Systems and Methods For Measuring the Effectiveness of a Workload Predictor on a Mobile Device

• US 20120079357A1
• Filed: 09/28/2010
• Published: 03/29/2012
• Est. Priority Date: 09/28/2010
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Salsbery, Brian J., Gargash, Norman S.
• Granted Patent: US 8,478,567 B2
• Time in Patent Office: Days
• US Class Current: 714/799
• CPC Class Codes: H04W 24/08 Testing, supervising or monitoring using real traffic
• CPC Class Codes: H04W 52/0225 using monitoring of external events, e.g. the presence of a signal
• CPC Class Codes: Y02D 30/70 in wireless communication networks

Abstract

Systems and methods for measuring the effectiveness of a workload predictor operative on a mobile device are disclosed. A load manager includes a workload predictor, a sensor, an error generator and a controller. The workload predictor generates an estimate of the workload on a processor core operative on the mobile device. The sensor generates a measure of the actual workload on the processor core. The error generator receives the estimate of the workload and the measure of the actual workload on the processor core and generates an error signal. The controller receives the error signal and determines the effectiveness of the workload predictor as a function of the error signal over time.

Claims

1. A method for measuring the effectiveness of a workload predictor on a mobile device, the method comprising:

generating a real-time measure of the workload on the mobile device;

using a workload predictor to generate an estimate of the workload on the mobile device;

comparing the real-time measure of the workload to the estimate of the workload on the mobile device to generate an error signal;

recording the error signal over time; and

generating a function of the error signal as a measure of the effectiveness of the workload predictor.

2. The method of claim 1, wherein using a function of the error signal comprises calculating an integral of the square of the error signal over a select period.

3. The method of claim 1, further comprising:

applying the error signal to a controller that adjusts at least one parameter in response to the error signal.

4. The method of claim 1, further comprising:

associating the error signal with the combination of an active performance scaling algorithm and at least one parameter.

5. The method of claim 1, further comprising:

using a measure of cross-correlation of the error signal to determine if a present operating state of the mobile device is similar to a known use case.

6. The method of claim 5, further comprising:

applying a combination of a performance scaling algorithm and one or more parameters when the mobile device is so configured.

7. The method of claim 1, wherein the mobile device comprises:

one of a cellular telephone, a pager, a personal digital assistant, a reader, a satellite phone, a navigation device, or a computer with a wireless connection.

8. A load manager for execution on a mobile device, comprising:

a workload predictor configured to generate an estimate of the workload on a processor core operative on the mobile device;

a sensor configured to generate a measure of the workload on the processor core operative on the mobile device;

an error generator configured to receive the estimate of the workload and the measure of the workload and generate an error signal; and

a controller arranged to receive the error signal and adjust operation of the processor core.

9. The load manager of claim 8, wherein the workload predictor is responsive to a performance scaling algorithm.

10. The load manager of claim 8, wherein the magnitude of the square of the error signal is used to determine the accuracy of the workload predictor to predict the workload.

11. The load manager of claim 8, further comprising:

a time sequence store responsive to the error signal.

12. The load manager of claim 11, further comprising:

a cross-correlator coupled to the time sequence store, the cross-correlator configured to generate a measure of similarity of the error signal to an error signal associated with an identified use of the mobile device.

13. A system for managing a load on a mobile device, the system comprising:

means for measuring a workload of a processor core operative on the mobile device;

means for generating an estimate of the workload of the processor core operative on the mobile device;

means for generating an error signal responsive to the workload and the estimate of the workload;

means for storing the error signal over time;

means for associating a stored error signal over time with a present state of use of the mobile device; and

means for selectively adjusting the mobile device in response to the stored error signal over time.

14. The system of claim 13, wherein the means for generating an estimate of the workload comprises a workload predictor.

15. The system of claim 13, wherein the means for selectively adjusting is responsive to a cross-correlator and a detector.

16. A computer program product comprising a computer-usable medium having a computer-readable program code embodied therein, said computer-readable program code adapted to be executed to implement a method for identifying the effectiveness of a workload predictor operative on a mobile device, the computer program product comprising:

logic configured to generate a real-time measure of the workload on the mobile device;

logic configured to generate an estimate of the workload on the mobile device;

logic configured to compare the real-time measure of the workload to the estimate of the workload on the mobile device to generate an error signal;

logic configured to record the error signal over time;

logic configured to calculate a function of the error signal; and

logic configured to determine the effectiveness of the workload predictor in response to the function of the error signal.

17. The system of claim 16, further comprising:

logic configured to adjust at least one parameter in response to the error signal.

18. The system of claim 16, further comprising:

logic configured to associate the error signal with a combination of a performance scaling algorithm and at least one parameter.

19. The system of claim 16, further comprising:

logic configured to generate a measure of cross-correlation to determine if a present operating state of the mobile device is similar to a known use case for which an appropriate combination of a performance scaling algorithm and one or more parameters has been defined.

20. The system of claim 19, further comprising:

logic configured to store an indication of the most similar combination of a performance scaling algorithm and one or more parameters; and

logic configured to apply the combination when the mobile device is so configured.