Application profiling via loopback methods

Patent metadata

Field	Value
Publication number	US-10853093-B2
Application number	US-201715719789-A
Country	US
Kind code	B2
Filing date	Sep 29, 2017
Priority date	Sep 29, 2017
Publication date	Dec 1, 2020
Grant date	Dec 1, 2020

Abstract

A system, method, and computer-readable medium are disclosed for performing a dynamic application optimization operation, comprising: instrumenting a plurality of system parameters of a client information handling system for monitoring; instructing a user to execute a particular application on the client information handling system; obtaining a plurality of samples of the plurality of system parameters; performing a machine learning operation using the plurality of samples of the plurality of system parameters, the machine learning operation training a machine learning model to generate a profile for the particular application and an operating mode of the particular application; applying the profile to the client information handling system to provide a new information handling system configuration, the new information handling system configuration optimizing the information handling system for the particular application.

First claim

What is claimed is:

1. A computer-implementable method for performing a dynamic application optimization operation, comprising:
instrumenting a plurality of system parameters of a client information handling system for monitoring;
instructing a user to execute a particular application on the client information handling system;
obtaining a plurality of samples of the plurality of system parameters;
performing a machine learning operation using the plurality of samples of the plurality of system parameters, the machine learning operation training a machine learning model to generate a profile for the particular application and an operating mode of the particular application;
applying the profile to the client information handling system to provide a new information handling system configuration, the new information handling system configuration optimizing the information handling system for the particular application, the dynamic application optimization operation optimizing the client information handling system for the particular application based on application characteristics on the client information handling system to provide increased performance of the particular application to the user of the client information handling system.

2. The method of claim 1 , wherein:
the instrumentation provides sufficient samples to analyze how the application behaves to the user preferences.

3. The method of claim 1 , wherein:
the machine learning model takes into account user preferences when executing the particular application.

4. The method of claim 1 , further comprising:
instructing the user to execute the particular application with the new system configuration to confirm the new system configuration achieves improved performance for the particular application in a particular mode of operation of the application.

5. The method of claim 1 , wherein:
the machine learning model performs at least one of a classification tree (ctree) operation, a logistic regression operation, a random forest operation, an adaptive boosting (adaboost) operation and a support vector machine (SVM) operation.

6. The method of claim 1 , wherein:
the plurality of system parameters comprise a CPU utilization parameter, a number of threads parameter, a utilization by thread parameter, a processor queue length parameter, a cpu frequency parameter, a turbo residency parameter, a memory utilization parameter, a paging activity parameter, a disk read/writes parameter, a graphics processor unit (gpu) utilization parameter, a gpu memory consumption parameter.

7. A system comprising:
a processor;
a data bus coupled to the processor; and
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for:
instrumenting a plurality of system parameters of a client information handling system for monitoring;
instructing a user to execute a particular application on the client information handling system;
obtaining a plurality of samples of the plurality of system parameters;
performing a machine learning operation using the plurality of samples of the plurality of system parameters, the machine learning operation training a machine learning model to generate a profile for the particular application and an operating mode of the particular application;
applying the profile to the client information handling system to provide a new information handling system configuration, the new information handling system configuration optimizing the information handling system for the particular application, the dynamic application optimization operation optimizing the client information handling system for the particular application based on application characteristics on the client information handling system to provide increased performance of the particular application to the user of the client information handling system.

8. The system of claim 7 , wherein:
the instrumentation provides sufficient samples to analyze how the application behaves to the user preferences.

9. The system of claim 7 , wherein:
the machine learning model takes into account user preferences when executing the particular application.

10. The system of claim 7 , wherein the instructions executable by the processor are further configured for:
instructing the user to execute the particular application with the new system configuration to confirm the new system configuration achieves improved performance for the particular application in a particular mode of operation of the application.

11. The system of claim 7 , wherein:
the machine learning model performs at least one of a classification tree (ctree) operation, a logistic regression operation, a random forest operation, an adaptive boosting (adaboost) operation and a support vector machine (SVM) operation.

12. The system of claim 7 , wherein:
the plurality of system parameters comprise a CPU utilization parameter, a number of threads parameter, a utilization by thread parameter, a processor queue length parameter, a cpu frequency parameter, a turbo residency parameter, a memory utilization parameter, a paging activity parameter, a disk read/writes parameter, a graphics processor unit (gpu) utilization parameter, a gpu memory consumption parameter.

13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
instrumenting a plurality of system parameters of a client information handling system for monitoring;
instructing a user to execute a particular application on the client information handling system;
obtaining a plurality of samples of the plurality of system parameters;
performing a machine learning operation using the plurality of samples of the plurality of system parameters, the machine learning operation training a machine learning model to generate a profile for the particular application and an operating mode of the particular application;
applying the profile to the client information handling system to provide a new information handling system configuration, the new information handling system configuration optimizing the information handling system for the particular application, the dynamic application optimization operation optimizing the client information handling system for the particular application based on application characteristics on the client information handling system to provide increased performance of the particular application to the user of the client information handling system.

14. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the instrumentation provides sufficient samples to analyze how the application behaves to the user preferences.

15. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the machine learning model takes into account user preferences when executing the particular application.

16. The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are further configured for:
instructing the user to execute the particular application with the new system configuration to confirm the new system configuration achieves improved performance for the particular application in a particular mode of operation of the application.

17. The non-transitory, computer-re

Assignees

• Dell Products Lp

Inventors

• Khosrowpour Farzad
• Vichare Nikhil

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N20/20

  Ensemble learning · CPC title

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title

• G06F11/34

  Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation {; Recording or statistical evaluation of user activity, e.g. usability assessment} · CPC title

• G06F11/3442

  for planning or managing the needed capacity · CPC title