Scheduling for Fault-Tolerant Communication on the Static Segment of FlexRay

Bogdan Tanasa, Unmesh D. Bordoloi, P. Eles, Zebo Peng
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引用次数: 45

Abstract

FlexRay has been widely accepted as the next generation bus protocol for automotive networks. This has led to tremendous research interest in techniques for scheduling messages on the FlexRay bus, in order to meet the hard real-time deadlines of the automotive applications. However, these techniques do not generate reliable schedules in the sense that they do not provide any performance guarantees in the presence of faults. In this work, we will present a framework for generating fault-tolerant message schedules on the time-triggered (static) segment of the FlexRay bus. We provide formal guarantees that the generated fault-tolerant schedules achieve the reliability goal even in the presence of transient and intermittent faults. Moreover, our technique minimizes the required number of re-transmissions of the messages in order to achieve such fault tolerant schedules, thereby, optimizing the bandwidth utilization. Towards this, we formulate the optimization problem in Constraint Logic Programming (CLP), which returns optimal results. However, this procedure is computationally intensive and hence, we also propose an efficient heuristic. The heuristic guarantees the reliability of the constructed schedules but might be sub-optimal with respect to bandwidth utilization. Extensive experiments run on synthetic test cases and real-life case studies illustrate that the heuristic performs extremely well. The experiments also establish that our heuristic scales significantly better than the CLP formulation.
FlexRay静态网段容错通信调度
FlexRay已被广泛接受为汽车网络的下一代总线协议。这导致了对FlexRay总线上调度消息技术的巨大研究兴趣,以满足汽车应用程序的硬实时截止日期。然而,这些技术不能生成可靠的调度,因为它们不能在出现故障时提供任何性能保证。在这项工作中,我们将提出一个框架,用于在FlexRay总线的时间触发(静态)段上生成容错消息调度。我们提供了正式的保证,即使在存在瞬态和间歇性故障的情况下,生成的容错调度也能实现可靠性目标。此外,为了实现这种容错调度,我们的技术最大限度地减少了消息重传所需的次数,从而优化了带宽利用率。为此,我们提出了约束逻辑规划(CLP)中的优化问题,它返回最优结果。然而,这个过程是计算密集的,因此,我们也提出了一个有效的启发式。启发式方法保证了构建调度的可靠性,但在带宽利用率方面可能不是最优的。在合成测试用例和真实案例研究上运行的大量实验表明,启发式执行得非常好。实验还表明,我们的启发式量表明显优于CLP公式。
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