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International Journal of Automotive Technology > Volume 20(5); 2019 > Article
International Journal of Automotive Technology 2019;20(5): 873-883.
doi: https://doi.org/10.1007/s12239-019-0081-0
Armaghan Darbandi, Myung Kyun Kim
University of Ulsan
PDF Links Corresponding Author.  Myung Kyun Kim , Email. mkkim@mail.ulsan.ac.kr
Recently, the authors have proposed a scalable holistic scheduling approach for time-triggered applications on FlexRay, under end-to-end deadline and data dependency constraints. This approach divides the problem to two sub-problems that can be solved separately. The first sub-problem optimally schedules the set of messages to minimize number of used slots, and with respect to information exchanges between tasks and messages, end-to-end deadlines and FlexRay protocol constraints. The second sub-problem optimally schedules the set of tasks to minimize tasks response times, with respect to the solution returned by the first sub-problem. In this paper, our goal is to increase the scalability of our solution to each subproblem. We propose a greedy heuristic approach for the first sub-problem that can find feasible solutions at a low runtime cost. In addition, to resolve assignment conflicts, we apply rescheduling the conflicted application with offset modification and priority promotion procedures. Furthermore, we show that the task scheduling in the second sub-problem can be divided into K independent child sub-problems, where K is the number of ECUs. Our experiments show high scalability and efficiency of our approaches comparing with our optimization-based approaches in the previous work.
Key Words: FlexRay, Distributed embedded systems, Holistic scheduling, Precedence constraints
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