Disclosed herein are a method for generating Gaussian error data using flash memory and an apparatus using the method. The method includes receiving a request to generate Gaussian error data and delivering an operation command to flash memory; generating Gaussian error noise based on a threshold voltage that is generated when the flash memory performs the operation command; and generating Gaussian error data so as to correspond to the Gaussian error noise and providing the same.

Examples of the present disclosure describe systems and methods for detecting and mitigating stack pivoting exploits. In aspects, various “checkpoints” may be identified in software code. At each checkpoint, the current stack pointer, stack base, and stack limit for each mode of execution may be obtained. The current stack pointer for each mode of execution may be evaluated to determine whether the stack pointer falls within a stack range between the stack base and the stack limit of the respective mode of execution. When the stack pointer is determined to be outside of the expected stack range, a stack pivot exploit is detected and one or more remedial actions may be automatically performed.

An example apparatus includes a first memory and a second memory coupled to the first memory. A controller may be coupled to the first memory and the second memory. The controller may be configured to cause the apparatus to be initialized by executing instructions on the first memory device. Initializing the apparatus may include operating the apparatus according to a set of semantics different than a set of semantics used by the second memory device. The controller may be configured to cause a determination regarding at least one health characteristic of the second memory to be made subsequent to the apparatus being initialized.

Techniques perform storage management. In accordance with such techniques, in response to determining that there is an abnormal process in a process group of an application, and a first size of storage space occupied by the abnormal process exceeds a first threshold, an abnormal process is terminated. A second size of storage space occupied by other processes than the abnormal process in the process group after the abnormal process is terminated is determined. In response to the second size exceeding a second threshold, at least one process in the process group to reduce the second size is terminated, where the at least one process is different from the abnormal process.

Non-limiting examples of systems, methods, and devices for automatic triaging of diagnostic failures for heterogeneous groups of tenants of a Software-as-a-Service, multi-tenant environment are disclosed herein. In an implementation, telemetry data for the heterogeneous groups of tenants is analyzed to classify individual tenant failures and detect the health status of the individual tenant. Tenant failures and/or tenant health statuses are filtered according to a threshold level. Anomalies having metrics that meet or exceed the threshold level are further analyzed to determine their priority (e.g., to a specific tenant). If the anomalies are known, then an existing entry for the anomaly is tagged and its priority may be changed. If the anomalies are unknown, then an entry is generated for the anomaly and prioritized. Tenants may be notified of a detected anomaly and may provide feedback. The feedback may be used to update triaging models.

Systems and methods are disclosed for retrieving, from a database, over a network, historical routing data for multiple attributes and determining, for each attribute, based on its respective historical routing data, whether processing volume and processing error rates for each attribute exceed respective threshold. If both processing volume and error rate exceed their respective thresholds, the systems and methods describe herein calculate, for each qualifying attribute, a degree to which routing for each attribute can be improved. The systems and methods described herein output a ranking for each qualifying attribute based on their respective degrees to which routing can be improved for the respective attributes.

Systems and methods are described identify a database metric value associated with a database instance storing a dataset associated with a user system. A database issue is detected in view of a determination that the database metric value satisfies a condition. In response to satisfaction of the condition, a set of user action metrics associated with the user system is collected from one or more data monitoring systems. At least one notification communication is generated including at least a portion of the set of user action metrics and information identifying the database issue. The at least one notification communication is transmitted to a remediation execution system configured to execute, using the at least a portion of the set of user action metrics and information identifying the database issue, a remedial action in response to the database issue.

In one embodiment, an exemplary method includes receiving multi-dimension information from a data domain operating system running on the server; determining that multiple drive failures occurred within a predetermined time frame based on the multi-dimension information; and extracting a list of system-level events and a timestamp of each event from the multi-dimension information. The method further includes determining a list of components impacted by the list of the system-level events based on the list of system-level events and the timestamp of each event; and determining one or more system-level events associated with one or more impacted components as root causes of the multiple drive failures based on the multi-dimension information. The method uses information from multiple regions of the DAE and correlate the information using a predetermined algorithm to automatically more efficiently identify one or more possible root causes of the multiple drive failures.

An abnormality detection device that detects an abnormality of a target device includes a processor that executes a process of acquiring a plurality of types of measured values of the target device, a process of calculating Mahalanobis distances of the acquired plurality of types of measured values, a process of extracting the plurality of Mahalanobis distances calculated in a past predetermined period from a point in time of evaluation of the target device and calculating a moving average value of a square value of each of the extracted Mahalanobis distances, and a process of determining whether or not an abnormality has occurred in the target device on the basis of the moving average value.

In some examples, a system receives data from peripheral devices connected to respective point-of-sale (POS) base terminals, the data captured using agents executing in the POS base terminals during periods of reduced activity of the POS base terminals. Based on processing the received data, the system determines linkage of peripheral devices to the POS base terminals, and determines, for a first POS base terminal, swapping of a first peripheral device with a second peripheral device. The system generates an output indicating that the first peripheral device has been swapped with the second peripheral device, and identifies an issue associated with a POS base terminal or a peripheral device, and trigger a remediation action to address the issue.