Advertisement

A Self-organizing Approach to Tuple Distribution in Large-Scale Tuple-Space Systems

  • Matteo Casadei
  • Ronaldo Menezes
  • Mirko Viroli
  • Robert Tolksdorf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4725)

Abstract

A system is said to be self-organizing if its execution yields temporal global structures out of simple and local interactions amongst its constituents (e.g agents, processes). In nature, one can find many natural systems that achieve organization at the global level without a reference to the status of the global organization; real examples include ants, bees, and bacteria. The future of tuple-space systems such as Linda lies on (i) their ability to handle non-trivial coordination constructs common in complex applications, and (ii) their scalability to environments where hundreds and maybe thousands of nodes exist. The Achilles heel of scalability in current tuple-space systems is tuple organization. Legacy solutions based on antiquated approaches such as hashing are (unfortunately) commonplace. This paper gets inspiration from self-organization to improve the status quo of tuple organization in tuple-space systems. We present a solution that organizes tuples in large networks while requiring virtually no global knowledge about the system.

Keywords

Distribution Mechanism Tuple Space Coordination Language Spatial Entropy Matching Tuple 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cardelli, L.: Lecture notes in computer science. In: Wiedermann, J., van Emde Boas, P., Nielsen, M. (eds.) ICALP 1999. LNCS, vol. 1644, pp. 10–24. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    Gelernter, D., Carriero, N.: Coordination languages and their significance. Communications of the ACM 35(2), 96–107 (1992)CrossRefGoogle Scholar
  3. 3.
    Ossowski, S., Menezes, R.: On coordination and its significance to distributed and multi-agent systems. Concurrency and Computation: Practice and Experience 18(4), 359–370 (2006)CrossRefGoogle Scholar
  4. 4.
    Menezes, R., Tolksdorf, R.: A new approach to scalable linda-systems based on swarms. In: Proceedings of the ACM Symposium on Applied Computing, Melbourne, FL, USA, ACM, New York (2003)Google Scholar
  5. 5.
    Mamei, M., Zambonelli, F., Leonardi, L.: Tuples on the air: A middleware for context-aware computing in dynamic networks. In: Proceedings of the 23rd International Conference on Distributed Computing Systems, vol. 342, IEEE Computer Society, Los Alamitos (2003)Google Scholar
  6. 6.
    Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The dynamic of collective sorting robot-like ants and ant-like robots. In: Proceedings of the First International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3, pp. 356–365. MIT Press, Cambridge (1991)Google Scholar
  7. 7.
    Camazine, S., Deneubourg, J.L., Franks, N., Sneyd, J., Theraula, G., Bonabeau, E. (eds.): Self-Organization in Biological Systems. Princeton Univ. Press (2003)Google Scholar
  8. 8.
    Google Inc.: Google scholar, http://scholar.google.com
  9. 9.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. In: Santa Fe Institute Studies in the Sciences of Complexity, Oxford University Press, Inc., New York (1999)Google Scholar
  10. 10.
    Parunak, H.: Go to the ant: Engineering principles from natural multi-agent systems. Annals of Operations Research 75, 69–101 (1997)zbMATHCrossRefGoogle Scholar
  11. 11.
    Picco, G.P., Murphy, A.L., Roman, G.C.: Lime: Linda meets mobility. In: Garlan, D. (ed.) ICSE 1999. Proceedings of the 21st International Conference on Software Engineering, Los Angeles, CA, USA, pp. 368–377. ACM Press, New York (1999)Google Scholar
  12. 12.
    Snyder, J., Menezes, R.: Using Logical Operators as an Extended Coordination Mechanism in Linda. In: Arbab, F., Talcott, C.L. (eds.) COORDINATION 2002. LNCS, vol. 2315, pp. 317–331. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Wyckoff, P., McLaughry, S.W., Lehman, T.J., Ford, D.A.: T Spaces. IBM Systems Journal Special Issue on Java Technology 37(3) (1998)Google Scholar
  14. 14.
    Freeman, E., Hupfer, S., Arnold, K.: JavaSpaces Principles, Patterns and Practice. The Jini Technology Series. Addison-Wesley, Reading (1999)Google Scholar
  15. 15.
    Ltd., G.T.: Gigaspaces platform. White Paper (2002)Google Scholar
  16. 16.
    Tolksdorf, R.: Laura | A service-based coordination language. Science of Computer Programming 31(2–3), 359–381 (1998)zbMATHCrossRefGoogle Scholar
  17. 17.
    Corradi, A., Leonardi, L., Zambonelli, F.: Strategies and protocols for highly parallel Linda servers. Software Practice and Experience 28(14), 1493–1517 (1998)CrossRefGoogle Scholar
  18. 18.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001)CrossRefGoogle Scholar
  20. 20.
    Casadei, M., Gardelli, L., Viroli, M.: Simulating emergent properties of coordination in Maude: the collective sorting case. In: 5th International Workshop on Foundations of Coordination Languages and Software Architectures (FOCLASA 2006), CONCUR 2006,, Bonn, Germany, University of Málaga, Spain, pp. 57–75 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Matteo Casadei
    • 1
  • Ronaldo Menezes
    • 2
  • Mirko Viroli
    • 1
  • Robert Tolksdorf
    • 3
  1. 1.Università di Bologna, DEIS, Cesena (FC)Italy
  2. 2.Florida Tech, Computer Sciences, Melbourne, FloridaUSA
  3. 3.Freie Universität Berlin, Institut für Informatik, BerlinGermany

Personalised recommendations