Dynatrace Corporation

Other

Herwig Moser

Ernst Ambichl

Otmar Ertl

Helmut Spiegl

Michael Kopp

Hans Kohlreiter

Bernd Greifeneder

Martin Carpella

A system and method is disclosed for the combined analysis of transaction execution monitoring data and a topology model created from infrastructure monitoring data of computing systems involved in the execution of the monitored transactions. Monitored communication activities of transactions are analyzed to identify intermediate processing nodes between sender and receiver side and to enrich transaction monitoring data with data describing those intermediate processing nodes. The topology model may also be improved by the combined analysis, as functionality and services provided by elements of the topology model may be derived by the involvement of those elements in the execution of monitored transactions. The result of the combined analysis is used by an automated anomaly detection and causality estimation system. The combined analysis may also reveal entities of a monitored environment that are used by transaction executions but which are not monitored.

Method and system for real-time correlation of disparate sources of topological information to create a unified topological model of a distributed information system

Technologies are disclosed for the automated, rule-based generation of models from arbitrary, semi-structured observation data. Context data of received observation data, like data describing the location of on which a phenomenon was observed, is used to identify related observations, to generate entities in a model describing the observed data and to assign observations to model data. Mapping rules may be used for the on-demand generation of models, and different sets of mapping rules may be used to generate different models out of the same observation data for different purposes. Further, observation time data may be used to observer the temporal evolution of the generated model. Possible use cases of the so generated models include the interpretation of observation data that describes unexpected operation conditions in view of the generated model, or to determine how a monitored system reacts on changing conditions, like increased load.

Method and system for the on-demand generation of graph-like models out of multidimensional observation data

A system and method for the aggregation and grouping of previously identified, causally related abnormal operating condition, that are observed in a monitored environment, is disclosed. Agents are deployed to the monitored environment which capture data describing structural aspects of the monitored environment, as well as data describing activities performed on it, like the execution of distributed transactions. The data describing structural aspects is aggregated into a topology model which describes individual components of the monitored environments, their communication activities and resource dependencies and which also identifies and groups components that serve the same purpose, like e.g. processes executing the same code. Activity related monitoring data is constantly monitored to identify abnormal operating conditions. Data describing abnormal operating condition is analyzed in combination with topology data to identify networks of causally related abnormal operating conditions. Causally related abnormal operating conditions are then grouped using known topological resource and same purpose dependencies. Identified groups are analyzed to determine their root cause relevance.

Forming root cause groups of incidents in clustered distributed system through horizontal and vertical aggregation

A system and method is disclosed for the automated identification of causal relationships between a selected set of trigger events and observed abnormal conditions in a monitored computer system. On the detection of a trigger event, a focused, recursive search for recorded abnormalities in reported measurement data, topological changes or transaction load is started to identify operating conditions that explain the trigger event. The system also receives topology data from deployed agents which is used to create and maintain a topological model of the monitored system. The topological model is used to restrict the search for causal explanations of the trigger event to elements of that have a connection or interact with the element on which the trigger event occurred. This assures that only monitoring data of elements is considered that are potentially involved in the causal chain of events that led to the trigger event.

Method and system for automatic real-time causality analysis of end user impacting system anomalies using causality rules and topological understanding of the system to effectively filter relevant monitoring data

A system and method is disclosed that analyzes a set of historic transaction traces to identify an optimized set of transaction clusters with the highest transaction frequency. The transaction clusters are defined according to multiple parameters describing the execution context of the analyzed transactions. The transaction clusters are described by coordinates in a multidimensional, hierarchical classification space. Descriptive statistical data is extracted from historic transactions corresponding to previously identified transaction clusters and stored as reference data. Transaction trace data from currently executed transactions is analyzed to find a best matching historic transaction cluster. The current transaction traces are grouped according to their corresponding historic transaction cluster. Statistical data is extracted from those groups of current transaction trace and statistical test are performed that compare current and historic data on a per historic transaction cluster basis to identify deviations in performance and functional behavior of current and historic transactions.

Method and system for real-time, load-driven multidimensional and hierarchical classification of monitored transaction executions for visualization and analysis tasks like statistical anomaly detection

A method is disclosed that estimates causal relationships between events based on heterogeneous monitoring data. The monitoring data consists in transaction tracing data, describing the execution performance of individual transactions, resource utilization measurements of infrastructure entities like processes or operating systems and network utilization measurement data. A topology model of the monitored environment describing its entities and the communication activities of these entities is incrementally created. The location of occurred events in the topology model is determined. The topology model is used in conjunction with a domain specific causality propagation knowledge base to calculate the possibility of causal relationships between events. Different causality determination mechanisms, based on the type of involved events are used to create graphs of causal related events. A set of root cause events, representing those events with greatest global impact on all other events in an event graph is calculated for each identified event graph.

Method and system for real-time causality and root cause determination of transaction and infrastructure related events provided by multiple, heterogeneous agents

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Freistadt, Austria

Herwig Moser