Estimation of process level energy consumption

Patent metadata

Field	Value
Publication number	US-2025139505-A1
Application number	US-202318499442-A
Country	US
Kind code	A1
Filing date	Nov 1, 2023
Priority date	Nov 1, 2023
Publication date	May 1, 2025
Grant date	—

Abstract

An information handling system stores a batch of energy data, and receives different sets of energy data from different components. The system stores the different sets of energy data as the batch of energy data. The system provides the batch of energy data to an input layer of a machine learning model and executes the machine learning model. Based on the execution of the machine learning model, the system determines energy consumption by the different components.

First claim

What is claimed is:

1 . An information handling system comprising:
a memory to store a batch of energy data for the information handling system; and a processor to communicate with the memory, wherein the processor to:
receive different sets of energy data from different components of the information handling system;
store the different sets of energy data as the batch of energy data in the memory;
provide the batch of energy data to an input layer of a machine learning model;
execute the machine learning model; and
based on the execution of the machine learning model, determine an amount of energy consumption by the different components.

2 . The information handling system of claim 1 , wherein the different sets of energy data correspond to a single process executed across all of the different components.

3 . The information handling system of claim 1 , wherein the processor further to:
execute multiple workloads within the different components; collect a first set of data based on the execution of the multiple workloads; collect a second set of data based on the execution of the multiple workloads; and train the machine learning model with the first and second sets of data.

4 . The information handling system of claim 3 , wherein the processor further to:
generate a matrix using the first and second sets of data; and provide the matrix as training data to the machine learning model.

5 . The information handling system of claim 1 , wherein prior to the batch of energy data being provided to the machine learning model, the processor further to: receive an energy consumption request from a request component.

6 . The information handling system of claim 5 , wherein in response to the amount of energy consumption being determined, the processor further to: provide the amount of energy consumption to the request component.

7 . The information handling system of claim 5 , wherein the request component is located within a cloud server.

8 . The information handling system of claim 1 , wherein the different components includes a network interface card, the memory, and the processor.

9 . A method comprising:
receiving, by an information handling system, different sets of energy data from different components of the information handling system; storing the different sets of energy data as a batch of energy data in the information handling system; providing the batch of energy data to an input layer of a machine learning model; executing the machine learning model; and based on the executing of the machine learning model, determining an amount of energy consumption by the different components.

10 . The method of claim 9 , wherein the different sets of energy data correspond to a single process executed across all of the different components.

11 . The method of claim 9 , wherein the method further comprises:
executing multiple workloads within the different components; collecting a first set of data based on the execution of the multiple workloads; collecting a second set of data based on the execution of the multiple workloads; and training the machine learning model with the first and second sets of data.

12 . The method of claim 11 , wherein the method further comprises:
generating a matrix using the first and second sets of data; and providing the matrix as training data to the machine learning model.

13 . The method of claim 9 , wherein prior to the providing of the batch of energy data to the machine learning model, the method further comprises receiving an energy consumption request from a request component.

14 . The method of claim 13 , wherein in response to the amount of energy consumption being determined, the method further comprises providing the amount of energy consumption to the request component.

15 . The method of claim 13 , wherein the request component is located within a cloud server.

16 . The method of claim 9 , wherein the different components include a network interface card, a memory, and a processor.

17 . A method comprising:
executing multiple workloads within different components of an information handling system; collecting a first set of data based on the execution of the multiple workloads; collecting a second set of data based on the execution of the multiple workloads; training a machine learning model with the first and second sets of data; storing different sets of energy data from the different components as a batch of energy data; receiving an energy consumption request from a request component; in response to the energy consumption request, providing the batch of energy data to an input layer of the machine learning model; executing the machine learning model; and based on the executing of the machine learning model, determining an amount of energy consumption by the different components.

18 . The method of claim 17 , wherein the different sets of energy data correspond to a single process executed across all of the different components.

19 . The method of claim 17 , wherein the method further comprises:
generating a matrix using the first and second sets of data; and providing the matrix as training data to the machine learning model.

20 . The method of claim 17 , wherein in response to the amount of energy consumption being determined, the method further comprises providing the amount of energy consumption to the request component.

Assignees

• Dell Products Lp

Inventors

• Vichare Nikhil
• Khosrowpour Farzad

Classifications

• G06N20/00 Primary

  Machine learning · CPC title