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A Fault Injection Approach to Evaluate Soft-Error Dependability of System Calls

机译:一种故障注入方法,用于评估系统调用的软错误可靠性

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Central Processing Units (CPUs) that satisfy the throughput demands of highly automated driving trade reliability off for performance. Such CPUs often do not include extensive hardware-implemented reliability measures e. g., lockstep CPU cores. At the same time, POSIX-compliant (including Linux-like) operating systems (OSs) become increasingly popular for such complex automotive systems, e. g., the upcoming AUTOSAR Adaptive standard is based on POSIX [1]. In such systems, the fault analysis of critical software components such as the OS becomes an important dependability asset. We determine the robustness of a given OS by injecting random hardware faults into the CPU and measure the extent to which these faults propagate through the OS in order to manifest as application level side effects. In this paper, we present our QEMU-based fault injection framework that simulates bit flips in x86 registers during the execution of the system calls of Linux 4.10 and classifies their effects at the application level. Our results show that for the clone, futex, mmap, mprotect, and pipe syscalls in average 76.3% of the 4.48 million injected faults are benign.Our experiments also show that the program counter and stack pointer (in case of memory operations) are the most susceptible registers. Our measurements help to guide the appropriate deployment of software-implemented hardware fault-tolerance (SIHFT) measures. Re-evaluation of the implemented SIHFT measures can be potentially used as an argument for safety.
机译:满足高度自动化驾驶的吞吐量需求的中央处理器(CPU)牺牲了性能的可靠性。这样的CPU通常不包括广泛的硬件实现的可靠性措施e。 g。锁步CPU内核。同时,对于这种复杂的汽车系统,例如POSIX兼容(包括类似Linux)的操作系统(OS)变得越来越流行。例如,即将到来的AUTOSAR自适应标准基于POSIX [1]。在这样的系统中,对关键软件组件(例如OS)的故障分析成为重要的可靠性资产。我们通过将随机的硬件故障注入CPU来确定给定OS的健壮性,并测量这些故障在OS中传播的程度,以体现为应用程序级别的副作用。在本文中,我们介绍了基于QEMU的故障注入框架,该框架在执行Linux 4.10的系统调用期间模拟x86寄存器中的位翻转,并在应用程序级别对它们的影响进行分类。我们的结果表明,对于克隆,futex,mmap,mprotect和管道syscalls,在448万个注入的故障中平均有76.3%是良性的;我们的实验还表明,程序计数器和堆栈指针(在内存操作的情况下)是最易受影响的寄存器。我们的测量有助于指导软件实施的硬件容错(SIHFT)措施的适当部署。对已实施的SIHFT措施的重新评估可以潜在地用作安全性的依据。

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