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International Journal of Automotive Technology

, Volume 20, Issue 5, pp 873–883 | Cite as

Scalable Holistic Scheduling of Safety-critical Systems on In-vehicle TDMA Networks

  • Darbandi Armaghan
  • Myung Kyun KimEmail author
Article
  • 15 Downloads

Abstract

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 sub-problem. 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 

Nomenclature

L

length of FlexRay cycle

H

number of communication cycles in a FlexRay cycle

K

number of ECUs

lslot

length of static slot

nslot

number of static slots in static segment

lCC

length of communication cycle

ECU-set

set of ECUs

G = (V, E)

DAG of application g

V

set of tasks

E

set of all messages communicating between tasks

M

set of messages communicating on the bus

T(g)

period of application g

Pr(gk)

priority of application g

τi

i-th task in application g

ECU(τi)

ECU that hosts τi

Ti

period of invocations of τi

Ci

execution time of τi

Di

relative deadline of τi

Φi

offset of τi

WCRT(τi)

worst case response time of τi

mi

generated message by τi

ri

repetition rate of mi

wi

size of message mi in bits

τi,k

k-th job of the task τi

mi,k

generated message by τi,k

ai,k

arrival time of task instance τi,k

si,k

start time of task instance τi,k

fi,k

finish time of task instance τi,k

di,k

absolute deadline of task instance τi,k

Instance-Graph(g)

instance graph of application g

Deadline-Path(τk,l,τp,q)

end-to-end deadline of Path(τk,l,τp,q)

Delay-Path(τk,l,τp,q)

end-to-end delay of Path(τk,l,τp,q)

Reduced-Graph(g)

reduced DAG of Instance-Graph(g)

Vi,pj,q

set of jobs in the path between mi,p and mj,q

twi,pj,q

time window assigned to jobs in Vi,pj,q

ltwi,pj,q

length of time window twi,pj,q

LBi,p

lower bound for transmission of mi,p

UBi,p

upper bound for transmission of mi,p

SCi,p

set of successor messages of mi,p in Reduced-Graph(g)

ISCi,p

set of immediate successors of mi,p in Reduced-Graph(g)

IPDi,p

set of immediate predecessors of mi,p in Reduced-Graph(g)

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Notes

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (KNRF-2017030208).

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Copyright information

© KSAE 2019

Authors and Affiliations

  1. 1.School of Computer and Electrical EngineeringUniversity of UlsanUlsanKorea

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