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Ensemble of Flexible Neural Trees for Predicting Risk in Grid Computing Environment

  • Sara AbdelwahabEmail author
  • Varun Kumar OjhaEmail author
  • Ajith AbrahamEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 424)

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.

Keywords

Risk assessment Flexible neural tree Feature selection Grid computing 

Notes

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.

References

  1. 1.
    Abdelwahab, S., Abraham, A.: A review of the risk factors in computational grid. J. Inf. Assur. Secur. 8(6), 270–278 (2013)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Carlsson, C., Fullér, R.: Probabilistic versus possibilistic risk assessment models for optimal service level agreements in grid computing. IseB 11(1), 13–28 (2013)CrossRefGoogle Scholar
  5. 5.
    Alsoghayer, R., Djemame, K.: Resource failures risk assessment modelling in distributed environments. J. Syst. Softw. 88, 42–53 (2014)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company (1994)Google Scholar
  9. 9.
    Golberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, pp. 95–99. Addion Wesley (1989)Google Scholar
  10. 10.
    Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-series forecasting using flexible neural tree model. Inf. Sci. 174(3-4), 219–235 (2005)MathSciNetCrossRefGoogle Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Chakrabarti, A., Damodaran, A., Sengupta, S.: Grid computing security: a taxonomy. IEEE Secur. Priv. 6(1), 44–51 (2008)CrossRefGoogle Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)CrossRefzbMATHGoogle Scholar
  16. 16.
    Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2010)Google Scholar
  17. 17.
    Dietterich, T.G.: Ensemble methods in machine learning. In: Multiple Classifier Systems, pp. 1–15. Springer (2000)Google Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologySudan University of Science and TechnologyKhartoumSudan
  2. 2.Computer Science and Information CollegePrincess Norah Bint Abddulrahman UniversityRiyadhSaudi Arabia
  3. 3.IT4InnovationsVSB Technical University of OstravaOstravaCzech Republic
  4. 4.Machine Intelligence Research Labs (MIR Labs)WashingtonUSA

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