Abstract: An apparatus includes at least one processing device configured to obtain event metadata for events published by event sources to an event platform, the event metadata comprising static event tags for respective ones of the events. The at least one processing device is also configured to generate dynamic event tags having an association with event types based at least in part on analysis of real-time event traffic comprising a subset of the events published by the event sources to the event platform over a designated time period. The at least one processing device is further configured to train a machine learning model utilizing the static event tags and the association of the dynamic event tags with the event types, receive a query comprising event parameters, and provide a response to the query by utilizing the trained machine learning model to match events with the event parameters in the query.

Abstract: An apparatus comprises a processing device configured to obtain runtime call pattern data for microservices in an information technology infrastructure, to generate a model of the runtime call pattern data characterizing transitions between states of the microservices, and to capture point of interest events from the runtime call pattern data utilizing the generated model. The processing device is also configured to determine, for a given sliding window time slot, association rules between the captured point of interest events, a given association rule characterizing a relationship between first and second point of interest events corresponding to first and second state transitions occurring during the given sliding window time slot for first and second ones of the microservices. The processing device is further configured to control operation of the microservices in the information technology infrastructure based at least in part on the determined association rules.

Abstract: A method includes identifying at least one software update available for a given computing device, determining a state of the given computing device, and utilizing a machine-learning based predictive model to assess compatibility of the at least one software update with the given computing device based at least in part on the state of the given computing device, the machine learning-based predictive model being trained utilizing historical incident data for a plurality of incidents associated with application of software updates to a plurality of computing devices. The method also includes generating a recommendation notification indicating compatibility of the at least one software update with the given computing device, and providing the recommendation notification in conjunction with presentation of one or more user interface features controlling whether to apply the at least one software update to the given computing device.

Abstract: A method includes identifying at least one software update available for a given computing device, determining a state of the given computing device, and utilizing a machine-learning based predictive model to assess compatibility of the at least one software update with the given computing device based at least in part on the state of the given computing device, the machine learning-based predictive model being trained utilizing historical incident data for a plurality of incidents associated with application of software updates to a plurality of computing devices. The method also includes generating a recommendation notification indicating compatibility of the at least one software update with the given computing device, and providing the recommendation notification in conjunction with presentation of one or more user interface features controlling whether to apply the at least one software update to the given computing device.

Abstract: A method includes monitoring client requests to access software container instances hosted by container host devices of a geographically-distributed software container platform and identifying, for a given software container instance hosted by a first one of the container host devices, geographic clusters of the client requests. The method also includes calculating a network distance from a given one of the geographic clusters to each of at least a subset of the container host devices. The method further includes replicating the given software container instance in a second one of the container host devices responsive to determining that the calculated network distance from the given geographic cluster to the second container host device is at least a threshold amount less than the calculated network distance from the given geographic cluster to the first container host device.

Abstract: A method includes detecting an error that has occurred in one or more assets of an enterprise system and generating a visual error signature of the detected error, the visual error signature comprising at least a portion of a graph-based visualization of operation of the assets. The method also includes providing the generated visual error signature for the detected error as input to a machine learning model and utilizing the machine learning model to classify the visual error signature for the detected error as belonging to at least a given one of a plurality of error classes, the machine learning model being trained using historical visual error signatures for previously-detected errors. The method further includes identifying at least one action taken to remediate each of one or more previously-detected errors of the given error class and remediating the detected error utilizing one or more of the identified actions.

Abstract: A method comprises receiving a request to execute an instance of a given software container, determining source code entities of source code of the given software container, and generating a given software container profile for the given software container based at least in part on rankings associated with the source code entities. The method also comprises creating a resource management plan for the given software container utilizing one or more machine learning algorithms, the resource management plan comprising resource management metric thresholds determined based at least in part on historical resource utilization data for additional software containers having associated software container profiles similar to the given software container profile.

Abstract: A method is used in monitoring an application in a computing environment. The method represents execution of the application on a system as a finite state machine. The finite state machine depicts at least one state of the application, where the state indicates at least one of successful application execution and unsuccessful application execution. The method identifies an error state within the finite state machine, where the error state indicates the unsuccessful application execution. The method identifies, by analyzing the finite state machine, a non-error state as a cause of the unsuccessful application execution, where the unsuccessful application execution is represented as a path comprising a plurality of states, where the path comprises the non-error state. The method maps the non-error state to a location in the application to identify the cause of the unsuccessful application execution.

Abstract: Implementing dynamic categorization and ranking of source code entities and relationships is provided. An aspect includes scanning source code of an application, extracting source code entities from the application, and generating a hierarchical source code entity graph model from extracted source code entities. An aspect also includes scanning a project management artifact repository, extracting project management artifacts from the project management artifact repository, and generating a hierarchical project management artifact graph model from extracted project management artifacts.

Abstract: A method is used in monitoring an application in a computing environment. The method represents execution of the application on a system as a finite state machine. The finite state machine depicts at least one state of the application, where the state indicates at least one of successful application execution and unsuccessful application execution. The method identifies an error state within the finite state machine, where the error state indicates the unsuccessful application execution. The method identifies, by analyzing the finite state machine, a non-error state as a cause of the unsuccessful application execution, where the unsuccessful application execution is represented as a path comprising a plurality of states, where the path comprises the non-error state. The method maps the non-error state to a location in the application to identify the cause of the unsuccessful application execution.