Firmware-based SSD block failure prediction and avoidance scheme

    公开(公告)号:US11567670B2

    公开(公告)日:2023-01-31

    申请号:US16701133

    申请日:2019-12-02

    Abstract: A Solid State Drive (SSD) is disclosed. The SSD may comprise flash storage for data, the flash storage organized into a plurality of blocks. A controller may manage reading data from and writing data to the flash storage. Metadata storage may store device-based log data for errors in the SSD. Identification firmware may identify a block responsive to the device-based log data. In some embodiments of the inventive concept, verification firmware may determine whether the suspect block is predicted to fail responsive to both precise block-based data and the device-based log data.

    Multi-non-volatile memory solid state drive block-level failure prediction with separate log per non-volatile memory

    公开(公告)号:US11500752B2

    公开(公告)日:2022-11-15

    申请号:US17093620

    申请日:2020-11-09

    Abstract: A storage device is disclosed. A first storage media may store data. The first storage media may be of a first storage type and may be organized into at least two blocks. A second storage media may also store data. The second storage media may be of a second storage type different from the first type, and may also be organized into at least two blocks. A controller may manage reading data from and writing data to the first storage media and the second storage media. Metadata storage may store device-based log data for errors in the storage device. The drive-based log data may include a first log data for the first storage media and a second log data for the second storage media. An identification circuit may identify a suspect block in the at least two blocks in the first storage media and the second storage media, responsive to the device-based log data.

    SYSTEM AND METHOD FOR EFFICIENTLY CONVERTING LOW-LOCALITY DATA INTO HIGH-LOCALITY DATA

    公开(公告)号:US20210255792A1

    公开(公告)日:2021-08-19

    申请号:US16795510

    申请日:2020-02-19

    Abstract: A system and method for processing unstructured source data is described. Input data having a range of V is loaded from off-chip storage to on-chip storage. The input data is partitioned into P temporary parent partitions via the on-chip storage, where a particular one of the P temporary parent partitions has a range of V/P. The P temporary parent partitions are stored from the on-chip storage to the off-chip storage. The P temporary parent partitions are partitioned for generating P temporary child partitions until the target number of T partitions is generated, where data from of the P temporary parent partitions is source data for recursively loading, partitioning, and storing the source data. An application is configured to access partitioned data from the T partitions for generating an output. The accesses of the partitioned data are sequential read accesses of the off-chip storage.

    System and method for LBA-based RAID

    公开(公告)号:US10684956B2

    公开(公告)日:2020-06-16

    申请号:US15949943

    申请日:2018-04-10

    Abstract: A system and method for an LBA RAID storage device. The LBA RAID storage device includes a plurality of data channels and a plurality of storage components. Each of the storage components is connected to one of the plurality of data channels. A storage controller is configured to receive a data and write the data to a RAID group made up of at least two storage components of the plurality of storage components that are each connected to a separate data channel.

    Systems and methods for predicting storage device failure using machine learning

    公开(公告)号:US12260347B2

    公开(公告)日:2025-03-25

    申请号:US18197717

    申请日:2023-05-15

    Abstract: A method for predicting a time-to-failure of a target storage device may include training a machine learning scheme with a time-series dataset, and applying the telemetry data from the target storage device to the machine learning scheme which may output a time-window based time-to-failure prediction. A method for training a machine learning scheme for predicting a time-to-failure of a storage device may include applying a data quality improvement framework to a time-series dataset of operational and failure data from multiple storage devices, and training the scheme with the pre-processed dataset. A method for training a machine learning scheme for predicting a time-to-failure of a storage device may include training the scheme with a first portion of a time-series dataset of operational and failure data from multiple storage devices, testing the machine learning scheme with a second portion of the time-series dataset, and evaluating the machine learning scheme.

    Systems, methods, and devices for data propagation in graph processing

    公开(公告)号:US12073256B2

    公开(公告)日:2024-08-27

    申请号:US17170881

    申请日:2021-02-08

    CPC classification number: G06F9/5061 G06F9/3885 G06F9/5016 G06F16/9024

    Abstract: A method of partitioning a graph for processing may include sorting two or more vertices of the graph based on incoming edges and outgoing edges, placing a first one of the vertices with fewer incoming edges in a first partition, and placing a second one of the vertices with fewer outgoing edges in a second partition. The first one of the vertices may have a lowest number of incoming edges, and the first one of the vertices may be placed in a first available partition. The second one of the vertices may have a lowest number of outgoing edges, and the second one of the vertices may be placed in a second available partition. A method for updating vertices of a graph may include storing a first update in a first buffer, storing a second update in a second buffer, and transferring the first and second updates to a memory using different threads.

    Systems, methods, and devices for data propagation in graph processing

    公开(公告)号:US12020079B2

    公开(公告)日:2024-06-25

    申请号:US17170881

    申请日:2021-02-08

    CPC classification number: G06F9/5061 G06F9/3885 G06F9/5016 G06F16/9024

    Abstract: A method of partitioning a graph for processing may include sorting two or more vertices of the graph based on incoming edges and outgoing edges, placing a first one of the vertices with fewer incoming edges in a first partition, and placing a second one of the vertices with fewer outgoing edges in a second partition. The first one of the vertices may have a lowest number of incoming edges, and the first one of the vertices may be placed in a first available partition. The second one of the vertices may have a lowest number of outgoing edges, and the second one of the vertices may be placed in a second available partition. A method for updating vertices of a graph may include storing a first update in a first buffer, storing a second update in a second buffer, and transferring the first and second updates to a memory using different threads.

    Storage device block-level failure prediction-based data placement

    公开(公告)号:US11734093B2

    公开(公告)日:2023-08-22

    申请号:US17009684

    申请日:2020-09-01

    Abstract: In a method for data placement in a storage device including one or more blocks and a controller, the method including: receiving, by the controller of the storage device, a request to write data; determining, by the controller, a data status of the data; calculating, by the controller, one or more vulnerability factors of the one or more blocks; determining, by the controller, one or more block statuses of the one or more blocks based on the one or more vulnerability factors; selecting, by the controller, a target block from the one or more blocks based on the data status and the one or more block statuses; and writing, by the controller, the data to the target block.

    Multi-non-volatile memory solid state drive block-level failure prediction with unified device log

    公开(公告)号:US11500753B2

    公开(公告)日:2022-11-15

    申请号:US17093626

    申请日:2020-11-09

    Abstract: A storage device is disclosed. A first storage media may store data. The first storage media may be of a first storage type, and may be organized into at least two blocks. A second storage media may also store data. The second storage media may be of a second storage type different from the first type, and may also be organized into at least two blocks. A controller may manage reading data from and writing data to the first storage media and the second storage media. Metadata storage may store device-based log data for errors in the storage device. The drive-based log data for errors may include a unified log data for the first storage media and the second storage media. An identification circuit may identify a suspect block in the at least two blocks in the first storage media and the second storage media, responsive to the unified log data. Parameters for the first storage media and the second storage media may be derived from a unified parameter in the unified log data in proportion to the number of write operations and the number of read operations to each storage media, relative to the number of write operations and the number of read operations of the storage device.

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