Foreseeing Cooperation Behaviors in Collaborative Grid Environments

  • Mauricio Paletta
  • Pilar Herrero
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 55)


Balancing the load of the computational nodes is one important problem in the management of distributed computing, specifically collaborative grid environments. Some models were developed to deal with this problem. Most of them have in common a lack of ability to learn from previous experience in order to make predictions about future collaborations in the environment. Therefore improving the way in which equilibrium/balance is done, and to optimize efforts, resources, and time spent. This paper presents CoB-ForeSeer (Cooperation Behavior Foreseer), a new learning strategy proposal to solve the particular problem presented above. This strategy is based on neural network technology, specifically on Radial Based Function Network (RBFN). The paper also presents the way in which this learning strategy is properly configured and its corresponding evaluation. Results show that CoB-ForeSeer can successfully learn (because it reaches an acceptable average error) previous cooperation in the grid, and use this knowledge to improve new scenarios where collaboration is needed.


Learning prediction collaboration RBFN Grid Computing 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mauricio Paletta
    • 1
  • Pilar Herrero
    • 2
  1. 1.Departamento de Ciencia y TecnologíaUniversidad Nacional Experimental de Guayana (UNEG)BolívarVenezuela
  2. 2.Facultad de InformáticaUniversidad Politécnica de Madrid (UPM)Spain

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