Predictable dynamic embedded data processing.

Cyber-physical systems interact with their physical environment. In this interaction, non-functional aspects, most notably timing, are essential to correct operation. In modern systems, dynamism is introduced in many different ways. The additional complexity threatens timely development and reliable operation. Applications often have different modes of operation with different resource requirements and different levels of required quality-of-service. Moreover, multiple applications in dynamically changing combinations share a platform and its resources. To preserve efficient development of such systems, dynamism needs to be taken into account as a primary concern, not as a verification or tuning effort after the design is done. This requires a model-driven design approach in which timing of interaction with the physical environment is taken into consideration; formal models capture applications and their platforms in the physical environment. Moreover, platforms with resources and resource arbitration are needed that allow for predictable and reliable behavior to be realized. Run-time management is further required to deal with dynamic use-cases and dynamic trade-offs encountered at run-time. In this paper, we present a model-driven approach that combines model-based design and synthesis with development of platforms that support predictable, repeatable, composable realizations and a run-time management approach to deal with dynamic use-cases at run-time. A formal, compositional model is used to exploit Pareto-optimal trade-offs in the system use. The approach is illustrated with dataflow models with dynamic application scenarios, a predictable platform architecture and run-time resource management that determines optimal trade-offs through an efficient knapsack heuristic