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Abstract:

This paper presents a method of synthesis of self-adaptable real-time embedded systems. The method assumes that the system specification is given as a task graph. Then, tasks are scheduled on distributed architecture consisting of low-power and high-performance processors. 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 cost, 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. The goal of rescheduling is to minimize the power consumption while all time constraints are satisfied. We present some experimental results for standard benchmarks, showing advantages of our method in comparison with worst case design used in existing approaches.

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Design for Self-Adaptivity of Real-Time Embedded Systems using Developmental Genetic Programming