Today the performance of web systems is getting ever more important, as the number of users and competitors is still increasing. Therefore performance analysis tools gain importance too. There are currently several tools on the market that ensure and test for adequate performance. There are a number of simulation tools and monitoring tools, but only few that automatise and combine both approaches.

Performance is a key feature in many systems nowadays. There are several tools on the market that ensure and test for adequate performance. They, can be divided into simulation tools and monitoring tools. But only a few automatise and combine both approaches. This paper describes a system capable of automatically creating a web performance simulation and conducting trend analysis of the system under test (SUT). To achieve this the system requires input information, like Monitoring Points and Static-Information about the SUT. The system monitors and analyses the SUT and based on this information generates a simulation model of the system. The simulation model is refinded stepwise e.g. by adding or removing connections between the model components or adjusting the parameters until the aimed accuracy is achieved. With the help of the simulation model a prediction module creates an analysis of the SUT, and thereby can give as much information about the current state of the system and potential trends as possible. This predictive information can be used for pro-active server tuning or other performance optimisations. The focus of my PhD thesis is on the adjustment and prediction part of the system described here. For all other parts, already existing tools and techniques will be used where possible. This initial paper outlines the complete system.