Low-Overhead Online Assessment of Timely Progress as a System Commodity
Low-Overhead Online Assessment of Timely Progress as a System Commodity
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DOI：
10.4230/lipics.ecrts.2023.13
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Weifan Chen;Ivan Izhbirdeev;Denis Hoornaert;Shahin Roozkhosh;Patrick Carpanedo;Sanskriti Sharma;R. Mancuso
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文献类型：
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作者：
Weifan Chen;Ivan Izhbirdeev;Denis Hoornaert;Shahin Roozkhosh;Patrick Carpanedo;Sanskriti Sharma;R. Mancuso
安全 - 关键系统的正确性取决于其逻辑和临时行为。检测违反控制流及时性的完整问题。随着我们的嵌入式系统的复杂性，随着内存层次结构的关键资源而被数个CPU和加速器共享，它们变得很难预测软件和硬件组件之间的精确相互作用。和理由如何恢复对及时的进步的控制？评估（TPA）。简而在商业平台上的开销和可行性。 0.6％的时间开销用于测试的应用。干扰共享内存资源。
The correctness of safety-critical systems depends on both their logical and temporal behavior. Control-flow integrity (CFI) is a well-established and understood technique to safeguard the logical flow of safety-critical applications. But unfortunately, no established methodologies exist for the complementary problem of detecting violations of control flow timeliness. Worse yet, the latter dimension, which we term Timely Progress Integrity (TPI), is increasingly more jeopardized as the complexity of our embedded systems continues to soar. As key resources of the memory hierarchy become shared by several CPUs and accelerators, they become hard-to-analyze performance bottlenecks. And the precise interplay between software and hardware components becomes hard to predict and reason about. How to restore control over timely progress integrity? We postulate that the first stepping stone toward TPI is to develop methodologies for Timely Progress Assessment (TPA). TPA refers to the ability of a system to live-monitor the positive/negative slack – with respect to a known reference – at key milestones throughout an application’s lifespan. In this paper, we propose one such methodology that goes under the name of Milestone-Based Timely Progress Assessment or MB-TPA, for short. Among the key design principles of MB-TPA is the ability to operate on black-box binary executables with near-zero time overhead and implementable on commercial platforms. To prove its feasibility and effectiveness, we propose and evaluate a full-stack implementation called Timely Progress Assessment with 0 Overhead (TPAw0v). We demonstrate its capability in providing live TPA for complex vision applications while introducing less than 0.6% time overhead for applications under test. Finally, we demonstrate one use case where TPA information is used to restore TPI in the presence of temporal interference over shared memory resources.