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Enhancing Task Priorities Optimization in FlexRay Using Immune Genetic Algorithms

This study explores the use of immune genetic algorithms for optimizing task priorities and FlexRay frame identifiers in distributed embedded systems. By minimizing delays and improving the overall solution quality, the research demonstrates the effectiveness of this approach compared to traditional genetic algorithms.

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Enhancing Task Priorities Optimization in FlexRay Using Immune Genetic Algorithms

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  1. Immune Genetic Algorithms for Optimization of Task Priorities and FlexRay Frame Identifiers Soheil Samii1, Yanfei Yin1,2, Zebo Peng1, Petru Eles1, Yuanping Zhang2 1 Dept. of Computer and Information Science Linköping University Sweden 2School of Computer and Communication Lanzhou University China

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

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

  4. System model

  5. FlexRay configuration Static phase Dynamic phase a c d b 2 3 1 2 3 Bus cycle 1 • Frame identifiers and priorities to messages • Priorities to tasks a b c d 1 3 2

  6. Timing with some configuration Average = 477

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

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

  9. Immune genetic algorithms Crossover Initial population 3 1 2 4 3 2 3 3 1 2 Simulation of each member Mutation Evaluate costs Population Population costs Vaccination Stop? New population No

  10. Vaccination Population 4 Create vaccines 3 1 2 4 3 2 3 3 1 2 2 1 4 2 3 3 3 3 1 3 2 3 2 4 1 3 1 1 1 2 1 3 2 1 3 2 4 3 1 3 3 2 1 1 3 4 2 1 3 4 2 2 1 3 4 2 3 4 2 1 Dominance threshold 50% 60 80 80 40 60 60

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

  12. Vaccination Member 2 3 1 4 1 2 1 2 1 3 4 Vaccines 2 New member 2 4 1 3 1 2 2 2 1 3

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

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

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

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

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

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

  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)

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