Energy efficient mapping of mixed criticality applications on unrelated heterogeneous multicore platforms

Heterogeneous multicore platforms are becoming an attractive choice to deploy mixed criticality systems demanding diverse computational requirements. One of the major challenges is to efficiently harness the computational power of these multicore platforms while deploying mixed criticality applications with timeliness properties. Energy efficiency is also one of the desired requirements in the design phase, and therefore it is often difficult for the system designer to simultaneously satisfy those sometimes contradictory requirements. In this paper, we propose a novel partitioning algorithm for unrelated heterogeneous multicore platforms to map mixed criticality applications. The algorithm not only ensures the timeliness in different modes of execution but also tries to allocate the applications to their energy-wise favourite cores. We considered a realistic power model that further increases the relevance of the proposed approach. We have performed an extensive set of experiments to evaluate the performance of the proposed approach, and we show that in the best-case, we achieve a 23.8% gain in the average power dissipation over the state-of-the-art partitioned algorithm. Our proposed algorithm also has a better weighted schedulability when compared to the existing partitioned algorithms.