In this paper, we address the industrial challenge put forth by ARM in ECRTS 2022. We systematically analyze the effect of shared resource contention to an augmented reality head-up display (AR-HUD) case-study application of the industrial challenge on a heterogeneous multicore platform, NVIDIA Jetson Nano. We configure the AR-HUD application such that it can process incoming image frames in real-time at 20Hz on the platform. We use micro-architectural denial-of-service (DoS) attacks as aggressor tasks of the challenge and show that they can dramatically impact the latency and accuracy of the AR-HUD application, which results in significant deviations of the estimated trajectories from the ground truth, despite our best effort to mitigate their influence by using cache partitioning and real-time scheduling of the AR-HUD application. We show that dynamic LLC (or DRAM depending on the aggressor) bandwidth throttling of the aggressor tasks is an effective mean to ensure real-time performance of the AR-HUD application without resorting to over-provisioning the system.

翻译：本文针对ARM在ECRTS 2022上提出的工业挑战，系统分析了共享资源争用对异构多核平台（NVIDIA Jetson Nano）上增强现实抬头显示（AR-HUD）案例研究应用的影响。我们配置AR-HUD应用，使其能够在平台上以20Hz的速率实时处理传入图像帧。采用微架构级拒绝服务（DoS）攻击作为挑战的攻击任务，结果表明，尽管我们通过缓存分区和AR-HUD应用的实时调度尽力缓解其影响，这些攻击仍能显著影响AR-HUD应用的延迟和准确性，导致估计轨迹与真实轨迹出现明显偏差。我们证明，对攻击任务进行动态LLC（或根据攻击者类型选择DRAM）带宽限制，是确保AR-HUD应用实时性能的有效手段，而无需过度配置系统资源。