Performance is one of the most important non-functional properties of computer and communication networks. The first step towards the control and optimization of performance is the ability to evaluate the performance.
One of the most common ways to evaluate the performance is to measure it. However, direct measurements at real systems are only possible if the system is already available. Additionally, doing optimizations based on measurements often involves costly trial-and-error cycles.
Therefore, our evaluations are based on formal mathematical models of the real system instead. Here, we mainly focus on analytic closed-form solutions, iterative numerical methods, and discrete-event simulation techniques for models based on stochastic processes, like Markov chains, as well as high-level model description methods, like queueing networks and stochastic Petri nets.
Especially for complex and self-organizing systems, we also employ models based on differential equations well-known in Physics and Biology research.
MOSEL-2 - Performance Modeling Language and Evaluation Environment
