Abstract
Assessing risk associated with computational grid is an essential need for both the resource providers and the users who runs applications in grid environments. In this chapter, we modeled the prediction process of risk assessment (RA) in grid computing utilizing meta-learning approaches in order to improve the performance of the individual predictive models. In this chapter, four algorithms were selected as base classifiers, namely isotonic regression, instance base knowledge (IBK), randomizable filtered classified tree, and extra tree. Two meta-schemes, known as voting and multi schemes, were adopted to perform an ensemble risk prediction model in order to have better performance. The combination of prediction models was compared based on root mean-squared error (RMSE) to find out the best suitable algorithm. The performance of the prediction models is measured using percentage split. Experiments and assessments of these methods are performed using nine datasets for grid computing risk factors. Empirical results illustrate that the prediction performance is enhanced by predictive model using ensemble methods.
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References
Sangrasi, A., Djemame, K.: Component level risk assessment in grids: a probabilistic risk model and experimentation. In: 2011 Proceedings of the 5th IEEE International Conference on Digital Ecosystems and Technologies Conference (DEST). IEEE (2011)
Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid. Berman et al. [2] 171–197 (2003)
Foster, I., Kesselman, C., Nick, J.M., Tuecke, S.: The physiology of the grid. Grid Comput.: Making Glob. Infrastruct. Reality 217–249 (2003)
Djemame, K., Gourlay, I., Padgett, J., Birkenheuer, G., Hovestadt, M., Kao, O., Voss, K.: Introducing risk management into the grid. In: Second IEEE International Conference on e-Science and Grid Computing, 2006, e-Science’06. IEEE (2006)
Rokach, L.: Ensemble methods in supervised learning. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 959–979. Springer, Berlin (2010)
Jones, J.: An introduction to factor analysis of information risk (fair). Norwich J. Inform. Assur. 2(1), 67 (2006)
Alberts, C.J., Dorofee, A.: Managing Information Security Risks: The OCTAVE Approach. Addison-Wesley Longman Publishing Co., Inc., Boston (2002)
Yadav, J.S., Jain, M.Y.A.: Risk assessment models and methodologies. Int. J. Sci. Res. Educ. 1(06) (2014)
Sangrasi, A., Djemame, K.: Risk assessment modeling in grids at component level: considering grid resources as repairable. In: Omatu, S. et al. (eds.) Distributed Computing and Artificial Intelligence, pp. 321–330. Springer, Berlin (2012)
Sangrasi, A., Djemame, K., Jokhio, I.A.: Aggregating node level risk assessment in grids using an R-out-of-N model. In: Chowdhry, B.S., Shaikh, F.K., Akbar Hussain, D.M., Aslam Uqaili, M. (eds.) Emerging trends and applications in information communication technologies, pp. 445–452. Springer, Berlin (2012)
Carlsson, C., Fullér, R.: Probabilistic versus possibilistic risk assessment models for optimal service level agreements in grid computing. Inf. Syst. e-Bus. Manage. 11(1), 13–28 (2013)
Alsoghayer, R., Djemame, K.: Probabilistic risk assessment for resource provision in grids. In: Proceedings of the 25th UK Performance Engineering Workshop, Leeds (2009)
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 in grid computing. In: Carlsson, C., Fullér R. (eds.) Possibility for Decision, pp. 145–165. Springer, Berlin (2011)
Carlsson, C., Fullér R.: Risk assessment of SLAs in grid computing with predictive probabilistic and possibilistic models. In: Greco, S., Pereira, R.A.M., Squillante, M., Yager, R.R., Kacprzyk, J. (eds.) Preferences and Decisions, pp. 11–29. Springer, Berlin (2010)
Wu, C.H., Su, W.H., Ho, Y.W.: A study on GPS GDOP approximation using support-vector machines. IEEE Trans. Instrum. Measur. 60(1), 137–145 (2011)
Chauhan, H., Kumar, V., Pundir, S., Pilli, E.S.: A comparative study of classification techniques for intrusion detection. In: 2013 International Symposium on Computational and Business Intelligence (ISCBI). IEEE (2013)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)
Désir, C., Petitjean, C., Heutte, L., Salaun, M., Thiberville, L.: Classification of endomicroscopic images of the lung based on random subwindows and extra-trees. IEEE Trans. Biomed. Eng. 59(9), 2677–2683 (2012)
Vilalta, R., Giraud-Carrier, C., Brazdil, P.: Meta-learning-concepts and techniques. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 717–731. Springer, Berlin (2010)
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Abdelwahab, S., Abraham, A. (2015). Risk Assessment for Grid Computing Using Meta-Learning Ensembles. In: Abraham, A., Muda, A., Choo, YH. (eds) Pattern Analysis, Intelligent Security and the Internet of Things. Advances in Intelligent Systems and Computing, vol 355. Springer, Cham. https://doi.org/10.1007/978-3-319-17398-6_23
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DOI: https://doi.org/10.1007/978-3-319-17398-6_23
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