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Lifetime-aware real-time task scheduling on fault-tolerant mixed-criticality embedded systems

机译：容错混合关键嵌入式系统上的基于生命周期的实时任务调度

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摘要

In recent years, the design of mixed-criticality embedded systems suffering from transient faults has attracted much attention. From the perspective of system users, it is desirable to optimize system lifetime while meeting all design constraints. Existing task scheduling algorithms cannot be utilized to maximize the lifetime of mixed-criticality embedded systems since they do not take into account the impact of providing transient fault tolerance on system lifetime. This paper investigates the problem of prolonging the lifetime of mixed-criticality embedded systems on a uniprocessor equipped with dynamic voltage and frequency scaling (DVFS) technique. The transient faults and thermal cycling incurred permanent faults are simultaneously considered in the system lifetime optimization under the constraints of safety requirements and schedule timeliness. A mixed-integer linear programming (MILP) formulation is first presented to deal with the task scheduling problem. Since the MILP method is a time-consuming solution for large-scale systems, a cross-entropy method based heuristic is then proposed to achieve a better tradeoff between the system lifetime achieved by the derived task schedule and the runtime consumed to generate the task schedule. Experiments based on synthetic and real-world benchmarks are conducted, and simulation results demonstrate that the proposed heuristic improves system lifetime by up to 32.73% with acceptable runtime as compared to benchmarking methods. (C) 2019 Elsevier B.V. All rights reserved.
机译：近年来，遭受瞬时故障的混合临界嵌入式系统的设计引起了广泛的关注。从系统用户的角度来看，希望在满足所有设计约束的同时优化系统寿命。现有的任务调度算法不能用于最大化混合关键嵌入式系统的寿命，因为它们没有考虑提供瞬态容错对系统寿命的影响。本文研究了在配备动态电压和频率缩放（DVFS）技术的单处理器上延长混合临界嵌入式系统寿命的问题。在安全性要求和时间表及时性的约束下，在系统寿命优化中同时考虑了瞬态故障和热循环引起的永久性故障。首先提出了混合整数线性规划（MILP）公式来处理任务调度问题。由于MILP方法是大规模系统的耗时解决方案，因此提出了一种基于交叉熵的启发式方法，以在派生任务计划实现的系统寿命与生成任务计划所消耗的运行时间之间取得更好的折衷。进行了基于综合基准和实际基准的实验，仿真结果表明，与基准方法相比，该启发式方法在可接受的运行时间下将系统寿命提高了32.73％。 （C）2019 Elsevier B.V.保留所有权利。

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