公开(公告)号：US20240168674A1

公开(公告)日：2024-05-23

申请号：US18127922

申请日：2023-03-29

Applicant: Samsung Electronics Co., Ltd.

Inventor： Kibeen JUNG ,  Han Kyoo LEE ,  Byeonghui KIM ,  Hyunkyo OH ,  Sungmin JANG

IPC: G06F3/06

CPC classification number: G06F3/0653 ,  G06F3/061 ,  G06F3/0656 ,  G06F3/0679

Abstract: A throttling method for a storage device is provided. The throttling method includes: receiving a write command from a host; identifying, using a first machine learning model, a throttling delay time; transmitting a completion message to the host according to the throttling delay time; collecting weights of the first machine learning model and performance information of the storage device corresponding to the weights; learning the weights and the performance information to generate an objective function indicating a relationship between the weights and the performance information using a second machine learning model of a weight learning device; selecting a weight corresponding to a maximum performance using the objective function; and updating the first machine learning model with the weight.

公开(公告)号：US20230214258A1

公开(公告)日：2023-07-06

申请号：US17949583

申请日：2022-09-21

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Chan Ha KIM ,  Han Kyoo LEE ,  Gyeong Min NAM ,  Seung Ryong JANG

IPC: G06F9/48 ,  G06F9/54 ,  G06K9/62 ,  G06N20/00

CPC classification number: G06F9/4881 ,  G06F9/546 ,  G06K9/6262 ,  G06N20/00

Abstract: A storage controller, including a processor configured to perform a plurality of tasks; and a scheduling module configured to schedule the plurality of tasks through reinforcement learning, and provide a scheduling result to the processor, wherein the scheduling module includes: a resource analysis module configured to analyze a usage history and a usage status of a resource; an access pattern analysis module configured to analyze an access pattern; a queue analysis module configured to analyze information included in a queue; and a performance analysis module configured to analyze a task execution result, wherein the scheduling module is further configured to perform the reinforcement learning using state information and reward information, and wherein the state information and the reward information are determined based on the performing of the plurality of tasks.