Synthesis of Self-Adaptable Software for Multicore Embedded Systems

This paper presents a method of synthesis of realtime software for self-adaptive multicore systems. The method assumes that the system specification is given as a task graph. Then, tasks are scheduled on multicore architecture consisting of low-power and high-performance cores. We apply the developmental genetic programming to generate the selfadaptive scheduler and the initial schedule. The initial schedule is optimized taking into consideration the power consumption, the real-time constraints as well as the self-adaptivity. The scheduler modifies the schedule, during the system execution, whenever execution time of the recently finished task occurred other than assumed during initial scheduling. We propose two models of self-adaptivity: self-optimization of power consumption and self-adaptivity of real-time scheduling. We present some experimental results for standard benchmarks, showing the advantages of our method in comparison with the worst case design used in existing approaches.