Model-Based Architecture Optimization for Self-adaptive Networked Signal Processing Systems

This short paper introduces a closed-loop design optimization method for self-organizing and self-optimizing networked systems with a focus on signal processing and control. The design process starts with creating graph-based model of the system using a dedicated modeling language. The design is exported and converted to executable code in order to obtain the properties of the runtime behavior of the system using a simulation environment. The embedding optimization loop iteratively invokes the evaluation and searches for optimal architectures and parameterization in the user defined design space. A distinguishing feature of the tool is that it allows for runtime changes in the models, i.e. it is capable of evaluating runtime reconfigurable architectures. The design space is split into two disjunct sub-spaces: one of them defines the runtime reconfigurability (the self-* capabilities), the other defines the region of design time optimization. The tool is demonstrated via a real-time monitoring application.