Oracle International Corporation

Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Brent Arthur Enck

Dustin R Garvey

Sampanna Shahaji Salunke

Sumathi Gopalakrishnan

Uri Shaft

Timothy Mark Frazier

Eric L Sutton

John M Green

John Branson Bley

Amit Ganesh

Ludmila Cherkasova

Mehmet Kivanc Ozonat

Vladimir Volchegursky

Thyagaraju Poola

Michael Haeuptle

Neil Goodman

Avi Huber

Steven E Smead

Constantinos Papadopoulos

James A Knoop

David S Flood

Andrea Martin

Mark Sturdevant

Generating persistent multifaceted statistical distributions of event data associated with computing nodes is disclosed. From a data stream, events are identified that occur during a first time interval. Characteristics associated with the events are determined. Based on a primary characteristic, it is determined that an event corresponds to an event cluster. The event count for that cluster is incremented. It is determined that the characteristics correspond to an event descriptor of events in the cluster. Responsive to requests to view the event cluster, information about descriptors from the cluster are displayed indicating events having a particular event descriptor, or a summary of characteristics that distinguish the descriptor from other event descriptors.

Streaming method for the creation of multifaceted statistical distributions

Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.

Automatic behavior detection and characterization in software systems

Techniques for efficiently generating aggregate distribution approximations are disclosed. In some embodiments, a system receives a plurality of piecewise approximations that represent different distributions of a set of values on at least one computing resource. Based on the plurality of piecewise approximations, a set of clusters are generated, within volatile or non-volatile memory, that approximate an aggregate distribution of the set of metric values on the at least one computing resource. The set of clusters is transformed, within volatile or non-volatile memory, to an aggregate piecewise approximation of a function for the set of metric values on the at least one computing resource.

Methods and systems for aggregating distribution approximations

Techniques for generating distribution approximations with low memory footprints are disclosed. In some embodiments, a system receives a first set of values that measure one or more metrics of at least one computing resource. A set of clusters are generated, within volatile or non-volatile memory, that approximate a distribution of the first set of values measuring the one or more metrics of the at least one computing resource. The set of clusters is transformed, within volatile or non-volatile memory, to a piecewise approximation of a function for the first set of values.

Scalable methods and systems for approximating statistical distributions

Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.

Systems and methods for automatically detecting, summarizing, and responding to anomalies

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.

Systems and methods for adaptive thresholding using maximum concentration intervals

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.

Modeling a computing entity

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.

Method and apparatus for measuring business transaction performance

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.

Method, system, and data structure for monitoring transaction performance in a managed computer network environment

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.

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Roseville, CA, United States of America

Brent Arthur Enck