Formal Analysis of Predictable Data Flow in Fault-Tolerant Multicore Systems

  • Boris Madzar
  • Jalil BoudjadarEmail author
  • Juergen Dingel
  • Thomas E. Fuhrman
  • S. Ramesh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10231)


The need to integrate large and complex functions into today’s vehicle electronic control systems requires high performance computing platforms, while at the same time the manufacturers try to reduce cost, power consumption and ensure safety. Traditionally, safety isolation and fault containment of software tasks have been achieved by either physically or temporally segregating them. This approach is reliable but inefficient in terms of processor utilization. Dynamic approaches that achieve better utilization without sacrificing safety isolation and fault containment appear to be of increasing interest. One of these approaches relies on predictable data flow introduced in PharOS and Giotto. In this paper, we extend the work on leveraging predictable data flow by addressing the problem of how the predictability of data flow can be proved formally for mixed criticality systems that run on multicore platforms and are subject to failures. We consider dynamic tasks where the timing attributes vary from one period to another. Our setting also allows for sporadic deadline overruns and accounts for criticality during fault handling. A user interface was created to allow automatic generation of the models as well as visualization of the analysis results, whereas predictability is verified using the Spin model checker.



This work is supported by the Natural Sciences and Engineering Research Council of Canada, as part of the NECSIS Automotive Research Partnership with General Motors, IBM and Malina Software Corp.


  1. 1.
    ISO 26262-1:2011D Road vehicles-Functional safety. Technical report, ISO (2011)Google Scholar
  2. 2.
    Bebelis, V., Fradet, P., Girault, A., Lavigueur, B.: BPDF: a statically analyzable dataflow model with integer and boolean parameters. In: EMSOFT 2013, pp. 3:1–3:10. IEEE Press (2013)Google Scholar
  3. 3.
    Bondarev, E., Chaudron, M., de With, P.: Compositional performance analysis of component-based systems on heterogeneous multiprocessor platforms. In: SEAA 2006, pp. 81–91, August 2006Google Scholar
  4. 4.
    Boudjadar, A., Dingel, J., Madzar, B., Kim, J.H.: Compositional predictability analysis of mixed critical real time systems. In: Artho, C., Ölveczky, P.C. (eds.) FTSCS 2015. CCIS, vol. 596, pp. 69–84. Springer, Cham (2016). doi: 10.1007/978-3-319-29510-7_4 CrossRefGoogle Scholar
  5. 5.
    Boudjadar, A., Kim, J.H., Larsen, K.G., Nyman, U.: Compositional schedulability analysis of an avionics system using UPPAAL. In: Proceedings of ICAASE 2014, pp. 140–147 (2014)Google Scholar
  6. 6.
    Chabrol, D., Aussagues, C., David, V.: A spatial and temporal partitioning approach for dependable automotive systems. In: IEEE Conference on Emerging Technologies Factory Automation, pp. 1–8 (2009)Google Scholar
  7. 7.
    de Niz, D., Lakshmanan, K., Rajkumar, R.: On the scheduling of mixed-criticality real-time task sets. In: RTSS 2009, pp. 291–300 (2009)Google Scholar
  8. 8.
    Feiler, P., Lewis, B., Vestal, S.: Improving predictability in embedded real-time systems. Technical report CMU/SEI-2000-SR-011, December 2000Google Scholar
  9. 9.
    Fredriksson, J.: Improving predictability and resource utilization in component-based embedded real-time systems. Ph.D. thesis, Mälardalen University (2008)Google Scholar
  10. 10.
    Garousi, V., Briand, L., Labiche, Y.: A unified approach for predictability analysis of real-time systems using UML-based control flow information. In: Gérard, S., Graf, S., Haugen, O., Selic, B. (eds.) MARTES 2005, Workshop on Modelling and Analysis of Real Time and Embedded Systems, with MODELS (2005).
  11. 11.
    Henzinger, T.A.: Two challenges in embedded systems design: predictability and robustness. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 366, 3727–3736 (2008)CrossRefGoogle Scholar
  12. 12.
    Henzinger, T.A., Horowitz, B., Kirsch, C.M.: Giotto: a time-triggered language for embedded programming. In: Henzinger, T.A., Kirsch, C.M. (eds.) EMSOFT 2001. LNCS, vol. 2211, pp. 166–184. Springer, Heidelberg (2001). doi: 10.1007/3-540-45449-7_12 CrossRefGoogle Scholar
  13. 13.
    Henzinger, T.A., Manna, Z., Pnueli, A.: Timed transition systems. In: Bakker, J.W., Huizing, C., Roever, W.P., Rozenberg, G. (eds.) REX 1991. LNCS, vol. 600, pp. 226–251. Springer, Heidelberg (1992). doi: 10.1007/BFb0031995 CrossRefGoogle Scholar
  14. 14.
    Madzar, B.: Modelling and verification of predictable data flow in real-time systems, M. Sc thesis. Queen’s University Canada (2015)Google Scholar
  15. 15.
    Mohaqeqi, M., Abdullah, J., Yi, W.: Modeling and analysis of data flow graphs using the digraph real-time task model. In: Bertogna, M., Pinho, L.M., Quiñones, E. (eds.) Ada-Europe 2016. LNCS, vol. 9695, pp. 15–29. Springer, Cham (2016). doi: 10.1007/978-3-319-39083-3_2 Google Scholar
  16. 16.
    Pellizzoni, R., Betti, E., Bak, S., Yao, G., Criswell, J., Caccamo, M., Kegley, R.: A predictable execution model for COTS-based embedded systems. In: RTAS 2011Google Scholar
  17. 17.
    Yau, S., Zhou, X.: Schedulability in model-based software development for distributed real-time systems. In: WORDS 2002, pp. 45–52 (2002)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Boris Madzar
    • 1
  • Jalil Boudjadar
    • 2
    Email author
  • Juergen Dingel
    • 1
  • Thomas E. Fuhrman
    • 3
  • S. Ramesh
    • 3
  1. 1.Queen’s UniversityKingstonCanada
  2. 2.Aarhus UniversityAarhusDenmark
  3. 3.General Motors R&DWarrenUSA

Personalised recommendations