Towards Adaptive Migration Strategies for Mobile Agents

  • Steffen Kern
  • Peter Braun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3825)


Mobile agents were introduced as a new design paradigm for distributed systems. One advantage of mobile agents is to reduce network traffic as compared to the client-server paradigm, simply by moving code close to the data instead of moving large amount of data to the client. Unfortunately, many mobile agent toolkit suffer from to simple migration techniques. Therefore, we argue in this paper for an new migration technique that supports an adaptive decision on the code and data relocation technique. We propose several techniques for code analysis and alteration and present an algorithm to determine the optimal migration strategy within this model under the assumption of full knowledge about the application and network environment.


Mobile Agent Home Agency Code Unit Migration Strategy Call Graph 
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.


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  1. 1.
    Ball, T., Larus, J.R.: Branch prediction for free. In: Proceedings of the ACM SIGPLAN 1993 conference on Programming language design and implementation, pp. 300–313. ACM Press, New York (1993)CrossRefGoogle Scholar
  2. 2.
    Bäumer, C., Breugst, M., Choy, S., Magedanz, T.: Grasshopper — A universal agent platform based on OMG MASIF and FIPA standards. In: Karmouch, A., Impey, R. (eds.) Mobile Agents for Telecommunication Applications, Proceedings of the First International Workshop (MATA 1999), Ottawa, Canada, October 1999, pp. 1–18. World Scientific Pub., Singapore (1999)Google Scholar
  3. 3.
    Bellifimine, F., Caire, G., Poggi, A., Rimassa, G.: Jade – A White Paper. EXP in search of innovation 3(3), 6–19 (2003)Google Scholar
  4. 4.
    Braun, P., Eismann, J., Rossak, W.R.: Managing Tracy Agent Server Networks. Technical Report 12/01, Friedrich-Schiller-Universität Jena, Institut für Informatik (June 2001)Google Scholar
  5. 5.
    Braun, P., Kern, S.: Towards adaptive migration techniques for mobile agents. In: Guessoum, Z. (ed.) Fifth Workshop on Adaptive Agents and Multi- Agent Systems (AAMAS 2005), Paris, France (March 2005)Google Scholar
  6. 6.
    Braun, P., Rossak, W.R.: Mobile Agents–Basic Concept, Mobility Models, and the Tracy Toolkit. Morgan Kaufmann, San Francisco (2005)Google Scholar
  7. 7.
    Chambers, C., Grove, D., DeFouw, G., Dean, J.: Call graph construction in object-oriented languages. In: Proceedings of the ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages and Applications (OOPSLA 1997). ACM SIGPLAN Notices, vol. 32(10), pp. 108–124. ACM Press, New York (1997)Google Scholar
  8. 8.
    Cytron, R., Ferrante, J., Rosen, B.K., Wegman, M.N., Zadeck, F.K.: Efficiently computing static single assignment form and the control dependence graph. ACM Trans. Program. Lang. Syst. 13(4), 451–490 (1991)CrossRefGoogle Scholar
  9. 9.
    Grove, D., Chambers, C.: A framework for call graph construction algorithms. ACM Transactions on Programming Languages and Systems 23(6), 685–746 (2001)CrossRefGoogle Scholar
  10. 10.
    Kern, S., Braun, P., Fensch, C., Rossak, W.: Class Splitting as a Method to Reduce Migration Overhead of Mobile Agents. In: Meersman, R., Tari, Z. (eds.) OTM 2004. LNCS, vol. 3291, pp. 1358–1375. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Krintz, C., Calder, B., Hölzle, U.: Reducing transfer delay using java class file splitting and prefetching. ACM Sigplan Notices 34(10), 276–291 (1999)CrossRefGoogle Scholar
  12. 12.
    Lange, D.B., Oshima, M.: Programming and Deploying Java Mobile Agents with Aglets. Addison-Wesley, Reading (1998)Google Scholar
  13. 13.
    Patterson, J.R.C.: Accurate static branch prediction by value range propagation. In: Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation, pp. 67–78. ACM Press, New York (1995)CrossRefGoogle Scholar
  14. 14.
    Theilmann, W., Rothermel, K.: Dynamic distance maps of the internet. In: Proceedings IEEE INFOCOM 2000, The Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, Reaching the Promised Land of Communications, Tel Aviv, Israel, March 2000, vol. 1, pp. 275–284. IEEE Computer Society Press, Los Alamitos (2000)Google Scholar
  15. 15.
    Zhao, J.: Analyzing Control Flow in Java Bytecode. In: Proceedings of the 16th Conference of Japan Society for Software Science and Technology, pp. 313–316 (1999)Google Scholar
  16. 16.
    Zima, H., Chapman, B.: Supercompilers for parallel and vector computers. Addison-Wesley, Reading (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Steffen Kern
    • 1
  • Peter Braun
    • 2
  1. 1.Computer Science DepartmentFriedrich Schiller University JenaJenaGermany
  2. 2.Swinburne University of Technology, Faculty of Information TechnologiesHawthorn, VictoriaAustralia

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