A medical device arrangement (100) tests processing of data sets to be processed during operation of the medical device arrangement. The arrangement includes a data interface (110), analysis modules (120) and a test module (130). The analysis modules process a received medical data set (105). Each analysis module (122, 123, 124) forms a processing instance (390) for the medical data set or for the medical data set (125, 125′) already preprocessed by at least one other analysis module. The test module outputs a test data set (132) to one of the analysis modules during operation such that this analysis module processes the test data set in the same manner as the medical data set. The test module compares a correspondingly outputted, processed test data set (134) with a reference result (136) associated with the test data set and determines a test result (137) based on this comparison.

An apparatus in one embodiment comprises a host device configured to communicate with a storage system. Responsive to an instruction to migrate data from a source volume to a destination volume, the host device replaces an input-output entry function of a source pathing device associated with the source volume with a migration input-output entry function that is configured, in response to receiving an input-output operation, to call an input-output entry function of a destination pathing device associated with the destination volume and to call the input-output entry function of the source pathing device. Responsive to an indication that a migration of data has completed, the host device replaces the migration input-output entry function with a post-migration input-output entry function. The post-migration input-output function is configured to call the input-output entry function of the destination pathing device in response to receiving an input-output operation.

The present technology relates to an electronic device. A memory controller controls a memory device such that a life of the memory device is increased. The memory controller that controls the memory device includes a flash translation layer configured to generate a device health descriptor based on device information received from the memory device, and a bad block controller configured to generate a bad block table based on bad block information received from the memory device, and generate recycling information for recycling pages in bad blocks recorded in the bad block table based on the device health descriptor.

An example networked computing system comprises a node cluster; a database; at least one processor configured by instructions to perform operations in a method of node level recovery, the method comprising operations including at least: identifying a failed node among existing nodes in the node cluster; identifying and initiating a replacement node as a new node for the node cluster; accessing at the database a logical backup of the node cluster; retrieving logical backup data of the node cluster from the logical backup and applying a node level filter to identify rows of backup data associated with the failed node; and restoring the data rows identified by the node level filter to the new node.

In general, the invention relates to a method for managing data. The method includes detecting a failure of a persistent storage device of a plurality of persistent storage devices, and in response to the detecting, initiating a rebuilding of data in a spare persistent storage device using proactive copy metadata, checkpoint metadata, and slice metadata, wherein the data is a copy of data that was stored in the persistent storage device.

In general, the invention relates to a method for managing data. The method includes detecting a failure of a persistent storage device in a data node of a plurality of data nodes, and in response to the detecting, initiating a rebuilding of data in a spare location using proactive copy metadata and slice metadata, wherein the data is a copy of data that was stored in the persistent storage device.

In general, the invention relates to a method for managing data. The method includes detecting a failure of a persistent storage device of a plurality of persistent storage devices, and in response to the detecting, initiating a rebuilding of data in a spare persistent storage device using proactive copy metadata, checkpoint metadata, and slice metadata, wherein the data is a copy of data that was stored in the persistent storage device.

A memory controller includes a command queue, a memory interface queue, and a non-volatile error reporting circuit. The command queue receives memory access commands including volatile reads, volatile writes, non-volatile reads, and non-volatile writes, and an output. The memory interface queue has an input coupled to the output of the command queue, and an output for coupling to a non-volatile storage class memory (SCM) module. The non-volatile error reporting circuit identifies error conditions associated with the non-volatile SCM module and maps the error conditions from a first number of possible error conditions associated with the non-volatile SCM module to a second, smaller number of virtual error types for reporting to an error monitoring module of a host operating system, the mapping based at least on a classification that the error condition will or will not have a deleterious effect on an executable process running on the host operating system.

A method for managing a persistent storage system includes obtaining, by a first node in a node cluster, a write request, wherein the node cluster comprises the first node and a second node, processing the write request, storing data associated with the write request in a persistent storage system, updating a block-based change list based on the storing, making a first determination that a synchronization schedule is triggered, and in response to the first determination: initiating a block-based change list synchronization to the second node.

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.