A blockchain transaction manager implements a method of managing submission of blockchain transactions to a node in a blockchain network by validating a received blockchain transaction and enqueuing the validated received blockchain transaction in a transaction queue, preparing at least one transaction attribute of the received blockchain transaction and placing the received blockchain transaction in a persistence queue, digitally signing or certifying the received blockchain transaction, attempting to submit the digitally signed or certified blockchain transaction to the node, and polling a blockchain status of the submitted blockchain transaction. Processes are provided for automatically recalculating blockchain transaction processing fees in the blockchain transaction attributes. Processes are also provided for repairing transaction attributes when the blockchain transaction has been rejected and submitting the repaired blockchain transaction to the node. Also, nonces are automatically assigned to received blockchain transactions and reassigned when the associated blockchain transaction has been rejected.

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.

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.

A display device includes: a first memory storing compensation data and a display driver integrated chip including a compensator converting the input image data into output image data based on the compensation data. The display driver integrated chip includes: a second memory receiving the compensation data from the first memory when the display device is power-on; a third memory included in the compensator, the third memory storing the compensation data received from the second memory; and an error detector detecting an error in the compensation data stored in the third memory by comparing the compensation data stored in the first memory with the compensation data stored in the third memory.

An illustrative method includes receiving, in response to a storage query sent to a plurality of storage nodes of a data storage system to inquire as to which of the storage nodes can store data, a plurality of responses from a subset of storage nodes included in the plurality of storage nodes, the responses including an indication as to whether any of the storage nodes included in the subset is already storing additional data having a data identifier included in the storage query; renaming, based on a particular storage node included in the subset indicating that the particular storage node is already storing the additional data, the data; selecting, based on the responses, multiple storage nodes included in the subset; and sending the data and the data identifier to the selected storage nodes for storage by the selected storage nodes.

A memory module is disclosed that includes a substrate, a memory device that outputs read data, and a buffer. The buffer has a primary interface for transferring the read data to a memory controller and a secondary interface coupled to the memory device to receive the read data. The buffer includes error logic to identify an error in the received read data and to identify a storage cell location in the memory device associated with the error. Repair logic maps a replacement storage element as a substitute storage element for the storage cell location associated with the error.

The present invention provides a System and method for multi-tiered data synchronization. Data is synchronized between a master synchronization server, one or more proxy synchronization servers, and client devices. Client devices establish synchronization sessions with either a proxy synchronization server or a master synchronization server, depending on which server provides the “best” available connection to that client device. Each proxy synchronization server synchronizes data with client devices that have established a synchronization session with such proxy synchronization server. The master synchronization server synchronizes data with client devices that have established a synchronization session with the master synchronization server. Each proxy synchronization server synchronizes data with the master synchronization server. Metadata associated with synchronized files is synchronized throughout the system in real-time. Files may be synchronized in real-time or of a delayed time.

Systems and methods for lossless restoration of a digital system are provided. A method may include creating a digital twin of the digital system. Creating the digital twin may include constructing a digital model that replicates hardware and software components and performance metrics of the digital system. The components and the performance metrics may be detected via a plurality of edge devices. The digital model may be configured to be run on a processor to simulate performance of the digital system. The method may include receiving an indication that the digital system is disconnected from a central server, syncing the digital twin with the digital system while the digital system is disconnected from the central server, and, in response to an indication that the digital system has reconnected with the central server, syncing the central server with the digital twin.

A secondary storage controller determines one or more tracks of one or more volumes in which data loss has occurred in the secondary storage controller. The secondary storage controller suspends a peer to peer remote copy operation between the secondary storage controller and a primary storage controller. Information on the one or more tracks of the one or more volumes in which the data loss has occurred is transmitted to the primary storage controller.

A method for execution by a dispersed storage and task (DST) processing unit includes obtaining audit records for an audit object and determining when the audit object is complete. When the audit object is complete, aggregating the audit records of the audit object within the audit object by generating the audit object to include the audit records; generating identifier (ID) information and generating integrity information. Fields of the audit object are populated with the audit records, the ID information, and the integrity information and a name of the audit object is determined for storage of the audit object and the name of the audit object in a dispersed storage network (DSN).