Aspects of the disclosure describe methods and systems for cross-referencing forensic snapshots over time. In one exemplary aspect, a method may comprise receiving a first snapshot of a computing device at a first time and a second snapshot of the computing device at a second time and applying a pre-defined filter to the first snapshot and the second snapshot, wherein the pre-defined filter includes a list of files that are to be extracted from each snapshot. The method may comprise subsequent to applying the pre-defined filter, identifying differences in the list of files extracted from the first snapshot and the second snapshot. The method may comprise creating a change map for the computing device that comprises the differences in the list of files over a period of time, wherein the period of time comprises the first time and the second time, and outputting the change map in a user interface.

A system includes a cluster of nodes, memory, and a processor, where the cluster includes an application programming interface (API) server and one or more components. The processor is configured to initialize an interface to the API server, where the interface is operable to send status information from the one or more components within the cluster via a single output stream. The API server is configured to modify the single output stream of the API server to output status information associated with a first component of the one or more components within the cluster. The status information is aggregated and it is determined whether the cluster is at a failure point. In response to determining that the cluster is at a failure point, an execution signal is set to false, where the execution signal is accessible to an automation tool in communication the cluster.

In an example in accordance with the present disclosure, a system is described. The system includes a data collector to collect usage data for the electronic device over a first period of time. The system also includes a model generator. The model generator extrapolates usage data for the electronic device over a second period of time that is longer than the first period of time and predicts a state of the electronic device based on extrapolated usage data for the electronic device over the second period of time.

A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: monitoring data center assets within a data center; identifying an issue within the data center, the issue being associated with an operational situation associated with a particular component of the data center; determining whether data associated with the issue corresponds to predefined conditional criteria; and, triggering an autonomous widget operation in response to a determination of the data associated with the issue corresponding to the predefined conditional criteria, the autonomous widget operation executing a particular autonomous widget.

Various embodiments herein each include at least one of systems, methods, software, and devices for edge system health monitoring and auditing. One embodiment, in the form of a method includes performing a system audit over a first network of devices deployed within the facility to determine a status of each respective device. This embodiment further includes determining an overall system status for the facility based on results of the system audit including consideration of a status of each of the devices deployed within the facility and storing data representative of the overall system status of the facility. This embodiment also transmits at least a portion of the data representative of the overall system status of the facility over a second network to a facility system status monitoring application which may then present a single indicator of the overall system status or health.

Techniques for enabling secure access to data using data blocks is described. Computing device(s) can provide instruction(s) to a component associated with an entity, wherein the instruction(s) are associated with an identifier corresponding to a data block of a plurality of data blocks. The computing device(s) can receive, from the component, data associated with the component, wherein the data is associated with the identifier and is indicative of a state of the component. The computing device(s) can store the data in the data block and monitor, using rule(s), changes to the state of the component based at least partly on the data in the data block. As a result, techniques described herein enable near real-time—and in some examples, automatic—reporting and/or remediation for correcting changes to the state of the component using data that is securely accessed by use of data blocks.

A method for protecting data in a storage system is disclosed. In one embodiment, such a method includes detecting, by a first hardware management console, first battery-on status associated with a first uninterruptible power supply. The method further detects, by a second hardware management console, second battery-on status associated with a second uninterruptible power supply. The method communicates, from the first hardware management console to the second hardware management console, the first battery-on status. The method then triggers, by the second hardware management console, a dump of modified data from memory to more persistent storage upon detecting both the first battery-on status and the second battery-on status. A corresponding system and computer program product are also disclosed.

One example method includes accessing I/O traces, generating parameters based on the I/O traces, and defining an autoencoder deep neural network, training the autoencoder deep neural network using the parameters, collecting and storing new I/O traces, computing an encoded features difference series using the new I/O traces, detecting breakpoints in the encoded features difference series, evaluating a utility of the breakpoints, and performing an action based on the breakpoint utility evaluation.

The present invention relates to computerized (“smart”) mobile electronic devices and more particularly, to a system and methods of diagnosing and repairing malfunctions in smart mobile electronic devices, including a diagnostic process that utilizes decisions based on Big Data that holds information of multiple devices and offers a “disable components” (i.e., turn-off components) solution in order to overcome the problem without flashing a firmware or doing a factory-reset.

A system for generating a robustness score for hardware components, nodes, and clusters of nodes in a computing infrastructure is provided. The system includes a memory and at least one processing device coupled to the memory. The processing device is to obtain first telemetry data associated with a selected portion of a computing infrastructure, and the selected portion includes a first node and a first hardware component. The processing device is further to obtain first metadata associated with the selected portion, input one or more telemetry inputs corresponding to the first telemetry data into a machine learning model, input one or more metadata inputs corresponding to the first metadata into the machine learning model, and generate, from the machine learning model, a first robustness score for the first hardware component representing a health state of the first hardware component.