1. Immune Genetic Algorithms for Optimization of Task Priorities and FlexRay Frame Identifiers Soheil Samii 1, Yanfei Yin 1,2, Zebo Peng 1, Petru Eles 1, Yuanping Zhang 2 1 Dept. of Computer and Information Science Linköping University Sweden 2 School of Computer and Communication Lanzhou University China

2. 2 Motivation  FlexRay  Safety-critical applications in the static segment  Other applications in the dynamic segment Many optimization parameters

3. 3 Outline  System model  Bus cycle of FlexRay  Problem formulation  Optimization with immune genetic algorithms  Experimental results

4. 4 System model

5. 5 FlexRay configuration Static phase Dynamic phase acdb acdb 132 Bus cycle1 23123  Frame identifiers and priorities to messages  Priorities to tasks

6. 6 Timing with some configuration Average = 477

7. 7 Timing with some other configuration Average = 369 Previous case: Average = 477

8. 8 Problem formulation  Parameters:  Priorities of the tasks  Frame identifiers and priorities of the messages  Objective:  Minimize the average response time of tasks

9. 9 Immune genetic algorithms Initial population Evaluate costs Population costs Stop? Crossover Mutation Vaccination New population No Population Simulation of each member 3124323312

10. 10 Vaccination Population Create vaccines 31243233121423333132241311121313243133213421342213 423421 6080 4060 Dominance threshold 50% 4 2 3 2 1

11. 11 Vaccination Population Select member Member Vaccines Select vaccines Vaccinate Create vaccines Vaccine set Vaccination rate

12. 12 Vaccination 4223141212132413122213 Member Vaccines New member

13. 13 Vaccination New population Population Select member Member Vaccines Select vaccines Vaccinate Last member ? Create vaccines Vaccine set No Yes Dominance threshold Vaccination rate

14. 14 Tuning – Vaccination rate Vaccination rate [%] Cost

15. 15 Tuning – Dominance threshold Dominance threshold [%] Cost

16. 16 Vaccination  Takes advantage of local properties of good solutions  Speed up the optimization process  Improve the quality of the final solution

17. 17 Experiments – Improvements Number of tasks Cost improvements [%] GA IGA

18. 18 Experiments – Runtime Number of tasks Runtimes [seconds] GA IGA

19. 19 Conclusions  Minimize delays in distributed embedded systems  Task priorities  Frame identifiers for FlexRay messages  Immune genetic algorithms  Vaccination results in better optimization in terms of time and solution quality (compared to traditional genetic algorithms)