Dynamic Consistency Analysis for Convergent Operators

  • Alva L. Couch
  • Marc Chiarini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5127)


It has been shown that sets of convergent operators with a shared fixed point can simulate autonomic control mechanisms, but many questions remain about this management technique. We discuss how an autonomous agent can reason about whether its convergent operators share a fixed point with the operators of other agents. Using a concept of time based upon operator repetition, we show that a failure to achieve convergence within specific time limits can be used as a probabilistic indicator of inconsistencies in local policy. We describe a statistical inference technique that determines if an agent’s promise strategy is feasible. The strengths of this technique are that it is both scale-invariant and exterior to the operators whose consistency is being evaluated.


Autonomic Computing Logical Consistency Ubiquitous Computing Environment Policy Consistency Containment Relationship 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Burgess, M., Couch, A.: Autonomic computing approximated by fixed-point promises. In: Proceedings of the First IEEE International Workshop on Modeling Autonomic Communication Environments (MACE), pp. 197–222. Multicon Verlag (2006)Google Scholar
  2. 2.
    Couch, A., Chiarini, M.: A theory of closure operators. In: AIMS (submitted, 2008)Google Scholar
  3. 3.
    Burgess, M., Couch, A.L.: Modeling next generation configuration management tools. In: LISA, USENIX, pp. 131–147 (2006)Google Scholar
  4. 4.
    Anderson, P.: Configuration Management. SAGE Short Topics in System Administration. USENIX (2007)Google Scholar
  5. 5.
    Couch, A.: Configuration management. In: Bergstra, J., Burgess, M. (eds.) Handbook of Network and System Administration, pp. 75–133. Elsevier, Inc., Amsterdam (2007)Google Scholar
  6. 6.
    Couch, A.L., Daniels, N.: The maelstrom: Network service debugging via ”ineffective procedures”. In: LISA, USENIX, pp. 63–78 (2001)Google Scholar
  7. 7.
    Lupu, E., Sloman, M.: Conflicts in policy-based distributed systems management. IEEE Trans. Software Eng. 25(6), 852–869 (1999)CrossRefGoogle Scholar
  8. 8.
    Dunlop, N., Indulska, J., Raymond, K.: Dynamic conflict detection in policy-based management systems. In: EDOC, pp. 15–26. IEEE Computer Society, Los Alamitos (2002)Google Scholar
  9. 9.
    Couch, A.L., Sun, Y.: On observed reproducibility in network configuration management. Sci. Comput. Program 53(2), 215–253 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Lamport, L.: Time, clocks, and the ordering of events in a distributed system. Communications of the ACM 21(7), 558–565 (1978)CrossRefzbMATHGoogle Scholar
  11. 11.
    Burgess, M.: A site configuration engine. Computing Systems 8(2), 309–337 (1995)Google Scholar
  12. 12.
    Burgess, M., Ralston, R.: Distributed resource administration using cfengine. Softw., Pract. Exper. 27(9), 1083–1101 (1997)CrossRefGoogle Scholar
  13. 13.
    Burgess, M.: Theoretical system administration. In: LISA, USENIX, pp. 1–13 (2000)Google Scholar
  14. 14.
    Burgess, M.: Computer immunology. In: LISA, USENIX, pp. 283–298 (1998)Google Scholar
  15. 15.
    Burgess, M.: Cfengine as a component of computer immune-systems. In: Proceedings of the Norwegian Conference on Informatics (1998)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Alva L. Couch
    • 1
  • Marc Chiarini
    • 1
  1. 1.Tufts UniversityMedfordUSA

Personalised recommendations