STORAGE DEVICE CONFIGURED TO SUPPORT MULTI-STREAMS AND OPERATION METHOD THEREOF

    公开(公告)号:US20210200477A1

    公开(公告)日:2021-07-01

    申请号:US17136818

    申请日:2020-12-29

    Abstract: A storage device is configured to manage a plurality of nonvolatile memories with a plurality of physical streams. An operation method of the storage device includes receiving an input/output request from an external host device, determining a 0-th virtual stream identifier, extracting a 0-th representative value from a 0-th virtual stream feature, extracting a first and second representative values corresponding to first and second physical streams , calculating distance information including first and second similarities between the 0-th virtual stream and each of the first and second physical streams, based on the extracted representative values, assigning one of the plurality of physical streams to the 0-th virtual stream, based on the distance information, and performing an operation corresponding to the input/output request, at the assigned physical stream, and the extracting and the calculating are performed by using machine learning model.

    STORAGE DEVICE, STORAGE SYSTEM AND THROTTLING METHOD THEREOF

    公开(公告)号:US20240168674A1

    公开(公告)日:2024-05-23

    申请号:US18127922

    申请日:2023-03-29

    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.

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