A Space-Based Generic Pattern for Self-Initiative Load Clustering Agents

  • Eva Kühn
  • Alexander Marek
  • Thomas Scheller
  • Vesna Sesum-Cavic
  • Michael Vögler
  • Stefan Craß
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7274)


Load clustering is an important problem in distributed systems, which proper solution can lead to a significant performance improvement. It differs from load balancing as it considers a collection of loads, instead of normal data items, where a single load can be described as a task. Current approaches that treat load clustering mainly lack of provisioning a general framework and autonomy. They are neither agent-based nor configurable for many topologies. In this paper we propose a generic framework for self-initiative load clustering agents (SILCA) that is based on autonomous agents and decentralized control. SILCA is a generic architectural pattern for load clustering. The SILCA framework is the corresponding implementation and thus supports exchangeable policies and allows for the plugging of different algorithms for load clustering. It is problem independent, so the best algorithm or combination of algorithms can be found for each specific problem. The pattern has been implemented on two levels: In its basic version different algorithms can be plugged, and in the extended version different algorithms can be combined. The flexibility is proven by means of nine algorithms. Further contributions are the benchmarking of the algorithms, and the working out of their best combinations for different topologies.


Agents Load Clustering Load Balancing Coordination Tuple Space 


  1. 1.
    Achtert, E., Kriegel, H.-P., Zimek, A.: ELKI: A Software System for Evaluation of Subspace Clustering Algorithms. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 580–585. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Avgeriou, P., Zdun, U.: Architectural patterns in practice. In: Longshaw, A., Zdun, U. (eds.) EuroPLoP, pp. 731–734. UVK - Universitaetsverlag Konstanz (2005)Google Scholar
  3. 3.
    Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Sieb, C., Thiel, K., Wiswedel, B.: KNIME: The konstanz information miner. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds.) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 319–326. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)zbMATHCrossRefGoogle Scholar
  5. 5.
    Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees, 1st edn. Chapman and Hall/CRC (January 1984)Google Scholar
  6. 6.
    Chekanov, S.: Hep data analysis using jhepwork and java. In: Proceedings of the Workshop HERA and the LHC, 2nd Workshop on the Implications of HERA for LHC Physics (2008)Google Scholar
  7. 7.
    Craß, S., Kühn, E.: Coordination-based access control model for space-based computing. In: 27th Annual ACM Symposium on Applied Computing (2012)Google Scholar
  8. 8.
    Demšar, J., Zupan, B., Leban, G., Curk, T.: Orange: From Experimental Machine Learning to Interactive Data Mining. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 537–539. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Denti, E., Omicini, A.: An architecture for tuple-based coordination of multi-agent systems. Softw. Pract. Exper. 29, 1103–1121 (1999)CrossRefGoogle Scholar
  10. 10.
    Dobson, S., Denazis, S., Fernández, A., Gaïti, D., Gelenbe, E., Massacci, F., Nixon, P., Saffre, F., Schmidt, N., Zambonelli, F.: A survey of autonomic communications. ACM Trans. Auton. Adapt. Syst. 1, 223–259 (2006)CrossRefGoogle Scholar
  11. 11.
    Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics 3(3), 32–57 (1973)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Gowda, K.C., Krishna, G.: Agglomerative clustering using the concept of mutual nearest neighbourhood. Pattern Recognition 10(2), 105–112 (1978)zbMATHCrossRefGoogle Scholar
  13. 13.
    Gowda, K.C., Ravi, T.V.: Divisive clustering of symbolic objects using the concepts of both similarity and dissimilarity. Pattern Recognition 28(8), 1277–1282 (1995)CrossRefGoogle Scholar
  14. 14.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11, 10–18 (2009)CrossRefGoogle Scholar
  15. 15.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31, 264–323 (1999)CrossRefGoogle Scholar
  16. 16.
    Krishna, K., Narasimha-Murty, M.: Genetic K-means algorithm. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 29(3), 433–439 (1999)CrossRefGoogle Scholar
  17. 17.
    Kühn, E., Mordinyi, R., Keszthelyi, L., Schreiber, C.: Introducing the concept of customizable structured spaces for agent coordination in the production automation domain. In: The Eighth International Conference on Autonomous Agents and Multiagent Systems, AAMAS, May 10-15, pp. 625–632 (2009)Google Scholar
  18. 18.
    Kühn, E., Sesum-Cavic, V.: A Space-Based Generic Pattern for Self-Initiative Load Balancing Agents. In: Aldewereld, H., Dignum, V., Picard, G. (eds.) ESAW 2009. LNCS, vol. 5881, pp. 17–32. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Kuo, R.J., Wang, H.S., Hu, T.L., Chou, S.H.: Application of ant k-means on clustering analysis. Comput. Math. Appl. 50, 1709–1724 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Mhamdi, F., Elloumi, M.: A new survey on knowledge discovery and data mining. In: RCIS, pp. 427–432 (2008)Google Scholar
  21. 21.
    Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: rapid prototyping for complex data mining tasks. In: KDD 2006: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 935–940. ACM, New York (2006)CrossRefGoogle Scholar
  22. 22.
    Otero, F.E.B., Freitas, A.A., Johnson, C.G.: Handling continuous attributes in ant colony classification algorithms. In: CIDM, pp. 225–231. IEEE (2009)Google Scholar
  23. 23.
    Parpinelli, R., Lopes, H., Freitas, A.: Data Mining with an Ant Colony Optimization Algorithm. IEEE Trans. on Evolutionary Computation, Special Issue on Ant Colony Algorithms 6(4), 321–332 (2002)Google Scholar
  24. 24.
    Phyu, T.N.: Survey of classification techniques in data mining. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists 2009, IMECS 2009, Hong Kong, March 18-20. Lecture Notes in Engineering and Computer Science, vol. I, pp. 727–731. International Association of Engineers, Newswood Limited (2009)Google Scholar
  25. 25.
    Šešum-Čavić, V., Kühn, E.: Chapter 8 Self-Organized Load Balancing through Swarm Intelligence. In: Bessis, N., Xhafa, F. (eds.) Next Generation Data Technologies for Collective Computational Intelligence. SCI, vol. 352, pp. 195–224. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  26. 26.
    Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An ant colony approach for clustering. Analytica Chimica Acta 509(1) (2004)Google Scholar
  27. 27.
    Sonnenburg, S., Rätsch, G., Henschel, S., Widmer, C., Behr, J., Zien, A., de Bona, F., Binder, A., Gehl, C., Franc, V.: The SHOGUN Machine Learning Toolbox. Journal of Machine Learning Research (2010)Google Scholar
  28. 28.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn., ch. 8. Addison-Wesley Longman Publishing Co., Inc., Boston (2005)Google Scholar
  29. 29.
    Tiwari, R., Husain, M., Gupta, S., Srivastava, A.: Improving ant colony optimization algorithm for data clustering. In: Proceedings of the International Conference and Workshop on Emerging Trends in Technology, ICWET 2010, pp. 529–534. ACM, New York (2010)CrossRefGoogle Scholar
  30. 30.
    Weiss, D.: A Clustering Interface For Web Search Results In Polish And English. Master’s thesis, Poznan University of Technology, Poland (2001)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Eva Kühn
    • 1
  • Alexander Marek
    • 1
  • Thomas Scheller
    • 1
  • Vesna Sesum-Cavic
    • 1
  • Michael Vögler
    • 1
  • Stefan Craß
    • 1
  1. 1.Institute of Computer LanguagesVienna University of TechnologyViennaAustria

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