Respective life expectancies of a first data unit and a second data unit of the memory device is obtained. A first initial age value corresponding to the first data unit and a second initial age value corresponding to the second data unit are determined. A lower one of the first initial age value and the second initial age value is identified. A first media management operation on a corresponding one of the first data unit or the second data unit associated with the lower one of the first initial age value and the second initial age value is performed. A second media management operation on the first data unit and the second data unit is performed.

Methods, apparatus, and processor-readable storage media for device component management using deep learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more enterprise devices; determining, for each of the one or more enterprise devices, values for multiple device attributes by processing the obtained telemetry data; generating, for each of the one or more enterprise devices, at least one prediction related to lifecycle information of at least one device component by processing the determined attribute values using one or more deep learning techniques; and performing one or more automated actions based at least in part on the at least one generated prediction.

A controller of a non-volatile, dual, in-line memory modules (NVDIMM). A NVDIMM is configured to predict thermal events associated with save and restore operations prior to starting the save or restore operation. The controller of the NVDIMM includes a thermal event prediction circuit to predict whether a thermal event will occur in response to a request to perform a save or restore operation, and to cause the controller to perform an action in response to a determination that a thermal event is likely to occur. To predict the thermal event, the controller may be configured to predict a peak temperature of the save or restore operation based on a predicted temperature increase from an initial or starting temperature. The predicted temperature increase may be based on a rate of temperature change during the save or restore operation and a duration of the save or restore operation.

In one embodiment, an exemplary method includes receiving multi-dimension information from a data domain operating system running on the server; determining that multiple drive failures occurred within a predetermined time frame based on the multi-dimension information; and extracting a list of system-level events and a timestamp of each event from the multi-dimension information. The method further includes determining a list of components impacted by the list of the system-level events based on the list of system-level events and the timestamp of each event; and determining one or more system-level events associated with one or more impacted components as root causes of the multiple drive failures based on the multi-dimension information. The method uses information from multiple regions of the DAE and correlate the information using a predetermined algorithm to automatically more efficiently identify one or more possible root causes of the multiple drive failures.

Methods, apparatus, and processor-readable storage media for learning machine behavior related to install base information and determining event sequences based thereon are provided herein. An example computer-implemented method includes parsing data storage information based at least in part on parameters related to install base information comprising temporal parameters and event-related parameters; formatting the parsed set of data storage information into a parsed set of sequential data storage information compatible with a neural network model; training the neural network model using the parsed set of sequential data storage information and additional training parameters; predicting, by applying the trained neural network model to the parsed set of sequential data storage information, a future data unavailability event and/or a future data loss event; and outputting an alert based at least in part on the predicted future data unavailability event and/or predicted future data loss event.

A storage device is provided which shares a host memory with a host. The storage device includes an interface that exchanges data with the host and implements a protocol to use a partial area of the host memory as a buffer of the storage device. A storage controller of the storage device monitors deterioration information of a first area of the buffer and transmits a corruption prediction notification associated with the first area to the host based on a result of the monitoring.

A method includes, storing a set of valid codewords including: a first valid functional codeword representing a functional state of a controller subsystem; a first valid fault codeword representing a fault state of the controller subsystem and characterized by a minimum hamming distance from the first valid functional codeword; a second valid functional codeword representing a functional state of a controller; and a second valid fault codeword representing a fault state of the controller; in response to detecting functional operation of the controller subsystem, storing the first valid functional codeword in a first memory; in response to detecting a match between contents of the first memory and the first valid functional codeword, outputting the second valid functional codeword; in response to detecting a mismatch between contents of the first memory and every codeword in the first set of valid codewords, outputting the second valid fault codeword.

An example embodiment involves rules related to repairing software programs, wherein the rules associate indications of software program failures with repair applications that are configured to correct the software program failures. One or more processors are configured to: (i) receive, by a predictive model, a representation of an execution history of a particular software program, wherein the predictive model has been trained on a corpus of execution histories of the software programs; (ii) generate, by the predictive model and from the execution history, a failure prediction for the particular software program; (iii) receive, by an automated repair controller application, the failure prediction from the predictive model; (iv) based on applying the rules to the failure prediction, determine, by the automated repair controller application, a repair application from the repair applications; and (v) cause, by the automated repair controller application, the repair application to be executed within the network.

The disclosed embodiments include methods and systems for providing predictive quality analysis. Consistent with disclosed embodiments, a system may receive input data associated with a software program and compare the input data with one or more predetermined analysis parameters. The system may further determine at least one risk rating based on the comparison, wherein each risk rating corresponds to a distinct software category. The system may perform additional operations, including determining at least one adjustment to the software program based on the determined at least one risk rating, and prioritizing the at least one adjustment based on a predetermined adjustment priority standard. Furthermore, the system may provide a report including at least an indication of the at least one prioritized adjustment, a timeline for implementing the at least one prioritized adjustment, and plan implementing the at least one prioritized adjustment.

A method of optimizing healthcare services consumption according to the invention includes the steps of assessing the healthcare situation of an employer providing healthcare benefits to a population, identifying a first group of patients from the population likely to generate expensive healthcare claims based on data representing past claims, periodically determining whether patients in the first group have satisfied certain predetermined healthcare requirements, identifying a first group of providers who provide high quality, cost efficient healthcare services based on the practice patterns of the providers, prompting patients who have not satisfied the predetermined healthcare requirements to obtain services from providers in the first group, and responding to healthcare requests from patients by determining whether the requesting patient is seeking services from a provider in the first group, and, if not, urging the patient to obtain such services from a provider in the first group.