In this paper, we focus on two major problems in hard real-time embedded systems fault tolerance and energy minimization. Fault tolerance is achieved via both checkpointing technique and active replication strategy to tolerate multiple transient faults, whereas energy minimization is achieved by adapting Dynamic Voltage Frequency Scaling (DVFS) technique. First, we introduce an original fault-tolerance approach for hard real-time systems on multiprocessor platforms. Based on this approach, we then propose DVFS_FTS algorithm for energy-efficient fault-tolerant scheduling of precedence-constrained applications. DVFS_FTS is based on a list scheduling heuristics, it satisfies real-time constraints and minimizes energy consumption even in the presence of faults by exploring the multiprocessor architecture. Simulation results reveal that the proposed algorithm can save a significant amount of energy while preserving the required fault-tolerance of the system and outperforms other related approaches in energy savings.

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Real-time systems are becoming ever more widely used in life-critical applications, and the need for fault-tolerant scheduling can only grow in the years ahead. This article presents a novel fault tolerance approach for tolerating transient faults in hard real-time systems. The proposed approach combines both checkpointing with rollback and active replication to tolerate several transient faults. Based on this approach, a new static fault-tolerant scheduling algorithm SFTS is presented. It is based on a list of scheduling heuristics which satisfy the application time constraints even in the presence of faults by exploring the spare capacity of available processors in the architecture. Simulation results show the performance and effectiveness of the proposed approach compared to other fault-tolerant approaches. The results reveal that in the presence of multiple transient faults, the average timing overhead of this approach is lower than checkpointing technique. Moreover, the proposed algorithm SFTS achieves better feasibility rate in the presence of multiple transient faults.