Knowledge Based Optimization for Distributed Real-Time Systems

The design and the implementation of distributed real-time systems has always been a challenging task. A central question being how to efficiently coordinate parallel activities by means of point-to-point communication so as to keep global consistency while meeting timing constraints. In the domain of safety critical applications, system predictability allows to pre-compute optimal scheduling policies. In this paper, we consider a larger class of systems represented as compositions of timed automata subject to multiparty interactions, for which an implementation method for distributed platforms and based on intermediate model transformation already exists. To improve this approach, we developed specific static analysis techniques that, combined with local and global knowledge of the system, checks particular conditions that enables to decrease the number of messages exchanged in the system for executing each interaction, as well as to remove unnecessary scheduling overhead in some cases.