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.

Staging data on a storage element integrating fast durable storage and bulk durable storage, including: receiving, at a storage element integrating fast durable storage and bulk durable storage, a data storage operation from a host computer; storing data corresponding to the data storage operation within fast durable storage in accordance with a first data resiliency technique; and responsive to detecting a condition for transferring data between fast durable storage and bulk durable storage, transferring the data from fast durable storage to bulk durable storage in accordance with a second data resiliency technique.

Techniques for estimating raw bit error rate of data stored in a group of memory cells are described. Encoded data is read from a group of memory cells. A first population value is obtained based on a first number of memory cells in the group of memory cells having a read voltage within a first range of read voltages, each read voltage representing one or more bits of the encoded data. An estimated raw bit error rate of the data is determined to satisfy a first threshold. The determination is made using a first trained machine learning model and based in part on the first population value. A first media management operation is initiated in response to the determination that the estimated raw bit error rate satisfies the first threshold.

Provided is a method for performing a file system consistency check. The method comprises calculating, by a first thread that does not have access to an inode table, file block addresses for one or more files to be checked by the thread. The method further comprises collecting validity information for the one or more files. The method further comprises reading information relating to the one or more files from the inode table. The reading is performed in response to the thread being given access to the inode table after the calculating operation. The method further comprises validating the information by comparing the information from the inode table to the validity information.

Techniques for storage management involve: obtaining information indicating an error of a storage device of a data storage system; if the number of occurrences of the error within a predetermined time period exceeds a predetermined threshold, stopping obtaining the information indicating the error; and generating an event indicating whether the number of occurrences of the error within the predetermined time period exceeds the predetermined threshold for further diagnosis of the error. As a result, errors from the storage device can be automatically managed, which helps to improve the data storage system's capacity to handle different types of errors of the storage device.

A verification information modification device includes processing circuitry configured to acquire, from each verification device that uses verification information of software to verify a file forming the software, an error log relating to erroneous detection that has occurred in the verification device, when it is determined that a same error has occurred in a predetermined number or more of verification devices based on the acquired error log, extract an error log of the error from acquired error logs and create information indicating verification information that has caused the erroneous detection and candidates for modification details of the verification information based on the extracted error log, and output the information indicating verification information that has caused the erroneous detection and candidates for modification details of the verification information.

Apparatus and method for actively mitigating coherent errors by modifying an original quantum circuit, inserting Clifford gate operations at intermediate stages. Embodiments of the apparatus and method may perform CGI statically, at the compiling stage, and/or dynamically, at the control processing stage. The insertion of Clifford gates takes advantage of the symmetries in a quantum circuit and actively cancels coherent errors, maintaining the quantum processor in a state as close as possible to the original tune-up environment.

A method for error management is provided. The method comprises receiving a message call request regarding an error event generated by a software application. The message call request comprises a message ID associated with an error type. In response to the call request a message cache is searched for the message ID. If the ID is in the cache, an error message associated with the ID is returned. The error message provides a description of the error and suggested remedial action. If the message ID is not in the cache, the error message is fetched from a message repository that contains error messages corresponding to respective message IDs. The fetched error message is loaded into the cache and returned. Message call request data is stored in a metrics repository. The message call request data comprises frequency metrics that describe how often the message ID is received.

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.

In various examples, a VPU and associated components may be optimized to improve VPU performance and throughput. For example, the VPU may include a min/max collector, automatic store predication functionality, a SIMD data path organization that allows for inter-lane sharing, a transposed load/store with stride parameter functionality, a load with permute and zero insertion functionality, hardware, logic, and memory layout functionality to allow for two point and two by two point lookups, and per memory bank load caching capabilities. In addition, decoupled accelerators may be used to offload VPU processing tasks to increase throughput and performance, and a hardware sequencer may be included in a DMA system to reduce programming complexity of the VPU and the DMA system. The DMA and VPU may execute a VPU configuration mode that allows the VPU and DMA to operate without a processing controller for performing dynamic region based data movement operations.