Foreseeing Cooperation Behaviors in Collaborative Grid Environments
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.
KeywordsLearning prediction collaboration RBFN Grid Computing
Unable to display preview. Download preview PDF.
- 3.Herrero, P., Bosque, J.L., Salvadores, M., Pérez, M.S.: AMBLE: An Awareness Model for Balancing the Load in collaborative grid Environments. In: Proc. The 7th IEEE/ACM International Conference on Grid Computing, Barcelona, Spain, pp. 246–253 (2006)Google Scholar
- 4.Muller, B., Reinhardt, J., Strickland, M.T.: Neural Networks An Introduction, 2nd edn. Springer, Heidelberg (1995)Google Scholar
- 5.Ritter, H., Martinetz, T., Schulten, K.: Neural Networks. ISBN: 3−89319−131−3 (1991)Google Scholar
- 6.Benford, S.D., Fahlén, L.E.: A Spatial Model of Interaction in Large Virtual Environments. In: Proc. of the Third European Conference on Computer Supported Cooperative Work, Milano, Italy, pp. 109–124. Kluwer Academic Publishers, Dordrecht (1993)Google Scholar
- 7.Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration. Globus Project (2002)Google Scholar
- 9.Lingireddy, S., Ormsbee, L.E.: Neural Networks in Optimal Calibration of Water Distribution Systems. Artificial Neural Networks for Civil Engineers: Advanced Features and Applications, 53–76 (1998)Google Scholar
- 14.Qingkui, C., Lichun, N.: Multiagent Learning Model in Grid. International Journal of Computer Science and Network Security (IJCSNS) 6(8B), 54–59 (2006)Google Scholar
- 15.Qingkui, C.: Cooperative Learning Model of Agents in Multi-cluster Grid. In: Proc. 11th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 418–423 (2007) 10.1109/CSCWD.2007.4281472Google Scholar
- 16.Schikuta, E., Weishaupl, T.: N2Grid: neural networks in the grid. In: Proc. IEEE International Joint Conference on Neural Networks, vol. 2, pp. 1409–1414 (2004)Google Scholar