Adaptive resource allocation system and method for a target application executed in an information handling system (ihs)

Patent metadata

Field	Value
Publication number	US-2022179706-A1
Application number	US-202017114164-A
Country	US
Kind code	A1
Filing date	Dec 7, 2020
Priority date	Dec 7, 2020
Publication date	Jun 9, 2022
Grant date	—

Abstract

Embodiments of systems and methods for managing performance optimization of applications executed by an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, an IHS may include computer-executable instructions for determining one or more resource performance features of a resource used by the IHS using a first machine learning (ML) service, and determining one or more application performance features of a target application executed by the resource using a second ML service. Using the determined resource performance features and the application performance features, the instructions may generate a profile recommendation for the target application, and adjust one or more settings of the resource to optimize a performance of the target application executed by the resource.

First claim

1 . An Information Handling System (IHS), comprising:
at least one processor; and at least one memory coupled to the at least one processor, the at least one memory having program instructions stored thereon that, upon execution by the at least one processor, cause the IHS to:
determine one or more resource performance features of a resource used by the IHS using a first machine learning (ML) service;
determine one or more application performance features of a target application executed by the resource using a second ML service;
generate a profile recommendation for the target application according to the determined resource performance features and the application performance features; and
adjust one or more settings of the resource to optimize a performance of the target application executed by the resource.

2 . The IHS of claim 1 , wherein the instructions are further executed to generate the profile recommendation by combining the resource performance features with the application performance features using a ML hinting technique.

3 . The IHS of claim 1 , wherein the instructions are further executed to execute the first ML service separately and distinctly from how the second ML service is executed.

4 . The IHS of claim 1 , wherein the instructions are further executed to adjust the settings of the resource only when a loading of the resource exceeds a specified threshold level.

5 . The IHS of claim 1 , wherein the resource comprises the processor, and wherein the instructions are further executed to optimize the target application by adjusting at least one of a power operating level of the processor, a level of overclocking of the processor, or a level of underclocking of the processor.

6 . The IHS of claim 1 , wherein the resource comprises a graphics processing unit (GPU) of the IHS, and wherein the instructions are further executed to optimize the target application by adjusting at least one of a frame rate, a refresh rate, or a computational frame rate of the GPU.

7 . The IHS of claim 1 , wherein the resource comprises a storage device of the IHS, and wherein the instructions are further executed to optimize the target application by adjusting at least one of a write optimized setting, a read optimized setting, or a cache level of the storage device.

8 . The IHS of claim 1 , wherein the resource performance feature comprises one or more other applications that affect the loading of the resource, wherein the instructions are further executed to optimize the target application by adjusting a priority of the other applications executed on the resource.

9 . The IHS of claim 1 , wherein the application performance feature comprises detecting a particular operation performed by the target application, wherein the instructions are further executed to optimize the target application by adjusting a setting of the resource according to the detected operation.

10 . The IHS of claim 1 , wherein the application performance feature comprises a location of the IHS, wherein the instructions are further executed to optimize the target application by adjusting a setting of the resource according to the location of the IHS.

11 . A method comprising:
determining, using instructions stored in at least one memory and executed by at least one processor, one or more resource performance features of a resource used by the IHS using a first machine learning (ML) service; determining, using the instructions, one or more application performance features of a target application executed by the resource using a second ML service; generating, using the instructions, a profile recommendation for the target application according to the determined resource performance features and the application performance features; and adjusting, using the instructions, one or more settings of the resource to optimize a performance of the target application executed by the resource.

12 . The method of claim 11 , further comprising generating the profile recommendation by combining the resource performance features with the application performance features using a ML hinting technique.

13 . The method of claim 11 , further comprising executing the first ML service separately and distinctly from how the second ML service is executed.

14 . The method of claim 11 , further comprising adjusting the settings of the resource only when a loading of the resource exceeds a specified threshold level.

15 . The method of claim 11 , further comprising optimizing the target application by adjusting at least one of a power operating level of the processor, a level of overclocking of the processor, or a level of underclocking of the processor, wherein the resource comprises the processor.

6 . The method of claim 11 , further comprising optimizing the target application by adjusting at least one of a frame rate, a refresh rate, or a computational frame rate of the GPU, wherein the resource comprises a graphics processing unit (GPU) of the IHS.

17 . The method of claim 11 , further comprising optimizing the target application by adjusting at least one of a write optimized setting, a read optimized setting, or a cache level of the storage device, wherein the resource comprises a storage device of the IHS.

18 . The method of claim 11 , further comprising optimizing the target application by adjusting a priority of the other applications executed on the resource, wherein the resource performance feature comprises one or more other applications that affect the loading of the resource.

19 . The method of claim 11 , further comprising optimizing the target application by adjusting a setting of the resource according to the detected operation, wherein the application performance feature comprises detecting a particular operation performed by the target application.

20 . 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:
determine one or more resource performance features of a resource used by the IHS using a first machine learning (ML) service; determine one or more application performance features of a target application executed by the resource using a second ML service; generate a profile recommendation for the target application according to the determined resource performance features and the application performance features; and adjust one or more settings of the resource to optimize a performance of the target application executed by the resource.

Assignees

• Dell Products Lp

Inventors

• Khosrowpour Farzad
• Jasleen Fnu

Classifications

• G06F2209/508

  Monitor · CPC title

• G06F9/5005

  to service a request · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F11/3409

  for performance assessment · CPC title

• G06F11/302 Primary

  where the computing system component is a software system · CPC title