Bounds on Worst-Case Deadline Failure Probabilities in Controller Area Networks

Industrial communication networks like the Controller Area Network (CAN) are often required to operate reliably in harsh environments which expose the communication network to random errors. Probabilistic schedulability analysis can employ rich stochastic error models to capture random error behavio...

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Main Author: Michael Short
Format: Article
Language:English
Published: Wiley 2016-01-01
Series:Journal of Computer Networks and Communications
Online Access:http://dx.doi.org/10.1155/2016/5196092
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author Michael Short
author_facet Michael Short
author_sort Michael Short
collection DOAJ
description Industrial communication networks like the Controller Area Network (CAN) are often required to operate reliably in harsh environments which expose the communication network to random errors. Probabilistic schedulability analysis can employ rich stochastic error models to capture random error behaviors, but this is most often at the expense of increased analysis complexity. In this paper, an efficient method (of time complexity O(n log n)) to bound the message deadline failure probabilities for an industrial CAN network consisting of n periodic/sporadic message transmissions is proposed. The paper develops bounds for Deadline Minus Jitter Monotonic (DMJM) and Earliest Deadline First (EDF) message scheduling techniques. Both random errors and random bursts of errors can be included in the model. Stochastic simulations and a case study considering DMJM and EDF scheduling of an automotive benchmark message set provide validation of the technique and highlight its application.
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spelling doaj-art-c8efe632a6294a8ca92db8f89e3ea8de2025-08-20T02:19:41ZengWileyJournal of Computer Networks and Communications2090-71412090-715X2016-01-01201610.1155/2016/51960925196092Bounds on Worst-Case Deadline Failure Probabilities in Controller Area NetworksMichael Short0Electronics & Control Group, Teesside University, Middlesbrough TS1 3BA, UKIndustrial communication networks like the Controller Area Network (CAN) are often required to operate reliably in harsh environments which expose the communication network to random errors. Probabilistic schedulability analysis can employ rich stochastic error models to capture random error behaviors, but this is most often at the expense of increased analysis complexity. In this paper, an efficient method (of time complexity O(n log n)) to bound the message deadline failure probabilities for an industrial CAN network consisting of n periodic/sporadic message transmissions is proposed. The paper develops bounds for Deadline Minus Jitter Monotonic (DMJM) and Earliest Deadline First (EDF) message scheduling techniques. Both random errors and random bursts of errors can be included in the model. Stochastic simulations and a case study considering DMJM and EDF scheduling of an automotive benchmark message set provide validation of the technique and highlight its application.http://dx.doi.org/10.1155/2016/5196092
spellingShingle Michael Short
Bounds on Worst-Case Deadline Failure Probabilities in Controller Area Networks
Journal of Computer Networks and Communications
title Bounds on Worst-Case Deadline Failure Probabilities in Controller Area Networks
title_full Bounds on Worst-Case Deadline Failure Probabilities in Controller Area Networks
title_fullStr Bounds on Worst-Case Deadline Failure Probabilities in Controller Area Networks
title_full_unstemmed Bounds on Worst-Case Deadline Failure Probabilities in Controller Area Networks
title_short Bounds on Worst-Case Deadline Failure Probabilities in Controller Area Networks
title_sort bounds on worst case deadline failure probabilities in controller area networks
url http://dx.doi.org/10.1155/2016/5196092
work_keys_str_mv AT michaelshort boundsonworstcasedeadlinefailureprobabilitiesincontrollerareanetworks