The Decision Function

  • Philippe Lalanda
  • Julie A. McCann
  • Ada Diaconescu
Part of the Undergraduate Topics in Computer Science book series (UTICS)


In the previous chapters, we saw how self-managed systems could accumulate information about their execution context and how they could adapt their own internal structures. We now focus on the decision function that links sensory inputs to actuating outputs. This function heavily relies on the notion of knowledge (knowledge about the system internals, knowledge about the computing environment, knowledge about ways to solve problems) and as well as the ability to reason about this knowledge. There are many different ways to represent knowledge in computing science, and a wide range of reasoning techniques have been proposed, in particular in the artificial intelligence community.

The purpose of this section is to present different knowledge representations and associated reasoning techniques well suited to autonomic systems. It is not meant to be exhaustive. In fact, there is no such thing as a general knowledge representation of reasoning approach for autonomic management. Depending on the requirements, different formalisms and techniques with different properties can be selected.


Bayesian Network Autonomic System Knowledge Representation Propositional Logic Architectural Model 
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.
    Turing, A.: Computing machinery and intelligence. Mind LIX(36), 433–460 (1950)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Russel, S., Norvig, P.: Artificial Intelligence, a Modern Approach. Prentice Hall, Englewood Cliffs (2010)Google Scholar
  3. 3.
    Osogami, T., Harchol-Balter, M., Scheller-Wolf, A.: Analysis of cycle stealing with switching times and thresholds. Perform. Eval. 61(4), 347–369 (2005)CrossRefGoogle Scholar
  4. 4.
    Sharma, V., Thomas, A., Abdelzaher, T., Skadron, K., Lu, Z.: Power-aware qos management in web servers. In: RTSS’03: Proceedings of the 24th IEEE International Real-Time Systems Symposium, p. 63. IEEE Computer Society, Washington, DC (2003)Google Scholar
  5. 5.
    Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theor. Comput. Sci. 344(2–3), 243–278 (2005). doi: 10.1016/j.tcs.2005.05.020. MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Chollet, S., Lalanda, P.: An extensible Abstract Service Orchestration Framework. In: Proceedings of the IEEE 7th International Conference on Web Services (ICWS 09), Los Angeles, CA, 6 July 2009Google Scholar
  7. 7.
    France, R., Rumpe, B.: Model-driven development of complex software: a research roadmap. In: FOSE’07: 2007 Future of Software Engineering, pp. 37–54. IEEE Computer Society, Washington, DC (2007)Google Scholar
  8. 8.
    OMG.: Unified Modeling Language (UML). Feb 2009
  9. 9.
    OMG.: Meta-Object Facility (MOFTM) specification, version 1.4.­bin/doc?formal/2002-04-03. Apr 2002
  10. 10.
    Herrmann, C., Holger Krahn, H., Rumpe, B., Schindler, M., Völkel, S.: An algebraic view on the semantics of model composition. In: Model Driven Architecture – Foundations and Applications. Lecture Notes in Computer Science, vol. 4530, pp. 99–113. Springer, Berlin/Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Garlan, D., Schmerl, B., Chang, J.: Using gauges for architecture-based monitoring and adaptation. In: Working Conference on Complex and Dynamic Systems Architecture, Brisbane, Australia (2001)Google Scholar
  12. 12.
    Magee, J., Dulay, N., Eisenbach, S., Kramer, J. (eds.).: Specifying distributed software architectures. In: Proceedings of 5th European Software Engineering Conference (ESEC ‘95), Sitges. LNCS 989, pp. 137–153. Springer, Berlin/Heidelberg (1995)Google Scholar
  13. 13.
    Jackson, D.: Alloy: a lightweight object modelling notation. Softw.Eng. Methodol. 11(2), 256–290 (2002)CrossRefGoogle Scholar
  14. 14.
    Georgiadis, I., Magee, J., Kramer, J.: Self-organising software architectures for distributed systems. In: Proceedings of the First Workshop on Self-Healing Systems, Charleston, South Carolina, USA (2002)Google Scholar
  15. 15.
    Garlan, D., Schmerl, B.: Exploiting architectural design knowledge to support self- repairing systems. In: Proceedings of the 14th International Conference on Software Engineering and Knowledge Engineering, 15–19 July, Ischia Island, Italy (2002)Google Scholar
  16. 16.
    Garlan, D., Schmerl, B.: Model-based adaptation for self-healing systems. In: Proceedings of the First Workshop on Self-Healing Systems, Charleston, South Carolina, USA (2002)Google Scholar
  17. 17.
    Oreizy, P., Medvidovic, N., Taylor, R.N.: Architecture-based runtime software evolution. In: ICSE’98: Proceedings of the 20th International Conference on Software Engineering, pp. 177–186. IEEE Computer Society, Washington, DC (1998)Google Scholar
  18. 18.
    Dashofy, E.M., van der Hoek, A., Taylor, R.N.: Towards architecture-based self-healing systems. In: Proceedings of the First Workshop on Self-Healing Systems, Charleston, South Carolina, USA (2002)Google Scholar
  19. 19.
    Dearle, A., Kirby, G.N.C., McCarthy, A.J.: A framework for constraint-based development and autonomic management of distributed applications. In: Proceedings of International Conference on Autonomic Computing, 2004, pp. 300–301, 17–18 May 2004Google Scholar
  20. 20.
    McCann, J.A., Huebscher, M., Hoskins, A.: Context as autonomic intelligence in a ubiquitous computing environment. Int. J. Internet Protocol Technol. (IJIPT) special edition on Autonomic Computing 2(1), 30–39, Inderscience Publishers, Geneva, SwitzerlandGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Philippe Lalanda
    • 1
  • Julie A. McCann
    • 2
  • Ada Diaconescu
    • 3
  1. 1.Laboratoire Informatique de GrenobleUniversité Joseph FourierGrenobleFrance
  2. 2.Department of ComputingImperial College LondonLondonUK
  3. 3.Department of Computing and NetworkingTélécom ParisTechParisFrance

Personalised recommendations