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: Aspects provide a generic and adaptive approach to adaptive thresholding by using a maximum concentration interval of data to determine one or more adaptive thresholds for any type of operational metric. The generated adaptive thresholds and operational metrics may be used to calculate or otherwise perform a statistical analysis that provides a confidence-level for any changes detected in the operational metric behavior.

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: 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: Methods and apparatus for memory leak detection using clustering and trend detection are disclosed. Performance metrics are collected from an executing process. A first statistical analysis of at least one metric is used to identify trending and non-trending workload periods for the process. A second statistical analysis on the metrics for the non-trending workload periods is used to determine clusters of metrics corresponding to stable workload levels. A third statistical analysis is performed on each of the clusters to determine whether an upward trend in memory usage occurred. If an upward trend in memory usage is detected, a notification of a potential memory leak is generated.

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: A correlation relationship between two metric time series is determined after removing the impact of outlying metric values (“outliers”) that are unimportant for analytical purposes. Each of the metric time series can represent values of different system metrics obtained by mining data gathered through the monitoring of cloud deployments. The outliers can be determined based on a maximum concentration interval of the data. Removing the impact of the outliers enhances the correlation of the metric time series and provides a better representation of the correlation relationship.

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 provide a generic and adaptive approach to adaptive thresholding by using a maximum concentration interval of data to determine one or more adaptive thresholds for any type of operational metric. The generated adaptive thresholds and operational metrics may be used to calculate or otherwise perform a statistical analysis that provides a confidence-level for any changes detected in the operational metric behavior.

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: Methods and apparatus for memory leak detection using clustering and trend detection are disclosed. Performance metrics are collected from an executing process. A first statistical analysis of at least one metric is used to identify trending and non-trending workload periods for the process. A second statistical analysis on the metrics for the non-trending workload periods is used to determine clusters of metrics corresponding to stable workload levels. A third statistical analysis is performed on each of the clusters to determine whether an upward trend in memory usage occurred. If an upward trend in memory usage is detected, a notification of a potential memory leak is generated.