Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Coordination Languages and Models

COORDINATION 2012: Coordination Models and Languages pp 230–244Cite as

  1. Home
  2. Coordination Models and Languages
  3. Conference paper
A Space-Based Generic Pattern for Self-Initiative Load Clustering Agents

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

  • Eva Kühn17,
  • Alexander Marek17,
  • Thomas Scheller17,
  • Vesna Sesum-Cavic17,
  • Michael Vögler17 &
  • …
  • Stefan Craß17 
  • Conference paper
  • 587 Accesses

  • 3 Citations

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 7274)

Abstract

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.

Keywords

  • Agents
  • Load Clustering
  • Load Balancing
  • Coordination
  • Tuple Space

Download conference paper PDF

References

  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)

    CrossRef  Google Scholar 

  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. 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)

    CrossRef  Google Scholar 

  4. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)

    CrossRef  MATH  Google Scholar 

  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. 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. 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. 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)

    CrossRef  Google Scholar 

  9. Denti, E., Omicini, A.: An architecture for tuple-based coordination of multi-agent systems. Softw. Pract. Exper. 29, 1103–1121 (1999)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  MathSciNet  MATH  Google Scholar 

  12. Gowda, K.C., Krishna, G.: Agglomerative clustering using the concept of mutual nearest neighbourhood. Pattern Recognition 10(2), 105–112 (1978)

    CrossRef  MATH  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  15. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31, 264–323 (1999)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. 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)

    CrossRef  Google Scholar 

  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)

    CrossRef  MathSciNet  MATH  Google Scholar 

  20. Mhamdi, F., Elloumi, M.: A new survey on knowledge discovery and data mining. In: RCIS, pp. 427–432 (2008)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. 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. 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. 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)

    CrossRef  Google Scholar 

  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. 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. 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. 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)

    CrossRef  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Languages, Vienna University of Technology, Argentinierstr. 8, Vienna, Austria

    Eva Kühn, Alexander Marek, Thomas Scheller, Vesna Sesum-Cavic, Michael Vögler & Stefan Craß

Authors
  1. Eva Kühn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Marek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Scheller
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vesna Sesum-Cavic
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael Vögler
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Stefan Craß
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Science, Reykjavik University, Menntavegur 1, 101, Reykjavik, Iceland

    Marjan Sirjani

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 IFIP International Federation for Information Processing

About this paper

Cite this paper

Kühn, E., Marek, A., Scheller, T., Sesum-Cavic, V., Vögler, M., Craß, S. (2012). A Space-Based Generic Pattern for Self-Initiative Load Clustering Agents. In: Sirjani, M. (eds) Coordination Models and Languages. COORDINATION 2012. Lecture Notes in Computer Science, vol 7274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30829-1_16

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-30829-1_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30828-4

  • Online ISBN: 978-3-642-30829-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature