Instantiation of a Generic Model for Load Balancing with Intelligent Algorithms

  • Vesna Sesum-Cavic
  • eva Kühn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5343)


In peer-to-peer networks, an important issue is the distribution of load having an impact on the overall performance of the system. The answer could be the application of an intelligent approach that leads to autonomic self-organizing infrastructures. In this position paper, we briefly introduce a framework model for load balancing that allows various load-balancing algorithms to be plugged-in, and that uses virtual shared-memory-based communication known to be advantageous for the communication of auto nomous agents in order to enable the collaboration of load-balancing agents. As the main contribution, we show how the biological concepts of bees can be mapped to the load-balancing problem, explain why we expect that bee intelligence can outperform other (un)intelligent approaches, and present an instantiation of the model with the bee intelligence algorithm. This load-balancing scheme focuses on two main policies: a transfer and a location policy for which we suggest some improvements.


load balancing bee intelligence autonomous agents 


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  1. 1.
    Androutsellis-Theotokis, S., Spinellis, D.: A survey of peer-to-peer content distribution technologies. ACM Comput. Surv. 36, 335–371 (2004)CrossRefGoogle Scholar
  2. 2.
    Backschat, M., Pfaffinger, A., Zenger, C.: Economic-Based Dynamic Load Distribution in Large Workstation Networks. In: 2nd Int. Euro-Par Conf. on Parallel Processing, France, pp. 631–634 (1996)Google Scholar
  3. 3.
    Bronevich, A.G., Meyer, W.: Load-balancing algorithms based on gradient methods and their analysis through algebraic graph theory. Parallel and Distr. Comp. 68, 209–220 (2008)CrossRefzbMATHGoogle Scholar
  4. 4.
    Camazine, S., Sneyd, J.: A model of collective nectar source selection by honey bees: Self-organization through simple rules. J. of Theoretical Biology 149(4), 547–571 (1991)CrossRefGoogle Scholar
  5. 5.
    Chen, J.C., Liao, G.X., Hsie, J.S., Liao, C.H.: A study of a contribution made by evolutionary learning on dynamic load-balancing problems in distributed computing systems. Expert Systems with Application 34, 357–365 (2008)CrossRefGoogle Scholar
  6. 6.
    Chong, C.S., Sivakumar, A.I., Low, M.Y., Gay, K.L.: A bee colony optimization algorithm to job shop scheduling. In: Proc. of the 38th Conf. on Winter Simulation, California, pp. 1954–1961 (2006)Google Scholar
  7. 7.
    Cortes, A., Ripoll, A., Cedo, F., Senar, M.A., Luque, E.: An asynchronous and iterative LB algorithm for discrete load model. Parallel and Distr. Comp. 62, 1729–1746 (2002)CrossRefzbMATHGoogle Scholar
  8. 8.
    Da Silva, D.P., Cirne, W., Brasileiro, F.V., Grande, C.: Trading Cycles for Information: Using Replication to Schedule Bag-of-Tasks. In: Kosch, H., Böszörményi, L., Hellwagner, H. (eds.) Euro-Par 2003. LNCS, vol. 2790, pp. 169–180. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Dorigo, M., Di Caro, G., Gambardella, L.: Ant colony optimization: A new meta-heuristic. In: Proc. of the Congress on Evolutionary Computation, USA, vol. 2, pp. 1470–1477 (1999)Google Scholar
  10. 10.
    Eager, D.L., Lazowska, E.D., Zahorjan, J.: Adaptive Load Sharing in Homogeneous Distributed system. IEEE Trans. on Software Engineering 12(5), 662–675 (1986)CrossRefGoogle Scholar
  11. 11.
    Grosu, D., Chronopoulos, A.T.: A Game-Theoretic Model and Algorithm for Load Balancing in Distributed Systems. In: APDCM 2002, USA, pp. 146–153 (2002)Google Scholar
  12. 12.
    Ho, C.K., Ewe, H.T.: Ant Colony Optimization Approaches for the Dynamic Load-Balanced Clustering Problem in Ad Hoc Networks. In: Swarm Intelligence Symp., Hawaii (2007)Google Scholar
  13. 13.
    Huang, Y., Garcia-Molina, H.: Publish/Subscribe in a Mobile Environment. In: 2nd Int. Workshop on Data Engineering for Wireless and Mobile Access, USA, pp. 27–34 (2001)Google Scholar
  14. 14.
    Kraus, K.: Development and Evaluation of a Load Balancer Based on Corso (in German), Praktikum, Institute for Computer Languages, TU Wien (2004)Google Scholar
  15. 15.
    Kühn, e.: Virtual Shared Memory for Distributed Architecture. Nova Science (2001)Google Scholar
  16. 16.
    Kühn, e., Mordinyi, R., Schreiber, C.: An Extensible Space-based Coordination Approach for Modeling Complex Patterns in Large Systems. In: Proc. 3rd Int. Symposium on Leveraging Applications of Formal Methods, Verification and Validation, Greece, October 13-15 (2008)Google Scholar
  17. 17.
    Kühn, e., Šešum-Cavic, V.: A Model for Self-Initiative Load Balancing Agents with Support for Swarm Intelligence and Genetic Algorithms (submitted for publication) (2008)Google Scholar
  18. 18.
    Lemmens, N., de Jong, S., Tuyls, K., Nowe, A.: Bee Behaviour in Multi-agent Systems. In: Tuyls, K., Nowe, A., Guessoum, Z., Kudenko, D. (eds.) ALAMAS 2005, ALAMAS 2006, and ALAMAS 2007. LNCS, vol. 4865, pp. 145–156. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Lin, F.C.H., Cellars, R.M.: The gradient of modelling Load-balancing Method. IEEE Trans. on Software Engineering 13(1), 32–38 (1987)CrossRefGoogle Scholar
  20. 20.
    Markovic, G., Teodorovic, D., Acimovic-Raspopovic, V.: Routing and wavelength assignment in all-optical networks based on the bee colony optimization. AI Commun. 20(4), 273–285 (2007)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Nakrani, S., Tovey, C.: On honey bees and dynamic server allocation in the Internet hosting centers. Adaptive Behaviour 12(3-4), 223–240 (2004)CrossRefGoogle Scholar
  22. 22.
    Pollak, R.: A Hierarchical Load Balancing Environment for Parallel and Distributed Supercomputer. In: Int. Symposium on Parallel and Distr. Supercomputing, Japan (1995)Google Scholar
  23. 23.
    Rodrigues, J.A.N., Monteiro, P.C.L., de Oliveira Sampaio, J., de Souza, J.M., Zimbrao, G.: Autonomic business processes scalable architecture. In: Business Process Management Workshops, pp. 78–83 (2007)Google Scholar
  24. 24.
    Rohner, M.: Load Balancing for Grid Computing (German), dipl. thesis, TU Wien (2005)Google Scholar
  25. 25.
    Shivaratri, N.G., Krueger, P.: Adaptive Location Policies for Global Scheduling. IEEE Trans. on Software Engineering 20(6), 432–444 (1994)CrossRefGoogle Scholar
  26. 26.
    Wong, L.P., Low, M.Y.H., Chong, C.S.: A Bee Colony Optimization for Traveling Salesman Problem. In: 2nd Asia Int. Conf. on Modeling & Simulation, Malaysia, pp. 818–823 (2008)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Vesna Sesum-Cavic
    • 1
  • eva Kühn
    • 1
  1. 1.Institute for Computer Languages, Space Based Computing GroupVienna University of TechnologyWienAustria

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