Abstract
Risk assessment in grid computing is an important issue as grid is a shared environment with diverse resources spread across several administrative domains. Therefore, by assessing risk in grid computing, we can analyze possible risks for the growing consumption of computational resources of an organization and thus we can improve the organization’s computation effectiveness. In this paper, we used a function approximation tool, namely, flexible neural tree for risk prediction and risk (factors) identification. Flexible neural tree is a feed forward neural network model, where network architecture was evolved like a tree. Our comprehensive experiment finds score for each risk factor in grid computing together with a general tree-based model for predicting risk. We used an ensemble of prediction models to achieve generalization.
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Abdelwahab, S., Abraham, A.: A review of the risk factors in computational grid. J. Inf. Assur. Secur. 8(6), 270–278 (2013)
Abdelwahab, S., Ojha, V.K., Abraham, A.: Neuro-fuzzy risk prediction model for computational grids. In: The Second International Afro-European Conference for Industrial Advancement. Springer (2015)
Djemame, K., Gourlay, I., Padgett, J., Birkenheuer, G., Hovestadt, M., Kerstin, Kao, O.V.: Introducing risk management into the grid. In: Second IEEE International Conference on e-Science and Grid Computing, e-Science’06, pp. 28 (2006)
Carlsson, C., Fullér, R.: Probabilistic versus possibilistic risk assessment models for optimal service level agreements in grid computing. IseB 11(1), 13–28 (2013)
Alsoghayer, R., Djemame, K.: Resource failures risk assessment modelling in distributed environments. J. Syst. Softw. 88, 42–53 (2014)
Carlsson, C., Fullér, R.: Risk assessment of SLAs in grid computing with predictive probabilistic and possibilistic models. In: Greco, S. et al. (eds.) Preferences and Decisions, pp. 11–29. Springer, Berlin (2010)
Sangrasi, A., Djemame, K.: Component level risk assessment in grids: a probablistic risk model and experimentation. In: IEEE International Conference on Digital Ecosystems and Technologies Conference (DEST). IEEE (2011)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company (1994)
Golberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, pp. 95–99. Addion Wesley (1989)
Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-series forecasting using flexible neural tree model. Inf. Sci. 174(3-4), 219–235 (2005)
Rana, O.F., Warnier, M., Quillinan, T.B., Brazier, F., Cojocarasu, D.: Managing violations in service level agreements. In: Grid Middleware and Services, pp. 349–358. Springer (2008)
Syed, R.H., Syrame, M., Bourgeois, J.: Protecting grids from cross-domain attacks using security alert sharing mechanisms. Future Gener. Comput. Syst. 29(2), 536–547 (2013)
Chakrabarti, A., Damodaran, A., Sengupta, S.: Grid computing security: a taxonomy. IEEE Secur. Priv. 6(1), 44–51 (2008)
Lee, H.M., Chung, K.S., Jin, S.H., Lee, D.-W., Lee, W.G., Jung, S.Y.Y., Chang, H.: A fault tolerance service for QoS in grid computing. In: Computational Science—ICCS 2003, pp. 286–296. Springer (2003)
Smith, M., Friese, T., Engel, M., Freisleben, B.: Countering security threats in service-oriented on-demand grid computing using sandboxing and trusted computing techniques. J. Parallel Distrib. Comput. 66, 1189–1204 (2006)
Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2010)
Dietterich, T.G.: Ensemble methods in machine learning. In: Multiple Classifier Systems, pp. 1–15. Springer (2000)
Mendes-Moreira, J., Soares, C., Jorge, A.M., Sousa, J.F.D.: Ensemble approaches for regression: a survey. In: ACM Computing Surveys (CSUR), vol. 45, p. 10 (2012)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Acknowledgments
This work was supported by the IPROCOM Marie Curie Initial Training Network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007–2013/, under REA grant agreement number 316555.
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Abdelwahab, S., Ojha, V.K., Abraham, A. (2016). Ensemble of Flexible Neural Trees for Predicting Risk in Grid Computing Environment. In: Snášel, V., Abraham, A., Krömer, P., Pant, M., Muda, A. (eds) Innovations in Bio-Inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-28031-8_13
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DOI: https://doi.org/10.1007/978-3-319-28031-8_13
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