangements therefor G06F21/57) · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N5/048

  Fuzzy inferencing · CPC title