A Fuzzy, Utility-Based Approach for Proactive Policy-Based Management

  • Christoph Frenzel
  • Henning Sanneck
  • Bernhard Bauer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8035)


Policy-based Management with rules is a wide-spread approach for operations automation. However, the continuous pressure for decreasing operational costs and increasing reliability of the systems lead to new challenges. Unfortunately, current Policy-based Management Systems lack the ability to act proactively along operational objectives in an autonomous manner in order to face these challenges. In this paper, we present a Policy-based Management System based on a Fuzzy Logic System that attempts to avoid problematic system states before they occur and that is guided by operator objectives expressed as utilities. Our approach can be seen as an extension of current rule-based Policy-based Management Systems, thus, requiring a reduced implementation effort.


Policy-based Management Proactive Management Fuzzy Logic Utility Theory Rational Decision Making 


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  1. 1.
    Bartolini, C., Sallé, M., Trastour, D.: IT service management driven by business objectives: an application to incident management. In: Proc. 10th IEEE/IFIP Network Operations and Management Symposium (NOMS 2006), pp. 45–55. IEEE, Vancouver (2006)Google Scholar
  2. 2.
    Bellman, R.E., Zadeh, L.A.: Decision-Making in a Fuzzy Environment. Tech. Rep. NASA CR-1594, National Aeronautics and Space Administration (1970)Google Scholar
  3. 3.
    Boutaba, R., Aib, I.: Policy-based Management: A Historical Perspective. Journal of Network and Systems Management 15(4), 447–480 (2007)CrossRefGoogle Scholar
  4. 4.
    Boutalis, Y., Schmidt, K.: Multi-objective decision making using fuzzy discrete event systems: A mobile robot example. In: Proc. 18th Mediterranean Conference on Control and Automation (MED 2010), pp. 575–580. IEEE, Marrakech (2010)CrossRefGoogle Scholar
  5. 5.
    Cingolani, P., Alcala-Fdez, J.: jFuzzyLogic: a robust and flexible Fuzzy-Logic inference system language implementation. In: Proc. IEEE International Conference on Fuzzy Systems (Fuzz 2012), pp. 1–8. IEEE, Brisbane (2012)CrossRefGoogle Scholar
  6. 6.
    Domshlak, C., Hüllermeier, E., Kaci, S., Prade, H.: Preferences in AI: An overview. Artificial Intelligence 175(7-8), 1037–1052 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Frenzel, C., Sanneck, H., Bauer, B.: Rational Policy System for Network Management. In: Proc. International Symposium on Integrated Network Management (IM 2013). IEEE, Ghent (2013)Google Scholar
  8. 8.
    Hämäläinen, S., Sanneck, H., Sartori, C.: LTE Self-organizing Networks (SON): Network Management Automation for Operational Efficiency. Wiley, Chichester (2011)CrossRefGoogle Scholar
  9. 9.
    International Electronical Commission (IEC): IEC 1131 - Programmable Controllers: Part 7 - Fuzzy Control Programming (January 1997),
  10. 10.
    Kephart, J., Walsh, W.: An artificial intelligence perspective on autonomic computing policies. In: Proc. IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY 2004), pp. 3–12. IEEE, Yorktown Heights (2004)Google Scholar
  11. 11.
    Kousaridas, A., Nguengang, G.: Deliverable D2.3: Final Report on Self-Management Artefacts. Tech. rep., Self-Management of Cognitive Future InterNET Elements, Self-NET (2010)Google Scholar
  12. 12.
    Kruse, R., Gebhardt, J., Klawonn, F.: Foundations of Fuzzy Systems. Wiley, New York (1994)Google Scholar
  13. 13.
    Lupu, E., Sloman, M.: Conflicts in policy-based distributed systems management. IEEE Transactions on Software Engineering 25(6), 852–869 (1999)CrossRefGoogle Scholar
  14. 14.
    O’Hagan, M.: A Fuzzy Decision Maker,
  15. 15.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Upper Saddle River (2003)Google Scholar
  16. 16.
    Strassner, J.: Policy-Based Network Management: Solutions for the Next Generation. Morgan Kaufmann, Amsterdam (2004)Google Scholar
  17. 17.
    Zimmermann, H.J.: Fuzzy Programming and Linear Programming with Several Objective Functions. Fuzzy Sets and Systems 1(1), 45–55 (1978)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christoph Frenzel
    • 1
    • 2
  • Henning Sanneck
    • 2
  • Bernhard Bauer
    • 1
  1. 1.Department of Computer ScienceUniversity of AugsburgAugsburgGermany
  2. 2.Nokia Siemens Networks ResearchMunichGermany

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