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Automatic Optimization of the VLAN Partitioning in Automotive Communication Networks

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Published:21 December 2018Publication History
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Abstract

Dividing the communication network into so-called Virtual Local Area Networks (VLANs), i.e., subnetworks that are isolated at the data link layer (OSI layer 2), is a promising approach to address the increasing security challenges in automotive networks. The automation of the VLAN partitioning is a well-researched problem in the domain of local or metropolitan area networks. However, the approaches used there are hardly applicable for the design of automotive networks as they mainly focus on reducing the amount of broadcast traffic and cannot capture the many design objectives of automotive networks like the message timing or the link load, which are affected by the VLAN partitioning. As a remedy, this article proposes an approach based on a set of Pseudo-Boolean constraints to generate a message routing which is feasible with respect to the VLAN-related routing restrictions in automotive networks. This approach can be used for a design space exploration to optimize not only the VLAN partitioning but also other routing-related objectives. We demonstrate both the efficiency of our message routing approach and the now accessible optimization potential for the complete Electric/Electronic architecture with a mixed-criticality system from the automotive domain. There we thoroughly investigate the impact of the VLAN partitioning on the message timing and the link loads by optimizing these design objectives concurrently. During the exploration of the huge design space, where each resource can be assigned to one of four VLANs, our approach requires less than 40ms for the creation of a valid solution and ensures that all messages satisfy their deadlines and link load bounds.

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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 1
          January 2019
          309 pages
          ISSN:1084-4309
          EISSN:1557-7309
          DOI:10.1145/3293467
          • Editor:
          • Naehyuck Chang
          Issue’s Table of Contents

          Copyright © 2018 ACM

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          Publication History

          • Published: 21 December 2018
          • Accepted: 1 September 2018
          • Revised: 1 July 2018
          • Received: 1 November 2017
          Published in todaes Volume 24, Issue 1

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