公开(公告)号：US20250045641A1

公开(公告)日：2025-02-06

申请号：US18229593

申请日：2023-08-02

Applicant: Adobe Inc.

Inventor： Kanak MAHADIK ,  Jashwant Raj GUNASEKARAN ,  Haoliang WANG ,  Vani NAGARAJAN

IPC: G06N20/20

Abstract: In various examples, a prediction machine learning model determines a set of computing instances capable of executing a machine learning model and a set of batch sizes associated with inferencing requests based on a set of model parameters associated with the machine learning model and a number of floating point operations (FLOPS). In such examples this information is used to update a user interface to indicate computing instances to perform inferencing operations.

公开(公告)号：US20240303569A1

公开(公告)日：2024-09-12

申请号：US18120088

申请日：2023-03-10

Applicant: Adobe Inc.

Inventor： Tong YU ,  Kanak MAHADIK

IPC: G06Q10/0631 ,  G06N20/00

CPC classification number: G06Q10/06316 ,  G06N20/00

Abstract: Embodiments of the present disclosure provide systems, methods, and computer storage media for resource provisioning of microservices using guided order of learning in a reinforcement learning framework. In embodiments, service resource information relating to microservices operating in a computing environment is received and used to perform a similarity analysis to generate similarity scores for each of the services. The service resource information is ordered based on a closeness between the similarity scores of the services. The ordered service resource information is inputted into a reinforcement learning agent to generate a resource configuration determination of at least one service of the services. The resource configuration determination is then provided to a provisioning component associated with the computing environment for provisioning the microservice.

公开(公告)号：US20250037006A1

公开(公告)日：2025-01-30

申请号：US18225970

申请日：2023-07-25

Applicant: ADOBE INC.

Inventor： Kanak MAHADIK ,  Sungchul KIM ,  Ryan ROSSI ,  Handong ZHAO ,  Shravika MITTAL

IPC: G06N20/00

Abstract: In various examples, a ranking is generated for a set of computing instances based on predicted metrics associated with computing instances. For example, a prediction model estimates various system performance metrics based on information associated with a workload and configuration information associated with computing instances. The system performance metrics estimated by the prediction model are used to rank the set of computing instances.

公开(公告)号：US20250007858A1

公开(公告)日：2025-01-02

申请号：US18342516

申请日：2023-06-27

Applicant: ADOBE INC.

Inventor： Kanak MAHADIK ,  Tong YU ,  Junda WU

IPC: H04L47/78 ,  H04L41/16

Abstract: Methods for determining optimal cloud service resource include determining a reward function for a set of resource configurations identifying cloud service resource parameters. The cloud service resource parameters include a source parameter and a target parameter of services to provide a client computing device. A source parameter dataset for the source parameter and a target parameter dataset is generated using the reward function and historical source parameter data. The matrices are then subject to SVD and clustering. A target parameter reward dataset is learned from output of the SVD and clustering. The target parameter dataset is used to determine the parameters for the target parameter for providing corresponding cloud service resources.