Plan B: Design Methodology for Cyber-Physical Systems Robust to Timing Failures

Mohammad Khayatian, Mohammadreza Mehrabian, Edward Andert, Reese Grimsley, Kyle Liang, Yi Hu, Ian McCormack, Carlee Joe-Wong, Jonathan Aldrich, Bob Iannucci, Aviral Shrivastava

Research output: Contribution to journalArticlepeer-review

Abstract

Many Cyber-Physical Systems (CPS) have timing constraints that must be met by the cyber components (software and the network) to ensure safety. It is a tedious job to check if a CPS meets its timing requirement especially when it is distributed and the software and/or the underlying computing platforms are complex. Furthermore, the system design is brittle since a timing failure can still happen (e.g., network failure, soft error bit flip). In this article, we propose a new design methodology called Plan B where timing constraints of the CPS are monitored at runtime, and a proper backup routine is executed when a timing failure happens to ensure safety. We provide a model on how to express the desired timing behavior using a set of timing constructs in a C/C++ code and how to efficiently monitor them at the runtime. We showcase the effectiveness of our approach by conducting experiments on three case studies: (1) the full software stack for autonomous driving (Apollo), (2) a multi-agent system with 1/10th-scale model robots, and (3) a quadrotor for search and rescue application. We show that the system remains safe and stable even when intentional faults are injected to cause a timing failure. We also demonstrate that the system can achieve graceful degradation when a less extreme timing failure happens.

Original languageEnglish (US)
Article number21
JournalACM Transactions on Cyber-Physical Systems
Volume6
Issue number3
DOIs
StatePublished - Sep 7 2022

Keywords

  • Cyber-physical systems
  • time-sensitive systems
  • worst-case execution time

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Optimization
  • Artificial Intelligence

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