research-article

JAMS-SG: A Framework for Jitter-Aware Message Scheduling for Time-Triggered Automotive Networks

Authors:

Vipin Kumar Kukkala,

Sudeep Pasricha,

Thomas BradleyAuthors Info & Claims

ACM Transactions on Design Automation of Electronic Systems (TODAES), Volume 24, Issue 6

Article No.: 63, Pages 1 - 31

https://doi.org/10.1145/3355392

Published: 17 September 2019 Publication History

Abstract

Time-triggered automotive networks use time-triggered protocols (FlexRay, TTEthernet, etc.) for periodic message transmissions that often originate from safety and time-critical applications. One of the major challenges with time-triggered transmissions is jitter, which is the unpredictable delay-induced deviation from the actual periodicity of a message. Failure to account for jitter can be catastrophic in time-sensitive systems, such as automotive platforms. In this article, we propose a novel scheduling framework (JAMS-SG) that satisfies timing constraints during message delivery for both jitter-affected time-triggered messages and high-priority event-triggered messages in automotive networks. At design time, JAMS-SG performs jitter-aware frame packing (packing of multiple signals from Electronic Control Units (ECUs) into messages) and schedules synthesis with a hybrid heuristic. At runtime, a Multi-Level Feedback Queue (MLFQ) handles jitter-affected time-triggered messages and high-priority event-triggered messages that are scheduled using a runtime scheduler. Our simulation results, based on messages and network traffic data from a real vehicle, indicate that JAMS-SG is highly scalable and outperforms the best-known prior work in the area in the presence of jitter.

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Cited By

El-Fatyany AWang XDuggirala PChakraborty SPasricha SSingh A(2024)Special Session: Emerging Architecture Design, Control, and Security Challenges in Software Defined Vehicles2024 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)10.1109/CODES-ISSS60120.2024.00014(27-36)Online publication date: 29-Sep-2024
https://doi.org/10.1109/CODES-ISSS60120.2024.00014
Munaro TMuntean IPretschner A(2024)A Failure Model Library for Simulation-Based Validation of Functional SafetyComputer Safety, Reliability, and Security10.1007/978-3-031-68606-1_2(18-32)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-68606-1_2
Kukkala VThiruloga SPasricha S(2023)Machine Learning for Anomaly Detection in Automotive Cyber-Physical SystemsEmbedded Machine Learning for Cyber-Physical, IoT, and Edge Computing10.1007/978-3-031-40677-5_11(253-283)Online publication date: 7-Oct-2023
https://doi.org/10.1007/978-3-031-40677-5_11
Show More Cited By

Index Terms

JAMS-SG: A Framework for Jitter-Aware Message Scheduling for Time-Triggered Automotive Networks
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Reliability
2. Networks
  1. Network properties
    1. Network reliability
  2. Network types
    1. Cyber-physical networks

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Published In

cover image ACM Transactions on Design Automation of Electronic Systems

ACM Transactions on Design Automation of Electronic Systems Volume 24, Issue 6

November 2019

275 pages

ISSN:1084-4309

EISSN:1557-7309

DOI:10.1145/3357467

Editor:
Naehyuck Chang
Korea Advanced Institute of Science and Technology, Korea

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 17 September 2019

Accepted: 01 July 2019

Revised: 01 July 2019

Received: 01 May 2019

Published in TODAES Volume 24, Issue 6

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Cited By

El-Fatyany AWang XDuggirala PChakraborty SPasricha SSingh A(2024)Special Session: Emerging Architecture Design, Control, and Security Challenges in Software Defined Vehicles2024 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)10.1109/CODES-ISSS60120.2024.00014(27-36)Online publication date: 29-Sep-2024
https://doi.org/10.1109/CODES-ISSS60120.2024.00014
Munaro TMuntean IPretschner A(2024)A Failure Model Library for Simulation-Based Validation of Functional SafetyComputer Safety, Reliability, and Security10.1007/978-3-031-68606-1_2(18-32)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-68606-1_2
Kukkala VThiruloga SPasricha S(2023)Machine Learning for Anomaly Detection in Automotive Cyber-Physical SystemsEmbedded Machine Learning for Cyber-Physical, IoT, and Edge Computing10.1007/978-3-031-40677-5_11(253-283)Online publication date: 7-Oct-2023
https://doi.org/10.1007/978-3-031-40677-5_11
Kukkala VBradley TPasricha S(2023)Security-Aware Design of Time-Critical Automotive Cyber-Physical SystemsMachine Learning and Optimization Techniques for Automotive Cyber-Physical Systems10.1007/978-3-031-28016-0_4(121-153)Online publication date: 2-Sep-2023
https://doi.org/10.1007/978-3-031-28016-0_4
Dey JPasricha S(2023)Machine Learning Based Perception Architecture Design for Semi-autonomous VehiclesMachine Learning and Optimization Techniques for Automotive Cyber-Physical Systems10.1007/978-3-031-28016-0_22(625-646)Online publication date: 2-Sep-2023
https://doi.org/10.1007/978-3-031-28016-0_22
Dey JPasricha S(2023)Sensing Optimization in Automotive PlatformsMachine Learning and Optimization Techniques for Automotive Cyber-Physical Systems10.1007/978-3-031-28016-0_19(545-563)Online publication date: 2-Sep-2023
https://doi.org/10.1007/978-3-031-28016-0_19
Thiruloga SKukkala VPasricha S(2023)Deep AI for Anomaly Detection in Automotive Cyber-Physical SystemsMachine Learning and Optimization Techniques for Automotive Cyber-Physical Systems10.1007/978-3-031-28016-0_12(381-397)Online publication date: 2-Sep-2023
https://doi.org/10.1007/978-3-031-28016-0_12
Kukkala VThiruloga SPasricha S(2023)Stacked LSTM Based Anomaly Detection in Time-Critical Automotive NetworksMachine Learning and Optimization Techniques for Automotive Cyber-Physical Systems10.1007/978-3-031-28016-0_11(349-380)Online publication date: 2-Sep-2023
https://doi.org/10.1007/978-3-031-28016-0_11
Kukkala VThiruloga SPasricha S(2023)Real-Time Intrusion Detection in Automotive Cyber-Physical Systems with Recurrent AutoencodersMachine Learning and Optimization Techniques for Automotive Cyber-Physical Systems10.1007/978-3-031-28016-0_10(317-347)Online publication date: 2-Sep-2023
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Kukkala VBradley TPasricha S(2023)Reliable Real-Time Message Scheduling in Automotive Cyber-Physical SystemsMachine Learning and Optimization Techniques for Automotive Cyber-Physical Systems10.1007/978-3-031-28016-0_1(3-42)Online publication date: 2-Sep-2023
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