Abstract: Techniques are provided for persistent memory file system reconciliation. As part of the persistent memory file system reconciliation, high level file system metadata associated with a persistent memory file system of persistent memory is reconciled. Client access to the persistent memory file system is inaccessible until reconciliation of the high level file system metadata has completed. A first scanner is executed to traverse pages of the persistent memory in order to fix local inconsistencies associated with the pages. A local inconsistency of a first set of metadata or data of a page is fixed using a second set of metadata or data of the page. The first scanner is executed asynchronously in parallel with processing client I/O directed to the persistent memory file system.

Abstract: Systems and methods are provided for managing logging and telemetry data in a cloud provider network including block storage volumes (“disks”) and virtual machine instances by scanning data written to the block storage volumes by the virtual machine instances. The disk recycler system can receive configuration information to scan for, obtain, and upload data to logging and telemetry services hosted by the cloud provider network. For example, in the scenario of a virtual machine instance failure, the disk recycler system may upload leftover data from the block storage volume to the relevant logging and telemetry services to avoid gaps in data required by the services of the cloud provider network.

Abstract: A method and an apparatus for assessing a risk caused by improper use of a cluster data resource, an electronic device and a storage medium are provided. A high-risk diagnosis indicator in a storage assessment dimension and a high-risk diagnosis indicator in a computation assessment dimension are obtained. The amount of high-risk diagnosis indicators in the storage assessment dimension that are satisfied in the use process of the data storage resource and the amount of high-risk diagnosis indicators in the computation assessment dimension that are satisfied in the use process of the data computation resource are counted, and are recorded as high-risk indicator trigger quantities. Risk on the data storage resource is assessed based on the high-risk indicator trigger quantity in the storage assessment dimension, and risk on the data computation resource is assessed based on the high-risk indicator trigger quantity in the computation assessment dimension.

Abstract: A data management system may support a configuration backup of a first backup system that manages backup procedures for data of a host environment that is separate from the first backup system. The data management system may transmit, to the first backup system, a request to generate the configuration backup that includes information for the one or more backup procedures managed by the first backup system. The data management system may cause the configuration backup to be stored in a separate storage location. In response to first backup system failure, the data management system may transmit, to a second backup system, a request to apply one or more configurations for the first backup system to the second backup system based on the stored configuration backup for the first backup system.

Abstract: In one example, a method includes receiving one or more test scenarios based on requirements for a system under test having one or more inputs, wherein a value for at least one input of the one or more inputs is undefined for at least one test scenario of the one or more test scenarios. The method further includes determining system intermediate values and system output values that are unambiguously derivable from defined system inputs for the one or more test scenarios. The defined system inputs are defined in the one or more test scenarios and not assumed or default input values. The method further includes determining coverage of one or more functional characteristics of each function of the system under test executed based on the defined system inputs. The method further includes validating the determined system output values that are unambiguously derivable from the defined system inputs.

Abstract: A multi-power-domain emulated persistent memory resource system includes a memory interface/data mover subsystem coupled to a second memory subsystem in a second power domain, and a microvisor subsystem coupled to the memory interface/data mover subsystem and a first memory subsystem in a first power domain that is different than the second power domain. The microvisor subsystem presents an emulated persistent memory resource to a client device using the first memory subsystem, and monitors metadata for the first memory subsystem to identify modification(s) of data in the first memory subsystem. In response, the microvisor subsystem uses the memory interface/data mover subsystem to copy the data from the first memory subsystem to the second memory subsystem. Following unavailability of the first power domain and corresponding loss of the data on the first memory subsystem, the memory interface/data mover subsystem copies the data from the second memory subsystem to the first memory subsystem.

Abstract: Machine-learning systems and methods to predict device failures are described. In some embodiments, an Information Handling System (IHS) may include: a processor; and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution by the processor, cause the IHS to: obtain a shock event notification of a second IHS; execute an Artificial Intelligence/Machine Learning (AI/ML) model; and determine, by the AI/ML model and based, at least in part, on the shock event notification, a likelihood or probability that one or more failures of the second IHS will occur. In some embodiments, the determination, by the AI/ML model, is further based, at least in part, on device model characteristics of the second IHS, device health telemetry of a plurality of other IHSs, and/or service records of a plurality of other IHSs.

Abstract: Systems and methods are provided for generating personalized service disruption notifications. The system allocates resources of a database system to a plurality of entities, the resources of the database system being distributed in a cloud environment and analyzes a plurality of signals on the database system. The system, in response to analyzing the plurality of signals, detects a likelihood of a service availability disruption on the database system for a first entity of the plurality of entities. The system notifies the first entity of the service availability disruption in response to detecting the likelihood of the service availability disruption.

Abstract: This application is directed to communicating telemetry and debugging data of a memory system using optical signals. An enclosed memory device has an optical indicator and receives a data request. In response to the data request, the enclosed memory device obtains internal activity data stored in the memory device, encodes the internal activity data into an electrical signal, and drives the optical indicator with the electrical signal to generate a visible light signal carrying the internal activity data. In some embodiments, the enclosed memory device includes a solid-state drive. In some embodiments, the internal activity data includes telemetry data stored by the enclosed memory device while the memory device is processing a sequence of memory access requests including at least one of a read request and a write request.

Abstract: Novel tools and techniques are provided for implementing intelligent alert automation (“IAA”). In various embodiments, IAA receives alert/event feeds from several different alerting and ticketing systems via input Redis queues, and uses a triage system to determine whether to process the alert/event or disregard it. If so, IAA may create a flow instance, assign a unique instance ID, and place the flow instance in one of a plurality of jobs queues based on alert/event type and/or or source. An abattoir system retrieves a flow instance from one of the jobs queues (in order of the queue's priority), and processes the next node or step in the flow instance. The flow instance is placed back into the jobs queue for subsequent processing by the same or different abattoir system until no additional nodes or steps remain in the flow, at which point the flow instance is considered complete.

Abstract: Provided is a method for recovering data for artificial intelligence calculation, which is performed by one or more processors and which includes extracting, from a command queue, a descriptor associated with a target job, that is a job to be executed, of a plurality of jobs, executing at least one command associated with the extracted descriptor to execute artificial intelligence calculation associated with the target job, resetting the command queue if an error occurs while executing the artificial intelligence calculation, determining at least one descriptor to be recovered, based on tracking data associated with at least one job, and recovering the determined at least one descriptor to the reset command queue.

Abstract: A cluster storage system including servers containing storage processing units (SPUs) can create synchronized snapshot sets for the volumes that the SPUs maintain and can report the snapshot sets to a cloud-based service. Each snapshot in a set reflects the state a corresponding volume had at a rollback point corresponding to the set. A user of the storage system contacts the cloud-based service about recovery of the storage system, and the cloud-based service may present the user with a list of rollback points corresponding to the synchronized snapshot sets. The user may select to recover the storage system to any of the rollback points, and the SPUs promote the selected snapshots to replace the volumes for storage services.

Abstract: A memory system includes a memory device configured to store memory data. The memory device includes an array of memory cells, and a plurality of word lines arranged in a plurality of rows and coupled to the array of memory cells. The memory system also includes a memory controller coupled to the array of memory cells. The memory controller is configured to receive the memory data and first parity data of the memory data, perform a redundant array of independent disks (RAID) encode operation on the memory data and the first parity data to form second parity data, and perform a write operation to store the memory data, the first parity data, and the second parity data into the array of memory cells.

Abstract: Systems and methods related to serial communication devices are provided. An example integrated circuit (IC) device includes interface circuitry coupled to a two-wire serial communication bus having a serial clock (SCL) line and a serial data (SDA) bus line. The IC device further includes bus stuck recovery circuitry to monitor for a local SDA fault condition at the IC device based on a number of clock cycles during which an internal SDA signal (e.g., generated by the IC device) drives the SDA bus line to a first signal state, the clock cycles based on a clock signal received from the SCL line; and responsive to the local SDA fault condition, release the SDA bus line independent of the internal SDA signal, where the SDA bus line is in a second signal state different from the first signal state based on the release.

Abstract: A computational device determines whether a generated system event in the computational device is a critical error or a non-critical error. In response to determining that the generated system event is a critical error caused by a first field-replaceable unit (FRU), the critical error is processed to initiate tasks that lead to replacement of the first FRU in the computational device to eliminate the critical error. In response to determining that the generated system event is a non-critical error caused by a second FRU, operations are performed to proactively reserve a substitute FRU to replace the second FRU to eliminate the non-critical error, in anticipation of the second FRU failing at a future time to cause another critical error.

Abstract: A semiconductor memory device includes a memory unit and an error detection and correction unit. The memory unit is an OTP memory including a data memory and a check memory. The data memory is written with replacement address information detected during a redundancy repair operation and corresponding to a defective memory cell of a memory cell array. The check memory is written with check information detected during the redundancy repair operation and corresponding to the replacement address information. The error detection and correction unit performs error detection and correction on the replacement address information based on the check information. The data memory includes a unit accommodating the replacement address information. When other replacement address information and any of the replacement address information and the check information corresponding to the replacement address information are in the unit, the error detection and correction unit will not perform error detection and correction.

Abstract: A request to access data programmed to a memory sub-system is received. A determination is made of whether a memory device that stores the data referenced by the request satisfies a weak memory device criterion in view of a quality rating for the device. In response to a determination that the memory device satisfies the weak memory device criterion, an error correction operation to access the data is performed in accordance with the request.

Abstract: An apparatus for anomaly detection, the apparatus comprising means for: Collecting a plurality of log messages from a data processing system, log messages comprising textual content and numeric attributes, Classifying the plurality of log messages into a plurality of clusters as a function of a number of the numeric attributes in the log messages, such that the log messages within a cluster have a given number of the numeric attributes, For at least one of the clusters, computing at least one encoding vector associated to a numeric attribute, Computing a combined semantic embedding vector from the textual contents of the plurality of log messages, Combining the at least one encoding vector with the combined semantic embedding vector into a final encoding vector, and Feeding the final encoding vector to an anomaly detection module intended to detect an anomaly in the data processing system.

Abstract: A data storage method and system comprising: at least one target server that comprises a storage media and configured to run an operating system designated to host data accessible over a data plane (DP) network, at least one initiator server comprises a storage media and configured to run an operating system designated to access and expose a remote resource/s over the data plane (DP) network, and at least one orchestrator configured to interact with each of said servers and designated to control a control plane (CP) of said DP network, wherein the combined storage medias form a shared drive storage stack, wherein the orchestrator is configured to utilize the CP in order to monitor and inspect the operability and condition of the shared drive storage stack, and further configured to monitor, record and inspect the proper operation of block device/s stored within said shared drive storage stack, wherein the orchestrator is configured to identify a malfunction affecting the shared drive storage stack, and wherei

Abstract: A storage device and an operating method thereof are provided. The storage device includes a non-volatile memory, an interconnect, a device controller and a buffer memory. The interconnect exchanges data with a host, receives a logging target setting request and a logging period setting request with respect to debugging information from the host, receives a debugging information logging request from the host, and receive a read request from the host. The device controller includes a buffer memory and controls the non-volatile memory, controls a logging operation on the debugging information based on a logging target and a logging period, which are requested by the host, in response to the debugging information logging request, and transmits the debugging information to the host in response to the read request. The device controller is further configured to logs the debugging information in the buffer memory according to the debugging information logging request.