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: To model a computing entity, information relating to transactions associated with the computing entity is received. The received information forms a collection of information. The collection is segmented into a plurality of segments, and at least one anomalous segment is identified. A model of the computing entity is built.

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: A method for measuring business transaction performance, includes the steps of, at a top-level component, assigning a correlation tag and original time stamp to a server request, passing the original time stamp with any server requests from the top-level component to one or more subsequent components, and computing an aggregate latency over a predetermined interval at the top-level component and at each subsequent component based on the original time stamp.

Abstract: A method for measuring business transaction performance, includes the steps of, at a top-level component, assigning a correlation tag and original time stamp to a server request, passing the original time stamp with any server requests from the top-level component to one or more subsequent components, and computing an aggregate latency over a predetermined interval at the top-level component and at each subsequent component based on the original time stamp.

Abstract: To model a computing entity, information relating to transactions associated with the computing entity is received. The received information forms a collection of information. The collection is segmented into a plurality of segments, and at least one anomalous segment is identified. A model of the computing entity is built.

Abstract: A method, system, and data structure are disclosed for monitoring transaction performance in a managed computer network environment having a requesting component configured to request a transaction of a servicing component. In accordance with exemplary embodiments the transaction is classified into a transaction type. A correlator is generated for the transaction including information identifying a parent transaction type and a root transaction type of the transaction. An events associated with a completion of the transaction is recorded. Performance statistics are aggregated on the transaction completion event in the context of the information included in the correlator.

Abstract: A method, system, and data structure are disclosed for monitoring transaction performance in a managed computer network environment having a requesting component configured to request a transaction of a servicing component. In accordance with exemplary embodiments the transaction is classified into a transaction type. A correlator is generated for the transaction including information identifying a parent transaction type and a root transaction type of the transaction. An events associated with a completion of the transaction is recorded. Performance statistics are aggregated on the transaction completion event in the context of the information included in the correlator.

Abstract: Methods and systems that adaptively measure and collect performance data are disclosed. An exemplary method includes acquiring system performance data in accordance with a performance policy. Then, data measurement and collection is dynamically adjusted based on the performance policy and a detected system condition. An alternative method includes establishing a self-monitoring data collection policy. At least one system performance metric is then measured in accordance with the self-monitoring data collection policy. The at least one system performance metric is evaluated to determine system health. The system performance metric measuring step is then dynamically adjusted based on the self-monitoring data collection policy and the system health. The steps of measuring, evaluating and dynamically adjusting are then repeated.

Abstract: Methods and systems that adaptively measure and collect performance data are disclosed. An exemplary method includes acquiring system performance data in accordance with a performance policy. Then, data measurement and collection is dynamically adjusted based on the performance policy and a detected system condition. An alternative method includes establishing a self-monitoring data collection policy. At least one system performance metric is then measured in accordance with the self-monitoring data collection policy. The at least one system performance metric is evaluated to determine system health. The system performance metric measuring step is then dynamically adjusted based on the self-monitoring data collection policy and the system health. The steps of measuring, evaluating and dynamically adjusting are then repeated.