Abstract: Performance regression between a first version of an application and a second version of the application are detected using a comparison between transaction response times measured at a plurality of resource levels of a computer system. A first set of transaction response times (for the first version) are used to determine lower and upper limit line graphs. A new line graph determined from second set of transaction response times (for the second version) can be compared with the lower and upper limit line graphs to detect a performance regression.

Abstract: An approach is provided for predicting system performance. The approach operates by identifying a Queuing Network Model (QNM) corresponding to an information technology (IT) environment that includes a number of servers that perform a plurality of parallel services. The QNM is transformed to a linear model by serializing the parallel services as sequential services. Hardware based service demands are retrieved from the system. Software-based service demands are inferred from the hardware-based service demands. Predicted performance results of the IT environment are calculated based on the hardware-based service demands, the software-based service demands inferred from the hardware-based service demands, and the system transaction rate of the system.

Abstract: An approach is provided for predicting system performance. The approach predicts system performance by identifying a Queuing Network Model (QNM) corresponding to a clustered system that handles a plurality of service demands using a plurality of parallel server nodes that process a workload for a quantity of users. A workload description is received that includes server demand data. Performance of the clustered system is predicted by transforming the QNM to a linear model by serializing the parallel services as sequential services, identifying transaction groups corresponding to each of the server nodes, and distributing the workload among the transaction groups across the plurality of nodes. The approach further solves analytically the linear model with the result being a predicted resource utilization (RU) and a predicted response time (RT).

Abstract: Performance regression between a first version of an application and a second version of the application are detected using a comparison between transaction response times measured at a plurality of resource levels of a computer system. A first set of transaction response times (for the first version) are used to determine lower and upper limit line graphs. A new line graph determined from second set of transaction response times (for the second version) can be compared with the lower and upper limit line graphs to detect a performance regression.

Abstract: A set of hypertext transfer protocol (HTTP) requests for Web application content can be routed from a client computing device to a Web server. The routed HTTP requests can be analyzed for patterns indicative of user interest in a business offering available via the Web application. An unrequested HTTP request can be conveyed to the Web server for Web application content that was not explicitly requested from the client computing device. The Web server can convey a response to the unrequested HTTP request that provides content from the Web application related specifically to the user interest. The response can cause a user interface of a Web browser of the client computing device to display the content that is related to the user interest.

Abstract: An approach is provided for predicting system performance. The approach operates by identifying a Queuing Network Model (QNM) corresponding to an information technology (IT) environment that includes a number of servers that perform a plurality of parallel services. The QNM is transformed to a linear model by serializing the parallel services as sequential services. Hardware based service demands are retrieved from the system. Software-based service demands are inferred from the hardware-based service demands. Predicted performance results of the IT environment are calculated based on the hardware-based service demands, the software-based service demands inferred from the hardware-based service demands, and the system transaction rate of the system.

Abstract: An approach is provided for predicting system performance. The approach predicts system performance by identifying a Queuing Network Model (QNM) corresponding to a clustered system that handles a plurality of service demands using a plurality of parallel server nodes that process a workload for a quantity of users. A workload description is received that includes server demand data. Performance of the clustered system is predicted by transforming the QNM to a linear model by serializing the parallel services as sequential services, identifying transaction groups corresponding to each of the server nodes, and distributing the workload among the transaction groups across the plurality of nodes. The approach further solves analytically the linear model with the result being a predicted resource utilization (RU) and a predicted response time (RT).

Abstract: A set of hypertext transfer protocol (HTTP) requests for Web application content can be routed from a client computing device to a Web server. The routed HTTP requests can be analyzed for patterns indicative of user interest in a business offering available via the Web application. An unrequested HTTP request can be conveyed to the Web server for Web application content that was not explicitly requested from the client computing device. The Web server can convey a response to the unrequested HTTP request that provides content from the Web application related specifically to the user interest. The response can cause a user interface of a Web browser of the client computing device to display the content that is related to the user interest.