Abstract: One embodiment of the present invention provides a system that automatic discovers and monitors enterprise components. During operation, the system scans an enterprise environment for an item of meta-data. Next, the system analyzes the item of meta-data to determine a monitoring instruction for a corresponding enterprise component. Finally, the system performs the monitoring instruction for the corresponding enterprise component.

Abstract: Correlations between patterns of events generated by various separate components within a cloud deployment are statistically determined. The determinations of the correlations can be based on dependencies between the components as indicated by a system topology graph including the components. The patterns correlated can be patterns of events from event streams generated by monitoring systems that monitor the components. The events in the event streams can represent changes over time in component state, behavior, or problem occurrence. Because the quantity of components within such a cloud deployment can be enormous, the quantity of events generated by the monitoring systems over a significant period of time can be voluminous.

Abstract: Systems, methods, and other embodiments associated with event driven metric data collection optimization are described. In one embodiment, a method includes providing a domain knowledge catalog that defines, for each of a plurality of source metrics: i) a plurality of target type relationships and ii) for each target type relationship, a plurality of metrics that are related to the source metric. For a particular system, a deployment topology is determined defines target entities that are included in the system, where the target entities comprise respective instances of a subset of the target type relationships. The method includes receiving configuration of an event related to a source metric. The domain knowledge catalog is accessed and metrics that are related to the subset of target type relationships for the source metric are selected for collection.

Abstract: Aspects of the present disclosure include systems and/or methods for detecting ranges of data that represent transient correlations in machine data corresponding to various hardware and/or software systems, such as enterprise systems employed by an information technology (“IT”) organization. In various aspects, the machine data may comprise one or more operational metrics that represent system performance, usage, and/or business activity of the enterprise system. The operational metrics may be used to identify operational issues within the enterprise system.

Abstract: Techniques are described for metadata-based monitoring of lifecycle operations on software deployments. In one embodiment, a set of metadata is stored in volatile or non-volatile store. The set of metadata may include a plurality of signatures and map a first signature of the plurality of signatures to a first status identifier for a first benchmark of a particular operation. A first set of log data that is associated with one or more software deployments is monitored for occurrence of the first signature. Based, at least in part, on the monitoring, a status of the first benchmark with respect to the first set of one or more software deployments is determined. Report data that indicates the status of the first benchmark is then generated and displayed.

Abstract: One embodiment of the present invention provides a system that facilitates automatically identifying components to monitor in an enterprise environment. During operation, the system receives a designation of the enterprise environment. The system then identifies an enterprise application in the enterprise environment. The system also scans the enterprise application for one or more software components, wherein a software component can include a service, a database, or any other object that provides functionality to the enterprise application. The system then adds each software component to a monitor list. Next, the system determines each hardware component hosting the enterprise application and the software components. The system then adds each hardware component to the monitor list. Finally, the system stores the monitor list to facilitate subsequent monitoring of the enterprise application.

Abstract: Techniques are described for metadata-based monitoring of lifecycle operations on software deployments. In one embodiment, a set of metadata is stored in volatile or non-volatile store. The set of metadata may include a plurality of signatures and map a first signature of the plurality of signatures to a first status identifier for a first benchmark of a particular operation. A first set of log data that is associated with one or more software deployments is monitored for occurrence of the first signature. Based, at least in part, on the monitoring, a status of the first benchmark with respect to the first set of one or more software deployments is determined. Report data that indicates the status of the first benchmark is then generated and displayed.

Abstract: Correlations between patterns of events generated by various separate components within a cloud deployment are statistically determined. The determinations of the correlations can be based on dependencies between the components as indicated by a system topology graph including the components. The patterns correlated can be patterns of events from event streams generated by monitoring systems that monitor the components. The events in the event streams can represent changes over time in component state, behavior, or problem occurrence. Because the quantity of components within such a cloud deployment can be enormous, the quantity of events generated by the monitoring systems over a significant period of time can be voluminous.

Abstract: Systems, methods, and other embodiments associated with event driven metric data collection optimization are described. In one embodiment, a method includes providing a domain knowledge catalog that defines, for each of a plurality of source metrics: i) a plurality of target type relationships and ii) for each target type relationship, a plurality of metrics that are related to the source metric. For a particular system, a deployment topology is determined defines target entities that are included in the system, where the target entities comprise respective instances of a subset of the target type relationships. The method includes receiving configuration of an event related to a source metric. The domain knowledge catalog is accessed and metrics that are related to the subset of target type relationships for the source metric are selected for collection.

Abstract: Aspects of the present disclosure include systems and/or methods for detecting ranges of data that represent transient correlations in machine data corresponding to various hardware and/or software systems, such as enterprise systems employed by an information technology (“IT”) organization. In various aspects, the machine data may comprise one or more operational metrics that represent system performance, usage, and/or business activity of the enterprise system. The operational metrics may be used to identify operational issues within the enterprise system.

Abstract: Techniques for improved metric correlation and analysis include, during a modeling phase, a user familiar with the types of system components deployed in an enterprise network and the metrics available for those types of system components specifying dependencies between metrics in a metric dependency model. During a binding phase, the metric dependency model is provided to a modified enterprise management (MEM) system which binds the model to particular enterprise system environment. During a metric correlation and analysis phase, the MEM system provides a user-interface through which a user visually compares metric data for metrics collected from system components deployed in an enterprise system environment bound to the model.

Abstract: One embodiment of the present invention provides a system that automatic discovers and monitors enterprise components. During operation, the system scans an enterprise environment for an item of meta-data. Next, the system analyzes the item of meta-data to determine a monitoring instruction for a corresponding enterprise component. Finally, the system performs the monitoring instruction for the corresponding enterprise component.

Abstract: One embodiment of the present invention provides a system that facilitates automatically identifying components to monitor in an enterprise environment. During operation, the system receives a designation of the enterprise environment. The system then identifies an enterprise application in the enterprise environment. The system also scans the enterprise application for one or more software components, wherein a software component can include a service, a database, or any other object that provides functionality to the enterprise application. The system then adds each software component to a monitor list. Next, the system determines each hardware component hosting the enterprise application and the software components. The system then adds each hardware component to the monitor list. Finally, the system stores the monitor list to facilitate subsequent monitoring of the enterprise application.