US 12,470,465 B2
Continuously improving API service endpoint selections via adaptive reinforcement learning
Rong Nickle Chang, Pleasantville, NY (US); Hongyi Bian, Ames, IA (US); and Nitin Gaur, Round Rock, TX (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Oct. 4, 2022, as Appl. No. 17/959,665.
Prior Publication US 2024/0113945 A1, Apr. 4, 2024
Int. Cl. H04L 41/16 (2022.01); H04L 41/0246 (2022.01); H04L 43/55 (2022.01)
CPC H04L 41/16 (2013.01) [H04L 41/0246 (2013.01); H04L 43/55 (2022.05)] 16 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, by a processor set, a request for a web-based service;
generating, by the processor set, a feature vector including values based on parameters of the request;
generating, by the processor set, an endpoint selection vector including plural probabilities corresponding to plural endpoints, wherein the endpoint selection vector is generated using the feature vector with a machine learning model;
selecting, by the processor set, one of the plural endpoints based on a random number and the plural probabilities;
invoking, by the processor set, the selected endpoint;
in response to the selecting one of the plural endpoints, providing data defining the selected one of the plural endpoints to an endpoint selection logger; and
in response to the invoking the selected endpoint, providing quality-of-experience data to the endpoint selection logger, wherein the quality-of-experience data comprises data that quantifies a quality-of-experience associated with the selected endpoint handling the request.