A system and method for reducing incident alerts for an enterprise environment are described. In one embodiment, a method of reducing incident alerts for an enterprise environment includes receiving a plurality of historical incident alerts associated with previous incidents associated with nodes within an enterprise environment. The method includes extracting from a first subset of the historical incident alerts a plurality of rules to generate a rule knowledge base and analyzing a second subset of the historical incident alerts against the plurality of rules to identify candidate incidents alerts as potential dead-end tickets. The method also includes providing feedback on the candidate incident alerts to confirm or deny that the alert is a dead-end ticket. Based on the feedback, a prescriptive avoidance rule set is generated to identify an incident alert as a dead-end ticket and eliminate the dead-end tickets from submitted incident alerts.

Methods, apparatus, and processor-readable storage media for automatically predicting device failure using machine learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from at least one client device; predicting failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques; predicting lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and performing at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.

Systems, methods, and computer-readable storage media configured to predict future system failures are disclosed. Performance metrics (e.g., key performance indicators (KPIs)) of a system may be monitored and machine learning techniques may utilize a trained model to evaluate the performance metrics and identify trends in the performance metrics indicative of future failures of the monitored system. The predicted future failures may be identified based on combinations of different performance metrics and the impact that the performance metric trends of the group of different performance metrics will have on the system in the future. Upon predicting that a system failure will occur, operations to mitigate the failure may be initiated. The disclosed embodiments may improve overall performance of monitored systems by: increasing system uptimes (i.e., availability); helping systems administrators maintain the monitored systems in a healthy state; and ensuring the functionality those systems provide is readily available to system users.

An information processing device includes control circuitry. The control circuitry is configured to store, in a first memory, record information formed each time a predetermined event occurs in a device and perform an update process of successively updating an old piece of record information with a new piece of record information in the record information stored in the first memory; transmit the record information stored in the first memory via a first signal line; transfer communication abnormality record information stored in the first memory to a second memory, which is configured not to update record information, and store the communication abnormality record information in the second memory when a communication abnormality signal of the first signal line is supplied via a second signal line; and transmit the communication abnormality record information stored in the second memory via the first signal line when communication of the first signal line is restored.

A method for Serial Peripheral Interface (SPI) operating-mode synchronization between an SPI host and an SPI device, which communicate over an SPI bus, includes predefining, in the SPI device, one or more values on the SPI bus as indicative of lack of synchronization of an SPI operating mode between the SPI host and the SPI device. Re-synchronization of the SPI operating mode is initiated in response to receiving any of the predefined values in the SPI device.

A system and method are described for flushing a specified region of a memory side cache (MSC) within a multi-level memory hierarchy. For example, a computer system according to one embodiment comprises: a memory subsystem comprised of a non-volatile system memory and a volatile memory side cache (MSC) for caching portions of the non-volatile system memory; and a flush engine for flushing a specified region of the MSC to the non-volatile system memory in response to a deactivation condition associated with the specified region of the MSC.

A method for detecting an anomaly includes retrieving a log file that includes log entries, grouping the log entries into clusters of log entry types based on number of occurrences and average time interval, and discovering a sequence of the log entry types within each of the clusters. The sequence of the log entry types is based on a shortest path from a first one of the log entry types to a last one of the log entry types.

Embodiments of the present disclosure provide a terminal verification method, a terminal device and a computer readable storage medium. The method includes: reading machine verification parameters of a target terminal, determining that the target terminal is incompletely configured in response to determining that the machine verification parameters of the target terminal satisfy a first condition and a second condition, and displaying on a user interface of the target terminal that, the target terminal is incompletely configured. The machine verification parameters of the target terminal include N terminal configuration parameters. The first condition includes abnormally reading the GPU manufacturer parameter and the GPU renderer parameter. The second condition includes normally reading a first group of terminal configuration parameters of the machine verification parameters of the target terminal. The first group of terminal configuration parameters includes (N2) terminal configuration parameters.

Disclosed herein are methods and systems enabling automatic powering on and off of a computer of a user when the user is within a predetermined range from the computer. When there is a startup error detected during the remote powering on process of the computer, an alert is generated and automatically transmitted to an analyst computer to resolve the startup error. The disclosed systems and methods save a lot of time for the user each day as the user does not have to wait for execution of computer startup processes and the computer is ready to use when the user arrives at a location of their computer.

A method and system that efficiently selects sensors without requiring advanced expertise or extensive experience even in a case of new machines and unknown failures. An abnormality detection system includes a storage unit for storing a latent variable model and a joint probability model, an acquisition unit for acquiring sensor data that is output by a sensor, a measurement unit for measuring the probability of the sensor data acquired by the acquisition unit based on the latent variable model and the joint probability model stored by the storage unit, a determination unit for determining whether the sensor data is normal or abnormal based on the probability of the sensor data measured by the measurement unit, and a learning unit for learning the latent variable model and the joint probability model based on the sensor data output by the sensor.