Enhancing Model-Based Architecture Optimization with Monitored System Runs

Typically, architecture optimization searches for good architecture candidates based on analyzing a model of the system. Model-based analysis inherently relies on abstractions and estimates, and as such produces approximations which are used to compare architecture candidates. However, approximations are often not sufficient due to the difficulty of accurately estimating certain extra-functional properties. In this paper, we present an architecture optimization approach where the speed of model-based optimization is combined with the accuracy of monitored system runs. Model-based optimization is used to quickly find a good architecture candidate, while optimization based on monitored system runs further refines this candidate. Using measurements assures a higher accuracy of the metrics used for optimization compared to using performance predictions. We demonstrate the feasibility of the approach by implementing it in our framework for optimizing the allocation of software tasks to the processing cores of a multicore embedded system.