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.

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.

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.

Abstract: In a method for training a machine learning model, the method includes: segmenting, by a processor, a dataset from a database into one or more datasets based on time period windows; assigning, by the processor, one or more weighted values to the one or more datasets according to the time period windows of the one or more datasets; generating, by the processor, a training dataset from the one or more datasets according to the one or more weighted values; and training, by the processor, the machine learning model using the training dataset.

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.

Abstract: In a method for training a machine learning model, the method includes: segmenting, by a processor, a dataset from a database into one or more datasets based on time period windows; assigning, by the processor, one or more weighted values to the one or more datasets according to the time period windows of the one or more datasets; generating, by the processor, a training dataset from the one or more datasets according to the one or more weighted values; and training, by the processor, the machine learning model using the training dataset.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.