n this paper, we present a solution to the industrial challenge put forth by ARM in 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 Microarchitectural Denial-of-Service (DoS) attacks as aggressor workloads of the challenge and show that they can dramatically impact the latency and accuracy of the AR-HUD application. This results in significant deviations of the estimated trajec- tories from known ground truths, despite our best effort to mitigate their influence by using cache partitioning and real-time scheduling of the AR- HUD application. To address the challenge, we propose RT-Gang++, a partitioned real-time gang scheduling framework with last-level cache (LLC) and integrated GPU bandwidth throttling capabilities. By applying RT-Gang++, we are able to achieve desired level of performance of the AR-HUD application even in the presence of fully loaded aggressor tasks.
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Denial-of-Service Attacks on Shared Cache in Multicore: Analysis and Prevention
In this paper we investigate the feasibility of denialof-service (DoS) attacks on shared caches in multicore platforms. With carefully engineered attacker tasks, we are able to cause more than 300X execution time increases on a victim task running on a dedicated core on a popular embedded multicore platform, regardless of whether we partition its shared cache or not. Based on careful experimentation on real and simulated multicore
platforms, we identify an internal hardware structure of a nonblocking cache, namely the cache writeback buffer, as a potential target of shared cache DoS attacks. We propose an OS-level solution to prevent such DoS attacks by extending a state-of-the-art memory bandwidth regulation mechanism. We implement the proposed mechanism in Linux on a real multicore platform and show its effectiveness in protecting against cache DoS attacks.
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- Award ID(s):
- 1815959
- NSF-PAR ID:
- 10097575
- Date Published:
- Journal Name:
- Proceedings - IEEE Real-Time and Embedded Technology and Applications Symposium
- ISSN:
- 1545-3421
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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In this paper, we present RT-Gang: a novel realtime gang scheduling framework that enforces a one-gang-at-atime policy. We find that, in a multicore platform, co-scheduling multiple parallel real-time tasks would require highly pessimistic worst-case execution time (WCET) and schedulability analysis—even when there are enough cores—due to contention in shared hardware resources such as cache and DRAM controller. In RT-Gang, all threads of a parallel real-time task form a real-time gang and the scheduler globally enforces the one-gangat-a-time scheduling policy to guarantee tight and accurate task WCET. To minimize under-utilization, we integrate a state-of-the-art memory bandwidth throttling framework to allow safe execution of best-effort tasks. Specifically, any idle cores, if exist, are used to schedule best-effort tasks but their maximum memory bandwidth usages are strictly throttled to tightly bound interference to real-time gang tasks. We implement RT-Gang in the Linux kernel and evaluate it on two representative embedded multicore platforms using both synthetic and real-world DNN workloads. The results show that RT-Gang dramatically improves system predictability and the overhead is negligible.more » « less
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