Performance optimization of object grouping schema in a network key-value storage device using adaptive regression

    公开(公告)号:US11972361B2

    公开(公告)日:2024-04-30

    申请号:US16817460

    申请日:2020-03-12

    CPC classification number: G06N5/04 G06F16/2474 G06N20/00

    Abstract: Provided is a method including receiving object IOs for a target device, grouping the object IOs using a first plurality of input parameters, associating a tracking parameter with the first plurality of input parameters and a performance parameter corresponding to the first plurality of input parameters, storing a first data entry including the tracking parameter, the first plurality of input parameters, and the performance parameter in a database, extracting a plurality of data entries from the database, the plurality of data entries including the first data entry, training a training model using one or more of the plurality of data entries, cross-validating the training model to determine a degree of error reduction of the training model, performing a model check to compare the training model to an inferencing model, and updating the inferencing model based on the model check.

    PERFORMANCE OPTIMIZATION OF OBJECT GROUPING SCHEMA IN A NETWORK KEY-VALUE STORAGE DEVICE USING ADAPTIVE REGRESSION

    公开(公告)号:US20210232946A1

    公开(公告)日:2021-07-29

    申请号:US16817460

    申请日:2020-03-12

    Abstract: Provided is a method including receiving object IOs for a target device, grouping the object IOs using a first plurality of input parameters, associating a tracking parameter with the first plurality of input parameters and a performance parameter corresponding to the first plurality of input parameters, storing a first data entry including the tracking parameter, the first plurality of input parameters, and the performance parameter in a database, extracting a plurality of data entries from the database, the plurality of data entries including the first data entry, training a training model using one or more of the plurality of data entries, cross-validating the training model to determine a degree of error reduction of the training model, performing a model check to compare the training model to an inferencing model, and updating the inferencing model based on the model check.

    Grouping key value object IOs to improve IO performance for key-value storage devices

    公开(公告)号:US11243694B2

    公开(公告)日:2022-02-08

    申请号:US16815974

    申请日:2020-03-11

    Abstract: Provided is a method of completing object IOs, the method including receiving a first set of object IOs for a target storage device, dispatching the first set of object IOs to a first buffer of a first zone, the first buffer being configured to function as a first log buffer, concatenating the first set of object IOs to form a first object group in the first buffer, logging the first object group to a log device, modifying a function of the first buffer from the first log buffer to a first flush buffer, and transferring the first object group to the target storage device.

    Automatic stream detection and assignment algorithm

    公开(公告)号:US10656838B2

    公开(公告)日:2020-05-19

    申请号:US15499877

    申请日:2017-04-27

    Abstract: A Solid State Drive (SSD) is disclosed. The SSD may include flash memory to store data and may support a plurality of device streams. A SSD controller may manage reading and writing data to the flash memory, and may store a submission queue and a chunk-to-stream mapper. A flash translation layer may include a receiver to receive a write command, an LBA mapper to map an LBA to a chunk identifier (ID), stream selection logic to select a stream ID based on the chunk ID, a stream ID adder to add the stream ID to the write command, a queuer to place the chunk ID in the submission queue, and background logic to update the chunk-to-stream mapper after the chunk ID is removed from the submission queue.

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