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    韩建军, 李庆华, Abbas A.Essa. 基于软件容错的动态实时调度算法[J]. 计算机研究与发展, 2005, 42(2): 315-321.
    引用本文: 韩建军, 李庆华, Abbas A.Essa. 基于软件容错的动态实时调度算法[J]. 计算机研究与发展, 2005, 42(2): 315-321.
    Han Jianjun, Li Qinghua, Abbas A.Essa. A Dynamic Real-Time Scheduling Algorithm with Software Fault-Tolerance[J]. Journal of Computer Research and Development, 2005, 42(2): 315-321.
    Citation: Han Jianjun, Li Qinghua, Abbas A.Essa. A Dynamic Real-Time Scheduling Algorithm with Software Fault-Tolerance[J]. Journal of Computer Research and Development, 2005, 42(2): 315-321.

    基于软件容错的动态实时调度算法

    A Dynamic Real-Time Scheduling Algorithm with Software Fault-Tolerance

    • 摘要: 在硬实时系统中,由于任务超时完成将会导致灾难性后果,因而硬实时系统具有严格的时间及可靠性限制条件.目前实时容错调度算法大部分针对硬件的容错,很少考虑软件运行的故障.提出了一种类似EDF基于软件容错的动态实时调度算法EBPA(expectation-based probing algorithm),该算法在任务执行过程中通过基于期望值的若干试探性检测步骤,提高了任务可执行性的预测,尽可能避免了任务早期的错误对后续任务的影响,因此提高了任务的完成率并同时有效地减少了浪费的CPU时间片.通过实验测试,同目前所知的同类算法相比,具有更佳的调度性能-调度成本比.

       

      Abstract: A hard real-time system is usually subject to stringent reliability and timing constraints due to the fact that failure to produce correct results in a timely manner may lead to a disaster. Almost all fault-tolerant scheduling algorithms at present are designed to deal with hardware faults, while less of those take possible software faults into account. Presented in this paper is a new software fault-tolerant real-time scheduling algorithm that is similar to EDF, called EBPA(expectation-based probing algorithm). The important contributions of the algorithm are probing a certain steps during the executions of primaries, which leads to improving the predictive quality of canceling ineffective primaries when heavy workload occurs and preventing early failures in execution from triggering failures in the subsequent primary executions as soon as possible. Therefore, the algorithm increases the successful percentage of tasks' completion, and meanwhile decreases the wasted CPU time slots. The simulation experiments show that the algorithm has a better trade-offs between scheduling costs and scheduling performance than the well-known algorithms so far. Moreover, some experimental parameters, such as the number of probing steps and failure probability, are also taken into account.

       

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