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

  • Boris Madzar
  • Jalil Boudjadar
  • Juergen Dingel
  • Thomas E. Fuhrman
  • S. Ramesh
Conference paper

DOI: 10.1007/978-3-319-57666-4_10

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10231)
Cite this paper as:
Madzar B., Boudjadar J., Dingel J., Fuhrman T.E., Ramesh S. (2017) Formal Analysis of Predictable Data Flow in Fault-Tolerant Multicore Systems. In: Kouchnarenko O., Khosravi R. (eds) Formal Aspects of Component Software. FACS 2016. Lecture Notes in Computer Science, vol 10231. Springer, Cham

Abstract

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.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Boris Madzar
    • 1
  • Jalil Boudjadar
    • 2
  • 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