A system is disclosed for generating a graphical user interface (GUI) for network management automation. The GUI is a data view that provides functionality for monitoring and controlling devices in a network using dynamic dataview templates.

Various methods, apparatuses, and media for notifying a user of an operational state of a web application are provided. Three values that relate to data caching, error occurrence, and data retrieval are used to determine the operational state. The methods may use a software module that is compatible with any React JavaScript application.

Various aspects of the disclosure relate to test automation systems with pre-compilers to validate various steps associated with a test script. An artificial intelligence (AI)-based pre-compiler may use natural language processing (NLP) to validate various steps associated with a test script associated with an application. Other aspects of this disclosure relate to automated encryption and mocking of test input data associated with test scripts.

Server hardware failure is predicted, with a probability estimate, of a possible future server failure along with an estimated cause of the future server failure. Based on the prediction, the particular server can be evaluated and if the risk is confirmed, load balancing can be performed to move a load (e.g., virtual machines (VMs)) off of the at-risk server onto low-risk servers. High availability of deployed load (e.g., VMs) is then achieved. A flow of big data may be on the order of 1,000,000 parameters per minute. A scalable tree-based AI inference engine processes the flow. One or more leading indicators are identified (including server parameters and statistic types) which reliably predict hardware failure. This allows a telco operator to monitor cloud-based VMs and perform a hot-swap on virtual machines if needed by shifting virtual machines VMs from the at-risk server to low-risk servers. Servers having a health score indicating high risk are indicated on a visual display called a heat map. The heat map quickly provides a visual indication to the telco person of identities of at-risk servers. The heat map can also indicate commonalities between at-risk servers, such as if the at-risk servers are correlated in terms of protocols in use, if the at-risk servers are correlated in terms of geographic location, server manufacturer, server OS load, or the particular hardware failure mechanism predicted for the at-risk servers.

The field of the disclosure relates generally to a method and system for analyzing behavior of a computer infrastructure and the displaying the behavior of the computer infrastructure in a graphical manner. The system comprises an analytical engine connected to agents running on devices in the computer infrastructure and analyzing continuous data and asynchronous data.

Methods and systems are described for assessing a computer network using a graph model. In some instances, the methods comprise: receiving data from at least one data stream of a plurality of data streams, wherein the plurality of data streams comprise computer network data provided by one or more data brokers, and wherein the data received from different data streams of the plurality comprise different data formats; converting the data received from the at least one data stream to a common data format comprising a node or an edge; updating a graph model comprising a plurality of nodes and edges stored within a graph database according to the node or edge of the converted data; and providing a user of the computer network with a visualization of a status of the computer network.

Systems and methods are disclosed including a method comprising sending, by a monitored central processing unit (CPU) to a monitoring CPU of a memory sub-system controller, an address range of a logging data structure stored within a local memory component of the monitored CPU; storing, in the logging data structure by the monitored CPU, a log file comprising system state information associated with one or more tasks performed by the monitored CPU; executing, by the monitoring CPU, a data-gathering task to retrieve the log file from the logging data structure; and sending, by the monitoring CPU, the log file to a host system.

A resource usage platform is disclosed. The platform performs preemptive container load balancing, auto scaling, and placement in a computing system. Resource usage data is collected from containers and used to train a model that generates inferences regarding resource usage. The resource usage operations are performed based on the inferences and on environment data such as available resources, service needs, and hardware requirements.

A method includes receiving, at an artificial intelligence (AI) accelerator of a computing system, at least one of: manufacturer data, third-party data, sensor data, or primary usage data of a consumable in a primary device and performing an AI operation on at least one of: the manufacturer data, the third-party data, the sensor data, or the primary usage data at the AI accelerator of the computing system using an AI model. The method further includes determining a primary life expectancy of the consumable in the primary device at the AI accelerator in response to performing the AI operation.

In one embodiment, a method for providing a verbalized diagnostic message to a user of an information handling system includes: determining, by an embedded controller, an error associated with the information handling system; accessing, by the embedded controller, a memory device of the information handling system, the memory device storing a plurality of diagnostic files; selecting, by the embedded controller, a diagnostic file from the plurality of diagnostic files, the diagnostic file indicating the error associated with the information handling system; generating, by the embedded controller, the verbalized diagnostic message; sending, by the embedded controller, the verbalized diagnostic message to an audio device of the information handling system in an embedded controller audio stream; and presenting, by the audio device, the verbalized diagnostic message to the user via one or more speakers of the information handling system.