Automated Scheduling for Tightly-Coupled Embedded Multi-core Systems Using Hybrid Genetic Algorithms

  • Pedro Cuadra
  • Lukas Krawczyk
  • Robert Höttger
  • Philipp Heisig
  • Carsten Wolff
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 756)

Abstract

Deploying software to embedded multi- and many-core hardware has become increasingly complex in the past years. Due to the heterogeneous nature of embedded systems and the complex underlying Network on Chip structures of many-core architectures, aspects such as the runtime of executable software are highly influenced by a variety of factors, e.g. the type, instruction set, and speed of the processor an executable is allocated to as well as its predecessors, their location, ordering and the communication channels in between them. In this work, we propose a semi-automated Hybrid Genetic Algorithm based optimization approach for distributing and re-scheduling executional software to heterogeneous hardware architectures in constrained solution spaces, along with an evaluation of its applicability and efficiency. The evaluation is based on both, publicly available as well as real world examples of automotive engine management systems.

Keywords

Hybrid Genetic Algorithms Many-core Automotive 

References

  1. 1.
    Alexandrescu, A., Agavriloaei, I., Craus, M.: A genetic algorithm for mapping tasks in heterogeneous computing systems. In: 15th International Conference on System Theory, Control and Computing, pp. 1–6, October 2011Google Scholar
  2. 2.
    Eclipse: App4mc website (2017). http://www.eclipse.org/app4mc/
  3. 3.
    Faragardi, H.R., Lisper, B., Sandström, K., Nolte, T.: An efficient scheduling of autosar runnables to minimize communication cost in multi-core systems. In: 2014 7th International Symposium on Telecommunications (IST), pp. 41–48, September 2014Google Scholar
  4. 4.
    Ferrandi, F., Lanzi, P.L., Pilato, C., Sciuto, D., Tumeo, A.: Ant colony heuristic for mapping and scheduling tasks and communications on heterogeneous embedded systems. IEEE Trans. Comput. Aided Design Integr. Circuits Syst. 29(6), 911–924 (2010)CrossRefGoogle Scholar
  5. 5.
    Frey, P.: A timing model for real-time control-systems and its application on simulation and monitoring of autosar systems (2011). doi:10.18725/OPARU-1743
  6. 6.
    Hamann, A., Ziegenbein, D., Kramer, S., Lukasiewycz, M.: Demo abstract: demonstration of the FMTV 2016 timing verification challenge. In: 2016 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), p. 1, April 2016Google Scholar
  7. 7.
    Jiang, Z., Feng, S.: A fast hybrid genetic algorithm in heterogeneous computing environment. In: 2009 Fifth International Conference on Natural Computation, vol. 4, pp. 71–75, August 2009Google Scholar
  8. 8.
    Krawczyk, L., Kamsties, E.: Hardware models for automated partitioning and mapping in multi-core systems. In: 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), vol. 02, pp. 721–725, September 2013Google Scholar
  9. 9.
    Krawczyk, L., Wolff, C., Fruhner, D.: Automated distribution of software to multi-core hardware in model based embedded systems development. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2015. CCIS, vol. 538, pp. 320–329. Springer, Cham (2015). doi:10.1007/978-3-319-24770-0_28 CrossRefGoogle Scholar
  10. 10.
    Mohr, D., Kaas, H.W., Gao, P., Cornet, A., Wee, D., Inampudi, S., Krieger, A., Richter, G., Habeck, A., Newman, J.: Connected car, automotive value chain unbound (2014)Google Scholar
  11. 11.
    Wilhelmstötter, F.: Jenetics: Java genetic algorithm library (2017). http://jenetics.io/
  12. 12.
    Xie, G., Li, R., Xiao, X., Chen, Y.: A high-performance dag task scheduling algorithm for heterogeneous networked embedded systems. In: 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, pp. 1011–1016, May 2014Google Scholar
  13. 13.
    Zeng, B., Wei, J., Liu, H.: Research of optimal task scheduling for distributed real-time embedded systems. In: 2008 International Conference on Embedded Software and Systems, pp. 77–84, July 2008Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pedro Cuadra
    • 1
  • Lukas Krawczyk
    • 1
  • Robert Höttger
    • 1
  • Philipp Heisig
    • 1
  • Carsten Wolff
    • 1
  1. 1.Institute for Digital Transformation of Application and Living Domains (IDiAL)Dortmund University of Applied Sciences and ArtsDortmundGermany

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