Abstract:
A system is provided for tracing end-to-end transactions. The system uses bytecode instrumentation and a dynamically injected agent to gather web server side tracing data, and a browser agent which is injected into browser content to instrument browser content and to capture tracing data about browser side activities. Requests sent during monitored browser activities are tagged with correlation data. On the web server side, this correlation information is transferred to tracing data that describes handling of the request. This tracing data is sent to an analysis server which creates tracing information which describes the server side execution of the transaction and which is tagged with the correlation data allowing the identification of the causing browser side activity. The analysis server receives the browser side information, finds matching server side transactions and merges browser side tracing information with matching server side transaction information to form tracing information that describes end-to-end transactions.
Abstract:
A system and method is disclosed that installs an agent on a computer system that is configured to be automatically started at startup of the computer system and that is also configured to detect the startup of processes on the computer system. The agent determines the capabilities to monitor process starts that available on the computer systems and installs an appropriate process start monitoring procedure. The installed process start monitoring procedure detects the start of a process, installs a core agent into the execution context of the started process and manipulates the start sequence of the started process to initialize and start the core agent. On startup, the core agent analyzes the execution context of the started process to identify the type of application executed by the started process, and injects a special agent matching the identified type into the started process to perform application level monitoring.
Abstract:
A system and method is disclosed that provides fail-save, scalable and clustered correlation of transaction trace data. The transaction trace data is provided by a set of agents injected into application processes and processed by a set of clustered correlation servers. Each agent monitors parts of transaction executions performed by the application process into which it is injected. The agents generate transaction trace data fragments describing the transaction execution part and report those transaction trace data fragments to the correlation cluster. Routing of transaction trace data from agents to correlation servers is performed in a way that transaction data fragments describing a specific transaction are received by one correlation server regardless of the agent sending it. Intermediate nodes may be used to server as proxy between agents and the correlation server cluster to further improve the availability and failure tolerance of the monitoring system.
Abstract:
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.
Abstract:
A system and method is disclosed that provides fail-save, scalable and clustered correlation of transaction trace data. The transaction trace data is provided by a set of agents injected into application processes and processed by a set of clustered correlation servers. Each agent monitors parts of transaction executions performed by the application process into which it is injected. The agents generate transaction trace data fragments describing the transaction execution part and report those transaction trace data fragments to the correlation cluster. Routing of transaction trace data from agents to correlation servers is performed in a way that transaction data fragments describing a specific transaction are received by one correlation server regardless of the agent sending it. Intermediate nodes may be used to server as proxy between agents and the correlation server cluster to further improve the availability and failure tolerance of the monitoring system.
Abstract:
A system and method for real-time discovery and monitoring of multidimensional topology models describing structural aspects of applications and of computing infrastructure used to execute those applications is disclosed. Different types of agents are deployed to the monitored application execution infrastructure dedicated to capture specific topological aspects of the monitored system. Virtualization agents detect and monitor the virtualization structure of virtualized hardware used in the execution infrastructure, operating system agents deployed to individual operating systems monitor resource utilization, performance and communication of processes executed by the operating system and transaction agents deployed to processes participating in the execution of transactions, providing end-to-end transaction trace and monitoring data describing individual transaction executions. The monitoring and tracing data of the deployed agents contains correlation data that allows to create a topology model of the monitored system that integrates transaction execution, process execution and communication and virtualization related aspects.
Abstract:
A system and method for real-time discovery and monitoring of multidimensional topology models describing structural aspects of applications and of computing infrastructure used to execute those applications is disclosed. Different types of agents are deployed to the monitored application execution infrastructure dedicated to capture specific topological aspects of the monitored system. Virtualization agents detect and monitor the virtualization structure of virtualized hardware used in the execution infrastructure, operating system agents deployed to individual operating systems monitor resource utilization, performance and communication of processes executed by the operating system and transaction agents deployed to processes participating in the execution of transactions, providing end-to-end transaction trace and monitoring data describing individual transaction executions. The monitoring and tracing data of the deployed agents contains correlation data that allows to create a topology model of the monitored system that integrates transaction execution, process execution and communication and virtualization related aspects.
Abstract:
A system is provided for tracing end-to-end transactions. The system uses bytecode instrumentation and a dynamically injected agent to gather web server side tracing data, and a browser agent which is injected into browser content to instrument browser content and to capture tracing data about browser side activities. Requests sent during monitored browser activities are tagged with correlation data. On the web server side, this correlation information is transferred to tracing data that describes handling of the request. This tracing data is sent to an analysis server which creates tracing information which describes the server side execution of the transaction and which is tagged with the correlation data allowing the identification of the causing browser side activity. The analysis server receives the browser side information, finds matching server side transactions and merges browser side tracing information with matching server side transaction information to form tracing information that describes end-to-end transactions.
Abstract:
A system and method is disclosed that installs an agent on a computer system that is configured to be automatically started at startup of the computer system and that is also configured to detect the startup of processes on the computer system. The agent determines the capabilities to monitor process starts that available on the computer systems and installs an appropriate process start monitoring procedure. The installed process start monitoring procedure detects the start of a process, installs a core agent into the execution context of the started process and manipulates the start sequence of the started process to initialize and start the core agent. On startup, the core agent analyzes the execution context of the started process to identify the type of application executed by the started process, and injects a special agent matching the identified type into the started process to perform application level monitoring.
Abstract:
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.