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:
May. 12, 2026

Filed:

Feb. 19, 2024
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Fernando Luiz Koch, Palm Beach Gardens, FL (US);

Aladin Djuhera, Dachau, DE;

Alecio Pedro Delazari Binotto, Munich, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 47/783 (2022.01); G06N 3/092 (2023.01); H04L 41/5054 (2022.01);
U.S. Cl.
CPC ...
H04L 47/783 (2013.01); G06N 3/092 (2023.01); H04L 41/5054 (2013.01);
Abstract

A computer implemented method of deployment and resource allocation of microservices of a distributed computing environment is disclosed. The distributed computing environment includes a microservice deployment scheduler and one or more computing nodes. The microservice deployment scheduler includes a reinforcement learning based dynamic workload orchestration module. The method includes receiving microservice constraints descriptive of a microservice computing task by the microservice deployment scheduler. The method further includes receiving node specific properties from the one or more computing nodes by the microservice deployment scheduler. The node specific properties are descriptive of a computing capacity and/or computing capabilities of the one or more computing nodes. The method further includes orchestrating operation of the one or more computing nodes by the microservice deployment scheduler by inputting the microservice constraints and the node specific properties into the reinforcement learning based dynamic workload orchestration module.


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