Managing the performance of an electronic device

• US 9,219,663 B1
• Filed: 10/15/2013
• Issued: 12/22/2015
• Est. Priority Date: 03/30/2007
• Status: Active Grant

• Current Assignee: United Services Automobile Association
• Original Assignee: United Services Automobile Association
• Inventors: Becker, Glen A.
• Primary Examiner(s): Hwang, Joon H
• Assistant Examiner(s): LIN, SHERMAN L
• Application Number:
  US14/054,502
• Time in Patent Office: 798 Days
• Field of Search: 709/223, 709/224, 709/226
• US Class Current: 1/1
• CPC Class Codes: G06F 11/3442 for planning or managing the needed capacity
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3495 for systems
• CPC Class Codes: G06F 2201/81 Threshold
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation

Abstract

A performance management system and method for generating a plurality of forecasts for one or more electronic devices is presented. The forecasts are generated from stored performance data and analyzed to determine which devices are likely to experience performance degradation within a predetermined period of time. A single forecast is extracted for further analysis such that computer modeling may be performed upon the performance data to enable the user to predict when device performance will begin to degrade. In one embodiment, graphical displays are created for those devices forecasted to perform at an undesirable level such that suspect devices may be subjected to further analysis.

Claims

1. A method comprising:

using one or more processors to perform the following;

collecting and analyzing historic resource utilization data relating to a plurality of computing devices, the historic resource utilization data representing a load placed on a set of finite resources over a predefined period of time;

generating an exponential growth model for each computing device based on analysis of the historic resource utilization data;

generating a linear growth model by adjusting the exponential growth model into a linear growth model at and beyond a point having a slope that exceeds a threshold multiple of a slope at a point at an end of a most recent portion of the historic resource utilization data in the exponential growth model; and

adjusting workload or capability of the plurality of computing devices based at least on the exponential growth model or the linear growth model.

2. The method of claim 1, further comprising creating a resource utilization forecast for each computing device from the exponential growth model or the linear growth model.

3. The method of claim 2, further comprising receiving an assignment of a threshold value for the resource utilization forecast of each computing device.

4. The method of claim 3, further comprising identifying an earliest forecasted date at which the threshold value is exceeded for the particular computing device based on the resource utilization forecast and the threshold value of each computing device.

5. The method of claim 4, wherein the threshold value for the linear growth model is substantially at an 84% confidence interval.

6. The method of claim 4, wherein the threshold value for the linear growth model creates a 1-sigma safety factor.

7. The method of claim 4, further comprising determining a type of the computing device based on a format of the collected historic resource utilization data.

8. The method of claim 4, wherein identifying an earliest forecasted date comprises identifying where the threshold value is first exceeded for the linear growth model and the exponential growth model.

9. The method of claim 4, further comprising:

determining whether the particular computing device is in need of attention to prevent a failure or performance degradation based on the resource utilization forecast for the particular computing device and the assignment of the threshold value; and

adjusting workload or capability of the plurality of computing devices based on a determination that the particular computing device is in need of attention.

10. A non-transitory computer-readable medium having computer-readable instructions stored thereon that are executable by a processor to:

collect and analyze historic resource utilization data relating to a plurality of computing devices, the historic resource utilization data representing a load placed on a set of finite resources over a predefined period of time;

generate an exponential growth model for each computing device based on analysis of the historic resource utilization data;

generating a linear growth model by adjusting the exponential growth model into a linear growth model at and beyond a point having a slope that exceeds a threshold multiple of a slope at a point at an end of a most recent portion of the historic resource utilization data in the exponential growth model;

identify a computing device from the plurality of computing devices with an earliest utilization forecast that exceeds a threshold value, wherein the earliest utilization forecast is created using the exponential growth model or the linear growth model; and

adjusting workload or capability of the computing device based at least on the exponential growth model or the linear growth model.

11. The computer readable medium of claim 10, wherein the threshold value for the linear growth model is substantially at an 84% confidence interval.

12. The computer readable medium of claim 10, wherein the threshold value for the linear growth model creates a 1-sigma safety factor.

13. The computer readable medium of claim 10, further comprising instructions stored thereon that are executed by the processor to determine a type of the computing device based on a format of the collected historic resource utilization data.

14. The computer readable medium of claim 10, wherein the instructions to identify the earliest utilization forecasted cause the processor to identify where the threshold value is first exceeded for the linear growth model and the exponential growth model.

15. The computer readable medium of claim 10, further comprising instructions stored thereon that are executed by the processor to:

determine whether the particular computing device is in need of attention to prevent a failure or performance degradation based on the earliest utilization forecast for the computing device and the threshold value; and

adjust workload or capability of the plurality of computing devices based on a determination that the computing device is in need of attention.

16. A system comprising:

a processor;

memory coupled to the processor and comprising computer-readable instructions saved thereon that are executed to;

collect and analyze historic resource utilization data relating to a plurality of computing devices, the historic resource utilization data representing a load placed on a set of finite resources over a predefined period of time;

identify and eliminate irrelevant data from the historic resource utilization data;

generate an exponential growth model for each computing device based on analysis of the historic resource utilization data;

generating a linear growth model by adjusting the exponential growth model into a linear model at and beyond a point having a slope that exceeds a threshold multiple of a slope at a point at an end of a most recent portion of the historic resource utilization data in the exponential growth model;

create a resource utilization forecast for each computing device from the exponential growth model or the linear growth model; and

identify an earliest forecasted date a threshold value is exceeded for the particular computing device based on the resource utilization forecast and the threshold value of each computing device;

adjusting workload or capability of the particular computing device based at least on the exponential growth model or the linear growth model.

17. The system of claim 16, wherein the threshold value for the linear growth model is substantially at an 84% confidence interval.

18. The system of claim 16, wherein the threshold value for the linear growth model creates a 1-sigma safety factor.

19. The system of claim 16, wherein identification of the earliest forecasted date includes identification of where the threshold value is exceeded first for the linear growth model and the adjusted exponential growth model.

20. The system of claim 16, further comprising instructions executable to:

determine whether the particular computing device is in need of attention to prevent a failure or performance degradation based on the resource utilization forecast for the particular computing device and the assignment of the threshold value; and

adjust workload or capability of the plurality of computing devices based on a determination that the particular computing device is in need of attention.