Research on Performance Engineering

Large software systems are often designed with limited explicit consideration of the implementation’s performance (e.g., response times, resource utilization). This ‘fix-performance-later’ attitude can lead to expensive redesigns in late software development stages. Model-based performance prediction methods try to assess the performance properties of systems already during early development stages. They rely on well-known mathematical formalisms, such as queueing networks, stochastic Petri nets or stochastic process algebra. Performance prediction based on numerical analysis or simulation can reveal potential bottlenecks in a software design and helps software architects to improve architectural designs.

Layered Queueing Network with derived performance prediction (Franks1996)

Introductory papers to research on performance engineering:

My PhD thesis focused on performance prediction for component-based software systems. I developed a parameterizable performance specification formalism for software components, which was later incorporated into the Palladio Component Model (PCM). Recent works originate from Anne’s PhD thesis on automatically improving architectural models for performance using evolutionary algorithms.

Palladio Component Model instance with predicted Pareto-front (performance vs. reliability vs. costs) (Martens2010)

Selected papers with my participation:

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