The Artificial Bee Colony Algorithm Applied to a Self-adaptive Grid Resources Selection Model

  • María Botón-Fernández
  • Miguel Á. Vega-Rodríguez
  • Francisco Prieto Castrillo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)


Swarm intelligence algorithms are used to simulate the behaviour of non-centralized and self-organizing systems, which could be natural or artificial. Grid computing environments are distributed systems comprised heterogeneous and geographically distributed resources. This computing paradigm presents problems related to resources management (discovery, monitoring and selection processes) which are caused by its dynamic and changing nature. These problems lead to a bad application performance due to the fact that resources availability and characteristics vary over time. In recent years, several approaches based on adaptation and defined from a system point of view have been proposed. The present contribution is focussed on enhancing the grid resources selection process by providing a self-adaptive ability to grid applications. A selection model based on the Artificial Bee Colony algorithm is described. In contrast to other alternatives, the model is defined from a user point of view (the model has not control on the internal grid components). Finally, the approach is tested in a real European grid infrastructure. The results show that both a reduction in execution time and an increase in the successfully completed tasks rate are achieved.


Artificial Bee Colony Optimization Grid Computing Self-adaptive Ability Swarm Intelligence 


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  1. 1.
    Foster, I.: What is the Grid? A three Point Checklist. GRIDtoday 1(6), 22–25 (2002)MathSciNetGoogle Scholar
  2. 2.
    Karaboga, D.: An Idea based on Honey Bee Swarm for Numerical Optimization. Technical Report-tr06, Erciyes University, Turkey (2005)Google Scholar
  3. 3.
    Berman, F., Wolski, R., Casanova, H., Cirne, W., Dail, H., Faerman, M., Figueira, S., Hayes, J., Obertelli, G., Schopf, J., Shao, G., Smallen, S., Spring, N., Su, A., Zagorodnov, D.: Adaptive Computing on the Grid Using AppLeS. IEEE Transactions on Parallel and Distributed Systems 14(4), 369–382 (2003)CrossRefGoogle Scholar
  4. 4.
    Huedo, E., Montero, R.S., Llorente, I.M.: A Framework for Adaptive Execution in Grids. Software-Practice & Experience 34(7), 631–651 (2004)CrossRefGoogle Scholar
  5. 5.
    Keung, H.N.L.C., Dyson, J.R.D., Jarvis, S.A., Nudd, G.R.: Self- Adaptive and Self-Optimising Resource Monitoring for Dynamic Grid Environments. In: Proceedings of the 15th International Workshop on Database and Expert Systems Applications, DEXA 2004, Washington DC, USA, pp. 689–693 (2004)Google Scholar
  6. 6.
    Vadhiyar, S.S., Dongarra, J.J.: Self Adaptivity in Grid Computing. Concurrency and Computation: Practice and Experience 17(2-4), 235–257 (2005)CrossRefGoogle Scholar
  7. 7.
    Groen, D., Harfst, S., Portegies Zwart, S.: On the Origin of Grid Species: The Living Application. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009, Part I. LNCS, vol. 5544, pp. 205–212. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Cameron, D., Gholami, A., Karpenko, D., Konstantinov, A.: Adaptive Data Management in the ARC Grid Middleware. Journal of Physics: Conference Series 331 (2011)Google Scholar
  9. 9.
    Batista, D.M., Da Fonseca, L.S.: A Survey of Self-adaptive Grids. IEEE Communications Magazine 48(7), 94–100 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • María Botón-Fernández
    • 1
  • Miguel Á. Vega-Rodríguez
    • 2
  • Francisco Prieto Castrillo
    • 1
  1. 1.Dept. Science and TechnologyCeta-CiematTrujilloSpain
  2. 2.Dept. Technologies of Computers and CommunicationsUniv. ExtremaduraCáceresSpain

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