skip to main content
10.1145/2677832.2677835acmotherconferencesArticle/Chapter ViewAbstractPublication PagesinternetwareConference Proceedingsconference-collections
Article

User preference based autonomic generation of self-adaptive rules

Published:17 November 2014Publication History

ABSTRACT

The internetware system is a complex and distributed self-adaptive system, which challenges the method for making adaptation plans. Rule based approaches are very efficient to make plans in adaptive systems. To enable effective rule-based adaptation, we need to write a set of well behaved self-adaptive rules which could always lead to desirable states. This adaptive rules-set needs to be correct, com- plete, conflicts-free and well satisfy user goals, and it should updates according to user preferences. However, it is a difficult task for sys- tem users to define such a set of rules. To resolve this problem, we provide an rule generation engine, which could automatically generate well behaved self-adaptive rules according to user pref- erences. The rule generation engine is realized by a three-stage algorithm: stage 1 integrates user goals and user preferences, stage 2 establishes 1-1 tracing relationship between a context state and its desirable software configuration, stage 3 extracts self-adaptive rules from the tracing relationship between context states and software configurations. We will apply this engine to generate self-adaptive rules for a smart phone system, and evaluate the quality of generated self-adaptive rules.

References

  1. (2013). {Online}. Available: http://www.nudt.edu.cn/internetware2013/Google ScholarGoogle Scholar
  2. D. S. F. A. G. D. G. E. M. F. Z. F. obson, S., “A survey of autonomic communications.” in ACM Transactions on Autonomous and Adaptive Systems (TAAS), vol. 1, no. 2, 2006, pp. 223–159. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. G. D. M. G. C. G. H. K. H. L. M.. S. M.. Brun, Y., “Engineering self-adaptive systems through feedback loops,” in Software Engineering for Self-Adaptive Systems. Springer Berlin Heidelberg., pp. 48–70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. M. Bashari, “Engineering self-adaptive systems synamic software product line,” in Tutorial at SPLC, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. K. Kang, J. Lee, and P. Donohoe, “Feature-oriented product line engineering,” Software, IEEE, vol. 19, no. 4, pp. 58–65, Jul 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S. R. W, “The analytic hierarchy process—what it is and how it is used,” Mathematical Modelling, no. 9(3), pp. 161–176, 1987.Google ScholarGoogle Scholar
  7. P.-C. David and T. Ledoux, “An infrastructure for adaptable middleware,” in On the Move to Meaningful Internet Systems 2002: CoopIS, DOA, and ODBASE. Springer, 2002, pp. 773–790. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. L. Capra, W. Emmerich, and C. Mascolo, “Carisma: Context-aware reflective middleware system for mobile applications,” Software Engineering, IEEE Transactions on, vol. 29, no. 10, pp. 929–945, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. L. Capra, “Reflective mobile middleware for context-aware applications,” Ph.D. dissertation, University of London, 2003.Google ScholarGoogle Scholar
  10. Q. Wang, “Towards a rule model for self-adaptive software,” in ACM SIGSOFT Software Engineering Notes, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. Batory, “Feature models, grammars, and propositional formulas.” in Proc. of SPLC, ser. LNCS 3714. Springer–Verlag, 2005, pp. 7–20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Acher, P. Collet, F. Fleurey, P. Lahire, S. Moisan, and J.-P. Rigault, “Modeling Context and Dynamic Adaptations with Feature Models,” in Proceedings of the 4th International Workshop [email protected], United States, Oct. 2009, p. 10. {Online}. Available: http://hal.archives-ouvertes.fr/hal-00419990Google ScholarGoogle Scholar
  13. H. Hartmann and T. Trew, “Using feature diagrams with context variability to model multiple product lines for software supply chains,” in Software Product Line Conference, 2008. SPLC ’08. 12th International, Sept 2008, pp. 12–21. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. J. O. Kephart and R. Das, “Achieving self-management via utility functions,” Internet Computing, IEEE, vol. 11, no. 1, pp. 40–48, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. R. L.. L. A. Rosa, L., “Self-management of distributed systems using high-level goal policies.” in Software Engineering for Self-Adaptive Systems. Springer Berlin Heidelberg., 2013, pp. 162–190.Google ScholarGoogle ScholarCross RefCross Ref
  16. J. Floch, S. Hallsteinsen, E. Stav, F. Eliassen, K. Lund, and E. Gjorven, “Using architecture models for runtime adaptability,” Software, IEEE, vol. 23, no. 2, pp. 62–70, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. B. Morin, O. Barais, J. Jezequel, F. Fleurey, and A. Solberg, “Models@ run. time to support dynamic adaptation,” Computer, vol. 42, no. 10, pp. 44–51, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. S. She, “Feature model mining,” Master’s thesis, University of Waterloo, 2008.Google ScholarGoogle Scholar

Index Terms

  1. User preference based autonomic generation of self-adaptive rules

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          Internetware '14: Proceedings of the 6th Asia-Pacific Symposium on Internetware
          November 2014
          152 pages
          ISBN:9781450333030
          DOI:10.1145/2677832
          • General Chairs:
          • Hong Mei,
          • Jian Lv,
          • Program Chairs:
          • Minghui Zhou,
          • Charles Zhang

          Copyright © 2014 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 17 November 2014

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • Article

          Acceptance Rates

          Overall Acceptance Rate55of111submissions,50%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader