Lune

RTSS2021顶会

Resilient Mixed-Trust Scheduling

Dionisio de Niz, Björn Andersson, Hyoseung Kim, Mark H. Klein, John P. Lehoczky

2021年份

摘要

In this paper we present a new scheduling model for resilient real-time mixed trust systems. This model extends the previous Real-Time Mixed-Trust Computing framework RT-MTC to support degradation modes. Management of these modes has been identified in industrial documents as a key requirement for deploying trusted autonomous vehicles for safe autonomy. RT-MTC uses verified components (known as enforcers) to guarantee that the output of a system is safe by replacing it with a verified safe one if this output is deemed unsafe or is not produced on time. In this paper we extend RT-MTC and develop a scheduling model that uses the digraph scheduling model as a baseline but extends it in four critical ways: (1) it creates extensions for the mixed-preemptive scheduling required by RT-MTC, (2) it enables priority bands in order to separate trusted and untrusted components, (3) it uses these bands in order to calculate intermediate deadlines used by the RT-MTC framework for the scheduling of the trusted components, and (4) it defines system mode semantics to obtain two desirable properties of the new schedulability analysis: low pessimism and low time-complexity. This paper evaluates the new schedulability algorithm and shows that it is efficient in that it only needs to analyze one transition at a time. The new model supports the construction of a resilient autonomous system with provable guarantees protected by verified enforcers within the RT-MTC framework and, more importantly, preserves these guarantees even across failure-triggered mode changes. Nominal Operation MRC m MRC n Final MRC I MRM m1 Degraded Operation Capabilities Not Fully available Capabilities Fully available MRM m2 MRM n1 MRM n2 MRM I1 MRM I2 Recovery (a) MRC Modes [12] VM HV LE Untrusted

‚ a high-speed enforcer that uses a lidar to detect an object with a detection range of 20m and braking power of

问问这篇 Paper

智能体会读完全文。

Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。

可以从这些问题问起

智能体调用

Luneget_paper_fulltext

在 Lune 里问

免费开始,无需绑卡

它引用的顶会 Paper2

相关 Paper

黄昏的海面,两侧是细线勾勒的悬崖