Publikacje

Abstract:

Contemporary embedded systems work in changing environments, some features (e.g., execution time, power consumption) of the system are often not completely predictable. Therefore, for systems with strong constraints, a worst-case design is applied. We observed that by enabling the self-adaptivity we may obtain highly optimized systems still guaranteeing the high quality of service. This paper presents a method of synthesis of real-time software for self-adaptive multicore systems. The method assumes that the system specification is given as a task graph. Then, the tasks are scheduled on a multicore architecture consisting of low-power and high-performance cores. We apply the developmental genetic programming to generate the self-adaptive 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 occurs other than assumed during the 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.

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Synthesis of self-adaptable energy aware software for heterogeneous multicore embedded systems