Abstract
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.
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Paletta, M., Herrero, P. (2009). Foreseeing Cooperation Behaviors in Collaborative Grid Environments. In: Demazeau, Y., Pavón, J., Corchado, J.M., Bajo, J. (eds) 7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2009). Advances in Intelligent and Soft Computing, vol 55. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00487-2_13
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DOI: https://doi.org/10.1007/978-3-642-00487-2_13
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