The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 13, 2024

Filed:

Feb. 23, 2022
Applicant:

Sedai Inc., Pleasanton, CA (US);

Inventors:

Suresh Mathew, San Ramon, CA (US);

Nikhil Gopinath Kurup, Tampa, FL (US);

Hari Chandrasekhar, Highlands Ranch, CO (US);

Benjamin Thomas, San Jose, CA (US);

Assignee:

SEDAI, INC., Pleasanton, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 9/50 (2006.01); G06N 3/08 (2023.01); G06F 11/07 (2006.01); G06F 8/71 (2018.01); G08B 21/18 (2006.01); H04L 43/16 (2022.01); H04L 67/10 (2022.01); H04L 67/00 (2022.01); G06F 11/30 (2006.01); G06F 11/34 (2006.01);
U.S. Cl.
CPC ...
G06F 9/5016 (2013.01); G06F 8/71 (2013.01); G06F 9/5094 (2013.01); G06F 11/079 (2013.01); G06F 11/0721 (2013.01); G06F 11/0769 (2013.01); G06F 11/3006 (2013.01); G06F 11/34 (2013.01); G06F 11/3452 (2013.01); G06N 3/08 (2013.01); G08B 21/182 (2013.01); H04L 43/16 (2013.01); H04L 67/10 (2013.01); H04L 67/34 (2013.01); G06F 2209/501 (2013.01);
Abstract

Implementations described herein relate to methods, systems, and computer-readable media to manage a computing resource allocation for a software application. In some implementations, a method may include executing a first test function using the distributed computing system at a first plurality of allocation setpoints for the computing resource, based on the execution, obtaining one or more performance metrics for the first test function for each setpoint of the first plurality of allocation setpoints, training a machine learning model based on the obtained one or more performance metrics; and utilizing the trained machine learning model to manage the computing resource for a second function.


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