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.

In some embodiments, the present disclosure provides an exemplary method that may include the steps of providing a computing device associated with a plurality of user; receiving output data transmitted from a target unit; analyzing the output data; transmitting a plurality of interaction commands; transmitting the plurality of interaction commands to an application or operating system; determining a plurality of identifying key words; dynamically determining a configuration screen image based on an identification of the plurality of identifying key words associated with the plurality of graphical user interface displays; automatically selecting a configuration setting associated with the plurality of interactive image elements based on the configurations screen image; and executing a plurality of ameliorative actions associated with the configuration setting.

A first information handling system may receive a telemetry metric report from a client information handling system. The first information handling system may determine that one or more characteristics of the telemetry metric report do not match one or more predetermined telemetry metric report characteristics. The first information handling system may perform one or more corrective actions based, at least in part, on the determination that the one or more characteristics of the telemetry metric report do not match one or more predetermined telemetry metric report characteristics.

A description of a snapshot to be generated is received by a local media controller of a memory device, from a memory sub-system controller. The description comprises a memory address range of a memory device. Responsive to detecting a triggering event, a snapshot of the memory address range of the memory device is generated in view of the description. The snapshot is stored to a destination address. The memory sub-system controller is notified of the triggering event.

Devices and techniques for managing flash memory are disclosed herein. A memory controller may receive a first program request comprising first host data to be written to the flash memory. The flash memory may comprise a number of storage units with each storage unit comprising a number of storage sub-units. If the first host data is less than a remainder threshold, the memory controller may generate a first program data unit comprising the first host data and first log data describing the flash memory. The memory controller may program the program data unit to the first storage unit of the flash memory, where the first log data is written to a first storage sub-unit of the number of storage sub-unit. The memory controller may also store an indication that the first storage sub-unit is invalid.

A computer system is provided. The computer system includes a memory and at least one processor coupled to the memory. The at least one processor is configured to scan session data representative of operation of a user interface comprising a plurality of user interface elements; detect, at a point in the session data, at least one changed element within the plurality of user interface elements; classify, in response to detecting the at least one changed element, the at least one changed element as either indicating or not indicating an error; store an association between the error and the point in the session data; and provide access to the point in the session data via the association.

A method and system for communicating with IoT devices connected to a vehicle to gather information related to device operation or performance is disclosed. The system makes a copy of at least a portion of the device's non-volatile memory and/or receives IoT device data (e.g., sensor data and/or log files etc.) from an IoT device that recently failed. The system determines which log files and/or sensor data, for example, the IoT device created before and/or after a failure. After gathering this information, the system stores the information, sends it to a storage destination for further analysis and diagnostics to troubleshoot the failure and send a fix or software update to the IoT device. The information can also be placed into secondary storage to comply with regulatory, insurance, or legal purposes.

Methods, systems, apparatuses, and computer-readable storage mediums are described for machine learning-based techniques for reducing the visual complexity of a dependency graph that is representative of an application or service. For example, the dependency graph is generated that comprises a plurality of nodes and edges. Each node represents a compute resource (e.g., a microservice) of the application or service. Each edge represents a dependency between nodes coupled thereto. A machine learning-based classification model analyzes each of the nodes to determine a likelihood that each of the nodes is a problematic compute resource. For instance, the classification model may output a score indicative of the likelihood that a particular compute resource is problematic. The nodes and/or edges having a score that exceed a predetermined threshold are provided focus via the dependency graph.

The confidentiality of data is maintained in a case where analysis of an operation state of a facility is entrusted to the outside. Time difference information in operation between the manufacturing apparatuses (RB1), (RB2), . . . , arranged on the production line (LN) is stored for each of the apparatuses, in an inter-apparatus time difference information storage unit (33). At the occurrence of a failure in one of the manufacturing apparatuses (RB1), (RB2), . . . , the operation estimation unit (13) selects, from among the peripheral apparatuses on the upstream side and the downstream side with respect to the manufacturing apparatus concerned, one on the upstream side and one on the downstream side, estimates an operation period of each selected peripheral apparatus associated with the failure, reads log data corresponding to the estimated operation period from the operation history storage unit (31), and transmits the read piece to the external support center (SC).

Systems, methods, and computer programming products for predicting, preventing and remediating failures of batch jobs being executed and/or queued for processing at future scheduled time. Batch job parameters, messages and system logs are stored in knowledge bases and/or inputted into AI models for analysis. Using predictive analytics and/or machine learning, batch job failures are predicted before the failures occur. Mappings of processes used by each batch job, historical data from previous batch jobs and data identifying the success or failure thereof, builds an archive that can be refined over time through active learning feedback and AI modeling to predictively recommend actions that have historically prevented or remediated failures from occurring. Recommended actions are reported to the system administrator or automatically applied. As job failures occur over time, mappings of the current system log to logs for the unsuccessful batch jobs help the root cause analysis becomes simpler and more automated.