US 12,294,529 B2
Transferable clustering of contextual bandits for cloud service resource allocation
Kanak Mahadik, San Jose, CA (US); Tong Yu, San Jose, CA (US); and Junda Wu, San Jose, CA (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by ADOBE INC., San Jose, CA (US)
Filed on Jun. 27, 2023, as Appl. No. 18/342,516.
Prior Publication US 2025/0007858 A1, Jan. 2, 2025
Int. Cl. H04L 41/16 (2022.01); H04L 47/78 (2022.01)
CPC H04L 47/781 (2013.01) [H04L 41/16 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for cloud service resource allocation, the system comprising:
at least one processor; and
one or more computer storage media storing computer readable instructions thereon that when executed by the at least one processor cause the at least one processor to perform operations comprising:
determining a reward function for a set of resource configurations identifying cloud service resource parameters, wherein the cloud service resource parameters include a source parameter and a target parameter of services to provide a client computing device;
generating a source parameter dataset for the source parameter using the reward function and historical source parameter data, the source parameter dataset corresponding to a source domain;
learning, from the source parameter dataset, a target parameter reward dataset for the target parameter, the target parameter reward dataset corresponding to a target domain different from the source domain, and each of the source domain and the target domain corresponding to different resource device types, whereby knowledge is transferred from the source domain to the different target domain by the learning; and
providing a cloud service resource corresponding to the target parameter derived from the target parameter reward dataset.