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Discrete Event Dynamic Systems

, Volume 17, Issue 3, pp 355–403 | Cite as

Partial Order Techniques for Distributed Discrete Event Systems: Why You Cannot Avoid Using Them

  • Eric Fabre
  • Albert Benveniste
Article

Abstract

Monitoring or diagnosis of large scale distributed Discrete Event Systems with asynchronous communication is a demanding task. Ensuring that the methods developed for Discrete Event Systems properly scale up to such systems is a challenge. In this paper we explain why the use of partial orders cannot be avoided in order to achieve this objective. To support this claim, we try to push classical techniques (parallel composition of automata and languages) to their limits and we eventually discover that partial order models arise at some point. We focus on on-line techniques, where a key difficulty is the choice of proper data structures to represent the set of all runs of a distributed system, in a modular way. We discuss the use of previously known structures such as execution trees and unfoldings. We propose a novel and more compact data structure called “trellis.” Then, we show how all the above data structures can be used in performing distributed monitoring and diagnosis. The techniques reported here were used in an industrial context for fault management and alarm correlation in telecommunications networks. This paper is an extended and improved version of the plenary address that was given by the second author at WODES’ 2006.

Keywords

Discrete event systems Distributed systems Diagnosis Partial orders Unfoldings Fault management Alarm correlation 

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References

  1. Aghasaryan A, Jard C, Thomas J (2004) UML specification of a generic model for fault diagnosis of telecommunication networks. In: International communication conference (ICT), August 2004. LNCS, vol 3124. Fortaleza, Brasil, pp 841–847Google Scholar
  2. Abbes S, Benveniste A (2005) Branching cells as local states for event structures and nets: probabilistic applications. In: Sassone (ed) FoSSaCSV, vol 3441, pp 95–109Google Scholar
  3. Abbes S, Benveniste A (2006) True-concurrency probabilistic models: branching cells and distributed probabilities for event structures. Inf Comput 204(2):231–274MATHCrossRefGoogle Scholar
  4. Baldan P, Haar S, König B (2006) Distributed unfolding of petri nets. In: Proc. of FOSSACS 2006. LNCS, vol 3921. Springer, pp 126–141Google Scholar
  5. Baroni P, Lamperti G, Pogliano P, Zanella M (1999) Diagnosis of large active systems. Artif Intell. 110:135–183MATHCrossRefGoogle Scholar
  6. Benveniste A, Fabre E, Haar S, Jard C (2003) Diagnosis of asynchronous discrete event systems, a net unfolding approach. IEEE Trans. Automat Contr 48(5):714–727CrossRefGoogle Scholar
  7. Boel RK, van Schuppen JH (2002) Decentralized failure diagnosis for discrete event systems with costly communication between diagnosers. In: Proc. 6th Int. workshop on discrete event systems, WODES’02, pp 175–181Google Scholar
  8. Boel RK, Jiroveanu G (2004) Distributed contextual diagnosis for very large systems. In: Proc. of WODES’04, pp 343–348Google Scholar
  9. Chatain T, Jard C (2005) Time supervision of concurrent systems using symbolic unfoldings of time petri nets. In: 3rd International conference on formal modelling and analysis of timed systems (FORMATS 2005), September 2005. LNCS, vol 3829, Springer, pp 196–210Google Scholar
  10. Contant O, Lafortune S (2004) Diagnosis of modular discrete event systems. In: Proc. of WODES’04, pp 337–342Google Scholar
  11. Debouk R, Lafortune S, Teneketzis D (2000) Coordinated decentralized protocols for failure diagnosis of discrete event systems. J Discrete Event Dyn Syst 10(1/2):33–86MATHCrossRefGoogle Scholar
  12. Debouk R, Lafortune S, Teneketzis D (2003) On the effect of communication delays in failure diagnosis of decentralized discrete event systems. J Discrete Event Dyn Syst 13(3):263–289MATHCrossRefGoogle Scholar
  13. Dousson C, Gaborit P, Ghallab M (1993) Situation recognition: representation and algorithms. IJCAI 1993, 166–174Google Scholar
  14. Devillers R, Klaudel H (2004) Solving petri net recursions through finite representation. In: Proc of IASTED’04.Google Scholar
  15. Fabre E (2003) Factorization of unfoldings for distributed tile systems, part 1 : limited interaction case, Inria research report no. 4829 http://www.inria.fr/rrrt/rr-4829.html
  16. Fabre E (2004) Factorization of unfoldings for distributed tile systems, part 2 : general case, Inria research report no. 5186 http://www.inria.fr/rrrt/rr-5186.html
  17. Fabre E (2003) Convergence of the turbo algorithm for systems defined by local constraints, Irisa research report no. PI 1510 http://www.irisa.fr/doccenter/publis/PI/2003/irisapublication.2006-01-27.8249793876
  18. Fabre E, Benveniste A, Haar S, Jard C (2005) Distributed monitoring of concurrent and asynchronous systems. J Discrete Event Dyn Syst, 15(1):33–84 (special issue)MATHCrossRefGoogle Scholar
  19. Fabre E (2005) Distributed diagnosis based on trellis processes. In: Proc. conf. on decision and control. Sevilla, pp 6329-6334Google Scholar
  20. Fabre E, Hadjicostis C (2006) A trellis notion for distributed system diagnosis with sequential semantics. In Proc. of Wodes 2006, 10–12 July 2006. Ann Arbor, USAGoogle Scholar
  21. Fabre E (2007) Habilitation thesis. Uni. Rennes IGoogle Scholar
  22. Fidge CJ (1991) Logical time in distributed computing systems. IEEE Computer 24(8):28–33Google Scholar
  23. Genc S, Lafortune S (2003) Distributed diagnosis of discrete-event systems using petri nets. In: Proc. 24th int. conf. on applications and theory of petri nets, June 2003. LNCS vol 2679, pp 316–336Google Scholar
  24. Genc S, Lafortune S (2007) Distributed diagnosis of place-bordered petri nets. IEEE Trans Automat Sci Eng 4(2):206–219, AprilCrossRefGoogle Scholar
  25. Haar S, Benveniste A, Fabre E, Jard C (2005) Fault diagnosis for distributed asynchronous dynamically reconfigured discrete event systems. In: IFAC world congress praha 2005Google Scholar
  26. Jéron T, Marchand H, Pinchinat S, Cordier M-O (2006) Supervision patterns in discrete event systems diagnosis. In: 8th international workshop on discrete event systems, July 2006. Ann Arbor, Michigan, USA, pp 10–12Google Scholar
  27. Kumar R, Takai S (2006) Inference-based ambiguity management in decentralized decision making: decentralized diagnosis of discrete event systems, 2006 American Control Conference, MinneapolisGoogle Scholar
  28. Lamperti G, Zanella M (2002) Diagnosis of discrete-event systems from uncertain temporal observations. Artif Intell 137(1–2):91–163MATHCrossRefGoogle Scholar
  29. Lamperti G, Zanella M (2003) Diagnosis of active systems: principles and techniques. Kluwer International Series in Engineering and Computer Science, vol 741Google Scholar
  30. Lamperti G, Zanella M (2006) Flexible diagnosis of discrete-event systems by similarity-based reasoning techniques. Artif Intell 170(3):232–297CrossRefGoogle Scholar
  31. Lamperti G, Zanella M (2006) Incremental processing of temporal observations in supervision and diagnosis of discrete-event systems. ICEIS (2) 2006:47–57Google Scholar
  32. Lauritzen SL (1996) Graphical models. Oxford Statistical Science Series 17, Oxford Univ. PressGoogle Scholar
  33. Mac Lane S (1998) Categories for the working mathematician. SpringerGoogle Scholar
  34. McMillan KL (1992) Using unfoldings to avoid the state explosion problem in the verification of asynchronous circuits. In: Proc. 4th Workshop of computer aided verification. Montreal, pp 164–174Google Scholar
  35. McMillan KL (1993) Symbolic Model checking: an approach to the state explosion problem, PhD. thesis, KluwerGoogle Scholar
  36. Mattern F (1989) Virtual time and global states of distributed systems. In: Cosnard, Quinton, Raynal, Robert (eds) Proc. int. workshop on parallel and distributed algorithms bonas, France, Oct. 1988. North HollandGoogle Scholar
  37. Nielsen M, Plotkin G, Winskel G (1981) Petri nets, event structures and domains. Theor Comput Sci 13(1):85–108MATHCrossRefGoogle Scholar
  38. Pearl J (1986) Fusion, propagation, and structuring in belief networks. Artif Intell 29:241–288MATHCrossRefGoogle Scholar
  39. Pencole Y, Cordier M-O, Roze L (2002) A decentralized model-based diagnostic tool for complex systems. Int J on Artif Intel Tools, World Scientific Publishing Comp 11(3):327–346CrossRefGoogle Scholar
  40. Qiu W, Kumar R (2006) Decentralized failure diagnosis of discrete event systems. IEEE Trans Syst Man Cybern Part A 36(2):384–395CrossRefGoogle Scholar
  41. Qiu W, Kumar R (2006) A new protocol for distributed diagnosis, 2006 American Control Conference. MinneapolisGoogle Scholar
  42. Rauch HE, Tung F, Striebel CT (1965) Maximum likelihood estimates of linear systems. AIAA J (3):1445–1450, AugustGoogle Scholar
  43. Raynal M (1988) Distributed algorithms and protocols. Wiley & SonsGoogle Scholar
  44. Rozenberg G (ed) (1997) Handbook on graph grammars and computing by graph transformation 1 (Foundations), World ScientificGoogle Scholar
  45. Sampath M, Sengupta R, Lafortune S, Sinnamohideen K, Teneketzis D (1995) Diagnosability of discrete-event systems. IEEE Trans Automat Contr 40(9):1555–1575MATHCrossRefGoogle Scholar
  46. Su R (2004) Distributed diagnosis for discrete-event systems, PhD Thesis, Dept of Elec and Comp Eng, Univ. of TorontoGoogle Scholar
  47. Su R, Wonham, WM, Kurien J, Koutsoukos X (2002) Distributed diagnosis for qualitative systems. In: Proc. 6th int. workshop on discrete event systems, WODES’02, pp 169–174Google Scholar
  48. Su R, Wonham WM (2006) Hierarchical fault diagnosis for discrete-event systems under global consistency. J Discrete Event Dyn Syst 16(1):39–70, JanuaryMATHCrossRefGoogle Scholar
  49. Tripakis S (2004) Undecidable problems in decentralized observation and control for regular languages. In: Information Processing Letters, 15 April 2004, vol 90, Issue 1, pp 21–28Google Scholar
  50. Yoo T, Lafortune S (2002) A general architecture for decentralized supervisory control of discrete-event systems. J Discrete Event Dyn Syst 12(3):335–377, JulyMATHCrossRefGoogle Scholar
  51. Wang Y, Lafortune S, Yoo T-S (2005) Decentralized diagnosis of discrete event systems using unconditional and conditional decisions. In: Proc. of the 44th IEEE Conference on Decision and Control Sevilla, Spain, 12–15 December 2005Google Scholar
  52. Winskel G (1985) Categories of models for concurrency. Seminar on Concurrency, Carnegie-Mellon Univ., July 1984. LNCS, vol. 197, pp 246-267Google Scholar
  53. Winskel G (1997) Petri nets, algebras, morphisms, and compositionality. Inf Comput (72):197–238Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.IRISA-INRIARennesFrance

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