Two-level indexing for key-value persistent storage device

    公开(公告)号:US11954345B2

    公开(公告)日:2024-04-09

    申请号:US17668312

    申请日:2022-02-09

    CPC classification number: G06F3/0638 G06F3/0604 G06F3/0679

    Abstract: A system and method for two-level indexing for key-value persistent storage. The method may include: sorting two or more key-value pairs to form a sorted key-value pair set; determining an address of a first key-value pair of the key-value pairs, the first key-value pair including a first key and a first value; determining an address of a second key-value pair of the key-value pairs, the second key-value pair including a second key and a second value; and training a first linear regression model to generate a first line corresponding to the key-value pairs, the training including training the first linear regression model with key-value pairs including the first key-value pair and the second key-value pair.

    SYSTEMS AND METHODS FOR AUTOMAPPING SOURCE CODE TO MACHINE CODE

    公开(公告)号:US20230176842A1

    公开(公告)日:2023-06-08

    申请号:US17674739

    申请日:2022-02-17

    CPC classification number: G06F8/48

    Abstract: Systems and methods for mapping a location in a source code to a location in a machine code are disclosed. An identifier of the source code and an identifier of a first location of the source code are received. A marker is inserted in association with the source code based on the identifier of the first location. The source code is compiled into machine code. A second location associated with the marker is identified in the machine code. A third location is returned based on identifying the second location.

    Systems and methods for predicting storage device failure using machine learning

    公开(公告)号:US11657300B2

    公开(公告)日:2023-05-23

    申请号:US15931573

    申请日:2020-05-13

    CPC classification number: G06N5/04 G06F11/16 G06N20/00

    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.

    System and method for managing conversion of low-locality data into high-locality data

    公开(公告)号:US11429299B2

    公开(公告)日:2022-08-30

    申请号:US16942442

    申请日:2020-07-29

    Abstract: A system and method for processing source data are disclosed. A first node receives from a second node, a request for the source data. The first node generates and returns, in response to the request, an output partition of the source data. The generating and returning of the output partition include generating a first child partition from parent input data, and generating and returning, as the output partition, a first grandchild partition from the first child partition. The first node also generates a second child partition from the parent input data.

    Binding application to namespace (NS) to set to submission queue (SQ) and assigning performance service level agreement (SLA) and passing it to a storage device

    公开(公告)号:US11409439B2

    公开(公告)日:2022-08-09

    申请号:US17189255

    申请日:2021-03-01

    Abstract: A host interface layer in a storage device is described. The host interface layer may include an arbitrator to select a first submission queue (SQ) from a set including at least the first SQ and a second SQ. The first SQ may be associated with a first Quality of Service (QoS) level, and the second SQ may be associated with a second QoS level. A command fetcher may retrieve an input/output (I/O) request from the first SQ. A command parser may place the I/O request in a first command queue from a set including at least the first command queue and a second command queue. The arbitrator may be configured to select the first SQ based at least in part on a first weight associated with the first SQ and a second weight associated with the second SQ. The first weight may be based at least in part on a first total storage capacity of at least one first namespace (NS) associated with the first QoS level, and the second weight may be based at least in part on a second total storage capacity of at least one second NS associated with the second QoS level.

    HEURISTIC INTERFACE FOR ENABLING A COMPUTER DEVICE TO UTILIZE DATA PROPERTY-BASED DATA PLACEMENT INSIDE A NONVOLATILE MEMORY DEVICE

    公开(公告)号:US20220171740A1

    公开(公告)日:2022-06-02

    申请号:US17671481

    申请日:2022-02-14

    Abstract: An interface for enabling a computer device to utilize data property-based data placement inside a nonvolatile memory device comprises: executing a software component at an operating system level in the computer device that monitors update statistics of all data item modifications into the nonvolatile memory device, including one or more of update frequencies for each data item, accumulated update and delete frequencies specific to each file type, and an origin of the data item; storing the update statistics of each of the data items and each of the data item types in a database; and intercepting all operations, including create, write, and update, of performed by applications to all the data items, and automatically assigning a data property identifier to each of the data items based on current update statistics in the database, such that the data items and assigned data property identifiers are transmitted over a memory channel to the non-volatile memory device.

    Systems and methods for storage device block-level failure prediction

    公开(公告)号:US11275510B2

    公开(公告)日:2022-03-15

    申请号:US16843823

    申请日:2020-04-08

    Abstract: In a method for dynamic wear-levelling and load redirection in a solid-state drive (SSD) including one or more blocks, the method including: receiving, by a controller, a request to write data; calculating, by the controller, a vulnerability factor of the one or more blocks; selecting, by the controller, a target block from the one or more blocks to receive the request to write data; determining, by the controller, a status of the target block based on the vulnerability factor of the target block; writing, by the controller, the data to the target block based on the status of the target block; and updating, by the controller, a mapping table based on the data written to the target block.

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