International Conference on Formal Modeling and Analysis of Timed Systems

FORMATS 2015: Formal Modeling and Analysis of Timed Systems pp 108-123 | Cite as

On the Scalability of Constraint Solving for Static/Off-Line Real-Time Scheduling

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9268)

Abstract

Recent papers have reported on successful application of constraint solving techniques to off-line real-time scheduling problems, with realistic size and complexity. Success allegedly came for two reasons: major recent advances in solvers efficiency and use of optimized, problem-specific constraint representations. Our current objective is to assess further the range of applicability and the scalability of such constraint solving techniques based on a more general and agnostic evaluation campaign. For this, we have considered a large number of synthetic scheduling problems and a few real-life ones, and attempted to solve them using 3 state-of-the-art solvers, namely CPLEX, Yices2, and MiniZinc/G12. Our findings were that, for all problems considered, constraint solving does scale to a certain limit, then diverges rapidly. This limit greatly depends on the specificity of the scheduling problem type. All experimental data (synthetic task systems, SMT/ILP models) are provided so as to allow experimental reproducibility.

Keywords

Real-time scheduling Satisfiability modulo theories  Constraint solving Repeatable 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bahn, J.H., Yang, J., Bagherzadeh, N.: Parallel fft algorithms on network-on-chips. In: Proceedings ITNG 2008, April 2008Google Scholar
  2. 2.
    Baptiste, P., Le Pape, C., Nuijten, W.: Constraint-based scheduling: applying constraint programming to scheduling problems, vol. 39. Springer Science & Business Media (2001)Google Scholar
  3. 3.
    Bini, E., Buttazzo, G.: Measuring the performance of schedulability tests. Real Time Systems 30, 129–154 (2005)CrossRefMATHGoogle Scholar
  4. 4.
    Carle, T., Potop-Butucaru, D.: Predicate-aware, makespan-preserving software pipelining of scheduling tables. TACO 11(1), 12 (2014)CrossRefGoogle Scholar
  5. 5.
    Carle, T., Potop-Butucaru, D., Sorel, Y., Lesens, D.: From dataflow specification to multiprocessor partitioned time-triggered real-time implementation. Research Report RR-8109 (2012). https://hal.inria.fr/hal-00742908
  6. 6.
    Coffman Jr., A.P.E., Graham, R.L.: Optimal scheduling for two-processor systems. Acta informatica 1(3), 200–213 (1972)CrossRefGoogle Scholar
  7. 7.
    Craciunas, S., Oliver, R.S.: SMT-based task- and network-level static schedule generation for time-triggered networked systems. In: Proceedings RTNS 2014. pp. 45:45–45:54. ACM, New York (2014). http://doi.acm.org/10.1145/2659787.2659812
  8. 8.
    Garey, M., Johnson, D.: Complexity results for multiprocessor scheduling under resource constraints. SIAM Journal of Computing 4(4), 397–411 (1975)CrossRefMathSciNetMATHGoogle Scholar
  9. 9.
    Gu, Z., He, X., Yuan, M.: Optimization of static task and bus access schedules for time-triggered distributed embedded systems with model-checking. In: 44th ACM/IEEE Design Automation Conference, DAC 2007, pp. 294–299, June 2007Google Scholar
  10. 10.
    Hang, C., Manolios, P., Papavasileiou, V.: Synthesizing cyber-physical architectural models with real-time constraints. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 441–456. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  11. 11.
    Leyton-Brown, K., Hoos, H.H., Hutter, F., Xu, L.: Understanding the empirical hardness of np-complete problems. Commun. ACM 57(5), 98–107 (2014). http://doi.acm.org/10.1145/2594413.2594424 CrossRefGoogle Scholar
  12. 12.
    Megel, T., Sirdey, R., David, V.: Minimizing task preemptions and migrations in multiprocessor optimal real-time schedules. In: 2010 IEEE 31st Real-Time Systems Symposium (RTSS), pp. 37–46, November 2010Google Scholar
  13. 13.
    Nowatzki, T., Sartin-Tarm, M., Carli, L.D., Sankaralingam, K., Estan, C., Robatmili, B.: A general constraint-centric scheduling framework for spatial architectures. SIGPLAN Not. 48(6), 495–506 (2013). http://doi.acm.org/10.1145/2499370.2462163, (Proceedings PLDI 2013)CrossRefGoogle Scholar
  14. 14.
    Tendulkar, P., Poplavko, P., Maler, O.: Symmetry breaking for multi-criteria mapping and scheduling on multicores. In: Braberman, V., Fribourg, L. (eds.) FORMATS 2013. LNCS, vol. 8053, pp. 228–242. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  15. 15.
    Topcuoglu, H., Hariri, S., Wu, M.Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems 13(3), 260–274 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.INRIARocquencourtFrance
  2. 2.CNESParisFrance
  3. 3.Brown UniversityProvidenceUSA
  4. 4.CNRS/UNSSophia-AntipolisFrance
  5. 5.INRIASophia-Antipolis MéditerranéeFrance

Personalised recommendations