A computer of a resource monitoring system is configured to execute an application for analyzing an operation of an industrial device. A use status acquisition module is configured to acquire a use status of a resource of the computer by the application. A display control module is configured to display the use status on a resource monitoring screen.

A system and method for determining automated uptime of internet-based software may include one or more memories storing check suites, each check suite including an ordered sequence of a plurality of checks corresponding to an execution flow of an ordered sequence of functions of the internet-based software; and one or more processors configured to determine a total amount of down time for a monitoring period by: during each time interval of the monitoring period, performing each of the plurality of check suites on the internet-based software, and applying at least one weighing factor to results of performing each of the plurality of check suites.

There is disclosed a method and system for operating an event-driven architecture. The event-driven architecture comprises a first machine-learning (ML) agent operating a first service and a second ML agent operating a second service. The first ML agent comprises a first model and first model metadata. The second ML agent comprises a second model and second model metadata. The method comprises generating, by the first ML agent, an event associated with event metadata. The event comprises results generated by the first model. The event metadata comprises an event identifier (ID). The first ML agent publishes the event in a virtualized dedicated space. The second ML agent determines whether the event is to be processed by the second ML agent. If a determination is made that the message is to be processed by the second ML agent, the second ML agent processes the event to generate an output.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for executing efficient and techniques for generating automated testing workflow data entities based at least in part on session data entities, integrating nestable automated testing workflow data entities based at least in part on session data entities into integrative automated testing workflow data entities based at least in part on session data entities, and generating execution longs for automated testing workflow data entities based at least in part on session data entities.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for executing efficient and reliable techniques for software test execution planning by utilizing at least one of static execution plan data entities, dynamic execution plan data entities, worksheet execution run data entities, automated execution run data entities, and manual execution run data entities.

A method of representing a distributed computing system, the distributed computing system comprising a plurality of processing devices connected together according to a predefined topology. The method comprising receiving at least one piece of data from an activity log file relating to at least one processing device among the plurality of processing devices, receiving at least one metric relating to at least one processing device among the plurality of processing devices, receiving at least the predefined topology of the distributed computing system, constructing a graph representative of a distributed computing system operation, the graph comprising the data item extracted from the received log file, the received metric, and the received topology, and embedding at least one part of the graph to obtain at least one state vector representing the at least one part of the embedded graph.

Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises generating a baseline prediction for each database in a plurality of databases, wherein the generated baseline prediction includes datapoints for each performance feature of a respective database in the plurality of databases; generating a linkage matrix using a dimension reduction algorithm in an equal-dimensional feature space based on a measured difference between actual performance datapoints of each respective database and respective datapoints associated with generated baseline prediction for each database in the plurality of databases; linking one or more databases in the plurality of databases based on the measured difference within the generated linkage matrix reaching a predetermined threshold of measured performance; and generating a design using the linked databases for a computer system capable of being displayed through a user interface.

The discussion relates to automatically providing information about what code sequences contribute to a length of time a program takes to execute. One example can collect context switch and ready thread event tracing data from a program over a period of interest and identify time blocks of program threads from the period of interest. The example can distinguish individual time blocks that contribute to execution time for the period of interest from other individual time blocks that do not contribute to the execution time.

Indications of a plurality of events whose occurrence is detected in a particular execution of a program are obtained. One or more partitions of a trace object corresponding to the execution are constructed, including a first partition corresponding to a first subset of the events. The first partition comprises a header portion which includes a compressed representation of one or more event chains, and a data portion comprising a compressed events record indicating an occurrence, during the execution, of a particular sequence of events indicated by an event chain. The trace object is stored.

A graph-based data multi-operation system includes a data multi-operation management subsystem coupled to an application and accelerator subsystems. The data multi-operation management subsystem receives a data multi-operation graph from the application that identifies first data and defines operations for performance on the first data to transform the first data into second data. The data multi-operation management subsystem assigns each of the operations to at least one of the accelerator systems, and configures the accelerator subsystems to perform the operations in a sequence that transforms the first data into the second data, When the data multi-operation management subsystem determine a completion status for the performance of the operations by the accelerator subsystems, it transmits a completion status communication to the application that indicates the completion status of the performance of the plurality of operations by the plurality of accelerator subsystems.