This thesis proposes an approach to integrate the use of time-related stochastic properties in a continuous design process based on models at runtime. Time-related specification of services are an important aspect of component-based architectures, for instance in distributed, volatile networks of computer nodes. The models at runtime approach eases the management of such architectures by maintaining abstract models of architectures synchronized with the physical, distributed execution platform. For self-adapting systems, prediction of delays and throughput of a component assembly is of utmost importance to take adaptation decision and accept evolutions that conform to the specifications. To this aim we define a metamodel extension based on stochastic Petri nets as an internal time model for prediction. We design a library of patterns to ease the specification and prediction of common time properties of models at runtime and make the synchronization of behaviors and structural changes easier. Furthermore, we apply the approach of Aspect-Oriented Modeling to weave the internal time models into timed behavior models of the component and the system. Our prediction engine is fast enough to perform prediction at runtime in a realistic setting and validate models at runtime.
