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In this paper, we propose a fault-tolerant scheduling real-time embedded system. This scheduling algorithm is dedicated to multi-bus heterogeneous architectures, which take as input a given system description and a given fault hypothesis. It is based on a data fragmentation and passive redundancy, which allow fast fault detection/retransmission and efficient use of buses. This scheduling approach consists of a list scheduling heuristic based on a Failure Rate Pressure. In order to maximize the reliability of the system, the scheduling of each fragmented data is depend on Failure Rate Pressure. Data fragmentation allows reliable communication and maximizes the reliability of the system. Finally, simulation results show the performance of our approach when using data fragmentation.

In this paper, the authors present a self-organized approach for scheduling tasks on processors in embedded real-time systems. For such a mapping, two conflicting objectives have to be optimized: the reliability and the schedule length. Since the scheduling problem is NP-hard, a heuristic algorithm is used to produce schedules achieving different trade-offs between the two objectives. Moreover, a self-organization strategy based on dynamic crowding distance is adopted. This allows a better exploration of the objective space as well as an enhanced solution diversity. The proposed algorithm, reliability schedule length trad-offs algorithm (RSTA), is tested and compared with the popular SPEA2 algorithm where experimental results are promising on both quality and diversity of solutions.

In this paper, we propose a fault-tolerant scheduling heuristic that achieves low energy consumption and high reliability efficiency. Our scheduling algorithm is dedicated to multi-bus heterogeneous architectures, which take as input a given system description and a given fault hypothesis. It is based on active redundancy to mask a fixed number k of failures supported in the system, so that there is no need for detecting and handling such failures. In order to maximize the system's reliability, the replicas of each operation are scheduled on different reliable processors. Finally, we show with an example that our approach can maximize reliability and reduce energy consumption when using active redundancy. 