Abstract: A cache management method, a solid state drive controller, and a solid state drive are provided. The solid state drive comprises a cache space, and the cache space comprises a dirty data block linked list. The method comprises: obtaining a write command and generating a first dirty data block used to record data information of the write command; and if the first dirty data block conflicts in address range with a second dirty data block in the dirty data block linked list, marking data in the conflicting region of the second dirty data block as invalid and inserting the first dirty data block into the dirty data block linked list.

Abstract: Disclosed is a method for enhancing service quality of a solid state drive and the solid state drive, and the method includes the steps of obtaining a remaining storage resource corresponding to a write I/O request periodically, and determining a resource waiting time of the write I/O request based on a resource consumption rate of the write I/O request. The method also includes setting a maximum scheduling delay time for the write I/O request according to the resource waiting time of the write I/O request. The method also includes identifying a type of unprocessed I/O requests and obtaining an arrival time of the unprocessed I/O requests, and sorting the unprocessed I/O requests in combination with the maximum scheduling delay time. The method further includes processing the unprocessed I/O requests according to the sort of the unprocessed I/O requests.

Abstract: Disclosed is a method for data processing applied to a solid state drive, a computer device and a computer-readable storage medium. The method includes acquiring an interface protocol command received by the solid state drive. The method also includes parsing the interface protocol command to obtain I/O information from the interface protocol command. The I/O information includes at least an I/O timestamp, an I/O type, and an I/O size. The method further includes invoking machine learning based on the I/O information to predict I/O information of a first future time period, so that a processor of the solid state drive is configured to proactively execute management functions according to the prediction results.

Abstract: The invention discloses a solid-state drive control device and a learning-based solid-state drive data access method, wherein the method comprises the steps of: presetting a hash table, the hash table comprising more than one hash value, the hash value is used to record and represent data characteristics of data pages in the solid-state drive. Obtaining an I/O data stream of the solid-state drive, and obtaining a hash value corresponding to the I/O data stream in the hash table. Predicting a sequence of data pages and/or data pages that are about to be accessed by a preset first learning model. Prefetching data is performed in the solid-state drive based on an output result of the first learning model. Through the embodiment of the present invention, when predicting prefetched data, learning can be performed in real time to adapt to different application categories and access modes through adaptive adjustment parameters, so that better data prefetching performance can be obtained.

Abstract: The invention discloses a solid-state drive control device and a learning-based solid-state drive data access method, wherein the method comprises the steps of: presetting a hash table, the hash table comprising more than one hash value, the hash value is used to record and represent data characteristics of data pages in the solid-state drive. Obtaining an I/O data. stream of the solid-state drive, and obtaining a hash value corresponding to the I/O data stream in the hash table. Predicting a sequence of data pages and/or data pages that are about to be accessed by a preset first learning model. Prefetching data is performed in the solid-state drive based on an output result of the first learning model. Through the embodiment of the present invention, when predicting prefetched data, learning can be performed in real time to adapt to different application categories and access modes through adaptive adjustment parameters, so that better data prefetching performance can be obtained.

Abstract: The present disclosure discloses a control device of data storage system, including a host interface, a peer interface, a storage unit interface, a processor and a local data management module. The host interface is connected and communicated with a storage server for data interaction with the storage server. The peer interface is configured for data communication connection with a storage unit of an adjacent control device in the data storage system. The storage unit interface is configured to connect a storage unit. The local data management module is configured for local data management of the data in the storage unit according to the data management instruction via the processor. The host interface is configured to send result data of local data management to the storage server.

Abstract: A data recovery method, an apparatus, and a solid state drive are provided. The method includes: copying, after an abnormality occurs in the solid state drive, memory data of a first memory space to a solidified area of a second memory space, and then writing address information of the solidified area to a third memory space; recovering, after a controller is reset for the first time, the memory data of the first memory space according to the address information of the solidified area; flushing data of the second memory space into a flash memory space according to the recovered memory data of the first memory space; and performing, after the controller is reset for the second time, data recovery on the solid state drive according to memory data of the flash memory space.

Abstract: A method for predicting logical blocks address (LBA) information, including: receiving, by a Solid State Drive (SSD), a trace sent from a host, wherein the host can acquire the trace in a reusable environment; determining, by the SSD, one or more LBAs received by the SSD according to the trace; obtaining, by the SSD, a distribution of the LBAs by learning the LBAs based on a preset learning algorithm; and predicting, by the SSD, one or more subsequent LBAs based on the distribution of the LBAs. As a result, it can perform heat classification and prediction of the following LBA used in the SSD by means of learning the LBA distribution of the SSD in a certain reusable environment of the host, thus to improve the hit rate of reading and writing and the efficiency of classification of hot and cold data in garbage collection.

Abstract: A data error correction method, apparatus, device, and readable storage medium are disclosed. The method includes: acquiring target data to be error-corrected; performing error correction on the target data using an error-correcting code to obtain first data; judging whether the performing of the error correction on the target data is successful; responsive to the performing of the error correction on the target data being not successful, correcting the target data using a target neural network to obtain second data, determining the second data as the target data, and continuing to perform the error correction on the target data again; and responsive to the performing of the error correction on the target data being successful, determining the first data as the error-corrected target data.

Abstract: A data processing method, apparatus, device, and readable storage medium are provided. The method includes: acquiring historical I/O data (S101), where the historical I/O data is data of a solid-state drive that is accessed within a preset time period; using a prediction model to learn the historical I/O data to obtain a prediction result (S102), where the prediction result includes a data intensity of the solid-state drive to be accessed within a future window period, and the future window period is determined according to a cycle in which the solid-state drive is accessed; and managing the solid-state drive according to the prediction result (S103).

Abstract: The present disclosure discloses a method, a system, and a solid-state drive for data processing. The method includes detecting the user operations on a first storage area which is visible to an operating system in a solid-state drive; if the user operation is a reading or writing operation, keeping the data of the first storage area and the data of a mirrored data storage area of a second storage area to be synchronization; if the user operation is a deleting operation or a TRIM operation, keeping the data of the first storage area and the data of the mirrored data storage area of the second storage area to be synchronization, and backing up the deleted data from the first storage area to a deleted data storage area of the second storage area. The second storage area is set to be invisible to the operating system in the solid-state disk.

Abstract: Disclosed is a method for enhancing service quality of a solid state drive and the solid state drive, and the method includes the steps of obtaining a remaining storage resource corresponding to a write I/O request periodically, and determining a resource waiting time of the write I/O request based on a resource consumption rate of the write I/O request. The method also includes setting a maximum scheduling delay time for the write I/O request according to the resource waiting time of the write I/O request. The method also includes identifying a type of unprocessed I/O requests and obtaining an arrival time of the unprocessed I/O requests, and sorting the unprocessed I/O requests in combination with the maximum scheduling delay time. The method further includes processing the unprocessed I/O requests according to the sort of the unprocessed I/O requests.

Abstract: Disclosed is a method for data processing applied to a solid state drive, a computer device and a computer-readable storage medium. The method includes acquiring an interface protocol command received by the solid state drive. The method also includes parsing the interface protocol command to obtain I/O information from the interface protocol command. The I/O information includes at least an I/O timestamp, an I/O type, and an I/O size. The method further includes invoking machine learning based on the I/O information to predict I/O information of a first future time period, so that a processor of the solid state drive is configured to proactively execute management functions according to the prediction results.

Abstract: A method and system for binding and verifying between a storage device and hosts are provided. The method includes: sharing system parameters in advance with the hosts by the storage device, where the hosts are to be bound to the storage device; using a programmable read-only memory as the storage device, dividing the programmable read-only memory into m regions, completing the binding between each region and one corresponding host to be bound using the pre-shared system parameters, where m is a positive integer; verifying an identity of a bound host in accordance with the pre-shared system parameters through a zero-knowledge proof method by the storage device; if the verification is successful, starting the storage device and serving the bound host; if the verification is unsuccessful, terminating the starting of the storage device and refusing to serve the bound host.

Abstract: The invention discloses a solid-state drive control device and a learning-based solid-state drive data access method, wherein the method comprises the steps of: presetting a hash table, the hash table comprising more than one hash value, the hash value is used to record and represent data characteristics of data pages in the solid-state drive. Obtaining an I/O data stream of the solid-state drive, and obtaining a hash value corresponding to the I/O data stream in the hash table. Predicting a sequence of data pages and/or data pages that are about to be accessed by a preset first learning model. Prefetching data is performed in the solid-state drive based on an output result of the first learning model. Through the embodiment of the present invention, when predicting prefetched data, learning can be performed in real time to adapt to different application categories and access modes through adaptive adjustment parameters, so that better data prefetching performance can be obtained.

Abstract: The invention discloses a solid-state drive control device and a learning-based solid-state drive data access method, wherein the method comprises the steps of: presetting a hash table, the hash table comprising more than one hash value, the hash value is used to record and represent data characteristics of data pages in the solid-state drive. Obtaining an I/O data. stream of the solid-state drive, and obtaining a hash value corresponding to the I/O data stream in the hash table. Predicting a sequence of data pages and/or data pages that are about to be accessed by a preset first learning model. Prefetching data is performed in the solid-state drive based on an output result of the first learning model. Through the embodiment of the present invention, when predicting prefetched data, learning can be performed in real time to adapt to different application categories and access modes through adaptive adjustment parameters, so that better data prefetching performance can be obtained.

Abstract: A file storage system, including a host and a storage device connected to the host. The host receives a file sent by an upload terminal, and transmits the file to the storage device; and the storage device includes a format conversion module and a storage module, the format conversion module converting an initial format of the file into a target format, and the storage module storing the file of the target format. Further provided in the present disclosure is a file storage method. The file storage system and method provided in the present disclosure achieve format file conversion and storage in a storage device, so that the file format conversion does not occupy resources of a host; and avoid the need for transmitting the converted files of the target format to the storage device from the host, thereby save bus energy between the host and the storage device.

Abstract: The invention discloses a solid-state drive control device and a learning-based solid-state drive data access method, wherein the method comprises the steps of: presetting a hash table, the hash table comprising more than one hash value, the hash value is used to record and represent data characteristics of data pages in the solid-state drive. Obtaining an I/O data stream of the solid-state drive, and obtaining a hash value corresponding to the I/O data stream in the hash table. Predicting a sequence of data pages and/or data pages that are about to be accessed by a preset first learning model. Prefetching data is performed in the solid-state drive based on an output result of the first learning model. Through the embodiment of the present invention, when predicting prefetched data, learning can be performed in real time to adapt to different application categories and access modes through adaptive adjustment parameters, so that better data prefetching performance can be obtained.

Abstract: A data processing method and a storage device, which are used for improving the security of data stored in the storage device. The method includes that the storage device acquires a data processing instruction sent from a host. The data processing instruction is used to operate data stored in the storage device. The method also includes that the storage device determines that the data processing instruction complies with a preset data destruction rule. The method further includes that in response to the determination, the storage device executes a preset processing strategy to protect data stored in the storage device.

Abstract: A method for predicting logical blocks address (LBA) information, including: receiving, by a Solid State Drive (SSD), a trace sent from a host, wherein the host can acquire the trace in a reusable environment; determining, by the SSD, one or more LBAs received by the SSD according to the trace; obtaining, by the SSD, a distribution of the LBAs by learning the LBAs based on a preset learning algorithm; and predicting, by the SSD, one or more subsequent LBAs based on the distribution of the LBAs. As a result, it can perform heat classification and prediction of the following LBA used in the SSD by means of learning the LBA distribution of the SSD in a certain reusable environment of the host, thus to improve the hit rate of reading and writing and the efficiency of classification of hot and cold data in garbage collection.