Abstract: Methods, systems, and products for health signature based load shifting for part longevity may include monitoring one or more variables associated with each of multiple redundant parts of a computing system, where one or more of the redundant parts are experiencing a load, generating, based on the monitored variables, a health signature for each of the redundant parts, and shifting, based on the health signatures, the load between the redundant parts.

Abstract: Instead of incurring interface errors caused by power drops, continuously track all the channels between the controller and the storage device (both in-band and side-band channels). The tracking will include extracting relevant indications and use a prediction model that correlates between the tracked indications to later occurrence of power drop events. In response to the prediction results, the system may perform different rehabilitation operations (countermeasures). The controller will monitor different indications from the storage element and power supply to predict a signal integrity degradation marginality event on the interface. The monitoring and mitigation strategy relies on an open-ended system. The controller does not employ a real-time continuous return channel from the storage device that may signal pass/fail conditions.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for identifying anomalies of failure events on a cloud computing system and determining cross-component and cross-layer correlation between change events that occur on the cloud computing system and the failure events associated with the anomalies. In particular, this disclosure describes a system that receives telemetry related to change events and failure events across any number of computing layers of a distributed computing environment (e.g., a cloud computing system) and detects anomalies based on counts of failure events that are manifested over discrete periods of time. Based on these detected anomalies, the anomaly correlation system can determine cross-layer and cross-component correlations between selective change events and the detected anomalies of failure events.

Abstract: Methods, systems, and devices for persistent health monitoring for volatile memory devices are described. A memory device may determine that an operating condition associated with an array of memory cells on the device, such as a temperature, current, voltage, or other metric of health status is outside of a range associated with a risk of device degradation. The memory device may monitor a duration over which the operating condition is outside of the range, and may determine whether the duration satisfies a threshold. In some cases, the memory device may store an indication of when (e.g., each time) the duration satisfied the threshold. The memory device may store the one or more indications in one or more non-volatile storage elements, such as fuses, which may enable the memory device to maintain a persistent indication of a cumulative duration over which the memory device is operated with operating conditions outside of the range.

Abstract: A method for monitoring a nonlinear dynamic process and a computing system are provided. The method includes training a model based on sample data, wherein the model comprises: a first layer, wherein the first layer is configured to linearize the sample data using one or more dimension lifting techniques; a second layer, wherein the second layer is configured to extract reduced-dimension dynamic latent variables (DLVs) from the linearized sample data using a reduced-dimension model; and a third layer, wherein the third layer is configured to parameterize the extracted reduced-dimension DLVs using a latent state space model; and inputting data from the nonlinear dynamic process into the trained model for monitoring the nonlinear dynamic process.

Abstract: Systems and methods are provided for implementing system object tracking for rule violation detection. In various examples, in response to a trigger event, a computing system may track a system object by tracking a tracker object corresponding to a memory address range of a database that has been allocated to the system object. Contemporaneous with the trigger event, the computing system determines, based on tracking of the tracker object, whether a system rule associated with the system object has been violated. Based on a determination that a system rule associated with the system object has been violated, the computing system triggers at least one action associated with the system object, including performing a contemporaneous error check, sending messages indicating an error caused by violation of the system rule, identifying a system component or driver that caused the error, and/or causing an immediate system crash.

Abstract: The present application discloses a health degree evaluation method of a software product. For a software product to be evaluated, a usage condition of using the software product to be evaluated by a client may represent a health degree of the software product to be evaluated. Therefore, when the health degree of the software product to be evaluated is evaluated, usage parameters for target clients to use the software product to be evaluated may be obtained, the usage parameters may represent the condition of usage by target clients of the software product to be evaluated, wherein the usage parameters may include at least two of a usage depth parameter, a usage breadth parameter and a usage frequency parameter. After the usage parameters is obtained, the health degree of the software product to be evaluated may be determined according to the usage parameters.

Abstract: Methods, systems, and apparatuses include determining, by a memory subsystem, use model statistics for a memory portion of the memory subsystem, where the use model statistics include a count of commands received by the memory subsystem from a host system targeting the memory portion and details about the memory subsystem. It is determined that the memory subsystem is powering down. The use model statistics are stored in a use model statistics storage of the memory subsystem by the memory subsystem in response to determining that the memory subsystem is powering down.

Abstract: A computer-implemented system and method can be used to identify policy information applicable to a compute resource within a specified time range, the policy information related to data management for the compute resource. Based on the identified policy information, a set of expected data management events for the compute resource is generated within the specified time range. The set of expected data protection events is modified based on information related to exceptions to the data management indicated by the policy information. The modified set of data protection events provides lineage information for the compute resource.

Abstract: Methods, systems, and devices for error log indication via error control information are described. For instance, a memory device may transmit, to a host device, a first signal including a set of error control bits indicating that an error log of the memory device includes information for use by the host device. The memory device may receive, from the host device in response to the first signal, a second signal including a request to retrieve the information of the error log. The memory device may transmit, to the host device in response to the second signal, a third signal including the information of the error log.

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.