Communication Scheduling for
Time-Triggered Systems
Paul Pop, Petru Eles and Zebo Peng
Dept. of Computer and Information Science
Linköping University
Sweden
Communication Scheduling for Time-Triggered Systems
Slide 1
Conditional Process Graph
Subgraph corresponding to
DCK
P0
P1
C
P2
P6
K
P5
P7
D
P3
C
C
P4
D
P11
P8
P9
P14
Communication Scheduling for Time-Triggered Systems
P13
K
P15
P16
P17
P10
 First processor
 Second processor
 ASIC
P12
P18
Slide 2
Hardware Architecture
• Safety-critical distributed embedded systems.
I/O Interface
• Nodes connected by a broadcast
communication channel.
RAM
ROM
CPU
• Nodes consisting of: TTP controller, CPU,
RAM, ROM, I/O interface, (maybe) ASIC.
ASIC
TTP Controller
• Communication between nodes is based on
the time-triggered protocol.
Node
• Buss access scheme: time-division multipleaccess (TDMA).
• Schedule table located in each TTP
controller: message descriptor list (MEDL).
S0 S 1
Slot
S2
S3
S0 S1
S2
S3
TDMA Round
Cycle of two rounds
Communication Scheduling for Time-Triggered Systems
Slide 3
Problem Formulation
Input
• Safety-critical application with several operating modes.
• Each operating mode is modelled by a conditional process graph.
• The system architecture and mapping of processes to nodes are given.
• The worst case delay of a process is known:
TPi  ( PA  t Pi   C1   C2 )
C 
1
local
N out
( Pi )

i 1
Si
C 
2
remote
N out
( Pi )

i 1
KS i

N inremote( Pi )

i 1
KRi
Output
• Local schedule tables for each node and the MEDL for the TTP controllers.
• Delay on the system execution time for each operating mode,
so that this delay is as small as possible.
Communication Scheduling for Time-Triggered Systems
Slide 4
Scheduling Example
P1
P4
P3
P2
24 ms
S1
S0
Round 1
m3
m1
m2
Round 2
Round 3
m4
Round 4
P1
S1
Round 1
m1
m1
P3
P2
m2
Round 2
m3
Round 3
Round 4
P4
20 ms
P2
S1
Round 1
m1 m2
Round 2
Communication Scheduling for Time-Triggered Systems
m2
m4
P1
S0
P1
P4
22 ms
S0
Round 5
P2
P3
m3
m4
P3
m3 m4
P4
Round 3
Slide 5
Experimental Results
Average percentage deviations
from the lengths of near-optimal schedules
% 60
50
• The Greedy Approach is producing accurate
results in a very short time (few seconds for
graphs with 400 processes).
Naive Designer
Greedy 1
Greedy 2
• Greedy 1 performs slightly better than
Greedy 2, but it is a bit slower.
40
30
• SA finds near-optimal results in a reasonable
time (few minutes for graphs with 80 processes
and 275 minutes for graphs with 400 processes).
20
• A real-life example implementing a vehicle
cruise controller validated our approach.
10
0
80
160
240
320
400
Number of processes
Communication Scheduling for Time-Triggered Systems
Slide 6