Abstract: In one embodiment, a method includes selecting a group of transactions having a common end-to-end transaction path comprising a plurality of transaction-path nodes. Each transaction of the group is associated with either an acceptable category or an unacceptable category based, at least in part, on an end-to-end response time for the transaction. The method further includes separately determining, for each transaction-path node, an execution-time pair. The execution-time pair includes first aggregate information indicative of execution time by the transaction-path node for transactions associated with the acceptable category. The execution-time pair also includes second aggregate information indicative of execution time by the transaction-path node for transactions associated with the unacceptable category. Additionally, the method includes generating a visualization of the end-to-end transaction path.

Abstract: In one embodiment, a method includes identifying at least one transaction-path node as a problem node based, at least in part, on an analysis of end-to-end response times for a group of transactions. The method further includes determining one or more event types for the at least one transaction-path node. Also, the method includes, for each of the one or more event types, inferring a first event-relevance weight from an abstract model. The method also includes, for each of the one or more event types, inferring a second event-relevance weight from a concrete model. Furthermore, the method includes, for each of the one or more event types, determining an event relevance based, at least in part, on the first event-relevance weight and the second event-relevance weight. Additionally, the method includes identifying most-relevant events among a set of active events based, at least in part, on the determined event relevance.

Abstract: In one embodiment, a method includes continuously receiving time-series data for end-user transactions occurring on one or more monitored systems. The method further includes continuously processing and storing the time-series data in a plurality of virtual machines. In addition, the method includes, responsive to a determined time-series-data burst, spawning one or more temporary virtual machines. Moreover, the method includes, during the determined time-series-data burst, continuously processing and storing the time-series data in the plurality of virtual machines and in the one or more temporary virtual machines. The method also includes, responsive to a determined conclusion of the determined time-series-data burst, causing the one or more temporary virtual machines to transition to a passive state in which the one or more temporary virtual machines cease processing and storing new time-series data but make previously-stored time-series data available for access.

Abstract: In an embodiment, a method includes accessing a call tree for a transaction, wherein the call tree traces routines called during execution of the transaction. The method further includes generating a graphical representation of the call tree in relation to two or more performance properties. In addition, the method includes causing the graphical representation of the call tree to be displayed. Further, the method includes allowing a user to graphically select a group of routines from the graphical representation of the call tree. In addition, the method includes creating a filtered call tree comprising the graphically selected group of routines. Furthermore, the method includes generating a drill-down visualization of the filtered call tree. The method also includes causing the drill-down visualization to be displayed.

Abstract: In one embodiment, a method includes operating at least one distributed software application. The method further includes receiving a web request from an end-user information handling system in connection with the at least one distributed software application. Further, the method includes generating a transaction identifier for the transaction. Moreover, the method includes processing the transaction. The processing includes generating backend-performance data indexed by the transaction identifier. Additionally, the method includes injecting the transaction identifier into a web response to the web request. Also, the method includes transmitting the web response to the end-user information handling system along a transmission path. In addition, the method includes, at a node along the transmission path, passively observing the response and generating end-user experience data therefrom.

Abstract: In one embodiment, a method includes selecting an overall set of end-user transactions. The method further includes selecting an outcome. In addition, the method includes identifying an outcome-filtered subset of the overall set, the outcome-filtered subset comprising end-user transactions of the overall set associated with the selected outcome. The method also includes, for each transaction property of a plurality of transaction properties, measuring a difference between the transaction property's prevalence in the outcome-filtered subset and the transaction property's prevalence in the overall set. The measuring yields a property-prevalence differential for each transaction property. Moreover, the method includes, based, at least in part, on the property-prevalence differentials, determining one or more of the plurality of transaction properties to be correlation factors for the selected outcome.

Abstract: In one embodiment, a method includes operating at least one distributed software application. The method further includes receiving a web request from an end-user information handling system in connection with the at least one distributed software application. Further, the method includes generating a transaction identifier for the transaction. Moreover, the method includes processing the transaction. The processing includes generating backend-performance data indexed by the transaction identifier. Additionally, the method includes injecting the transaction identifier into a web response to the web request. Also, the method includes transmitting the web response to the end-user information handling system along a transmission path. In addition, the method includes, at a node along the transmission path, passively observing the response and generating end-user experience data therefrom.

Abstract: This solution uses a statistical characterization of the transaction to predict the total effect on response time of each network component.

Abstract: A system and method are described for analyzing the performance of a network while processing an application. The method involves measuring and calculating the amount of time nodes are active processing and sending frames, as well as the amount of time that frames spend traversing the network. Graphical user interfaces are provided to effectively present significant measurements and calculations.