Abstract
Prediction of risk assessment is demanding because it is one of the most important contributory factors towards grid computing. Hence, researchers were motivated for developing and deploying grids on diverse computers, which is responsible for spreading resources across administrative domains so that resource sharing becomes effective. Risk assessment in grid computing can analyses possible risks, that is, the risk of growing computational requirements of an organization. Thus, risk assessment helps in determining these risks. In this, we present an adaptive neuro-fuzzy inference system that can provide an insight of predicting the risk environment. The main goal of this paper is to obtain empirical results with an illustration of high performance and accurate results. We used data mining tools to determine the contributing attributes so that we can obtain the risk prediction accurately.
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References
Abdelwahab, S., Abraham, A.: A review of the risk factors in computational grid. J. Inf. Assur. Secur. 8 (2013)
Hossain, S.J., Ahmad, N.: Adaptive neuro-fuzzy inference system (ANFIS) based surface roughness prediction model for ball end milling operation. J. Mech. Eng. Res. 4, 112–129 (2012)
Abraham, A.: Adaptation of fuzzy inference system using neural learning. In: Nedjah, N. et al. (eds.) Fuzzy Systems Engineering: Theory and Practice. Studies in Fuzziness and Soft Computing, pp. 53–83. Springer, Germany (2005)
Abraham, A.: Neuro fuzzy systems: state-of-the-art modeling techniques. In: Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, pp. 269–276. Springer (2001)
Bey, K.B., Benhammadi, F., Mokhtari, A., Guessoum, Z.: CPU load prediction model for distributed computing. In: Eighth International Symposium on Parallel and Distributed Computing, 2009. Ispdc’09, pp. 39–45 (2009)
Karthika, B., Deka, P.C.: Prediction of air temperature by hybridized model (wavelet-ANFIS) using wavelet decomposed data. Aquat. Procedia 4, 1155–1161 (2015)
Djemame, K., Gourlay, I., Padgett, J., Birkenheuer, G., Hovestadt, M., Kao, O.V., Kerstin: Introducing risk management into the grid. In: Second IEEE International Conference on e-Science and Grid Computing, 2006. e-Science’06, pp. 28–28 (2006)
Sangrasi, A., Djemame, K.: Component level risk assessment in grids: a probablistic risk model and experimentation. In: Proceedings of the 5th IEEE International Conference on Digital Ecosystems and Technologies Conference (DEST), 2011, pp. 68–75 (2011)
Negoita, C., Zadeh, L., Zimmermann, H.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)
Carlsson, C., Fullér, R.: Risk assessment of SLAs in grid computing with predictive probabilistic and possibilistic models. In: Preferences and Decisions, pp. 11–29. Springer (2010)
Gourlay, I., Djemame, K., Padgett, J.: Reliability and risk in grid resource brokering. In: 2nd IEEE International Conference on Digital Ecosystems and Technologies, 2008. DEST 2008, pp. 437–443 (2008)
Raju, N., Gottumukkala, Y.L., Leangsuksun, C.B., Nassar, R., Scott, S.: Reliability analysis in HPC clusters. In: Proceedings of the High Availability and Performance Computing Workshop (2006)
Abdelwahab, S., Abraham, A.: Data mining approach for modeling risk assessment in computational grid. In: Computational Intelligence in Data Mining, vol. 3, pp. 673–684. Springer (2015)
Hall, M.A., Smith, L.A.: Feature subset selection: a correlation based filter approach. Presented at the Proceedings of the Fourth International Conference on Neural Information Processing and Intelligent Information Systems (ICONIP’97). New Zealand, Berlin (1997)
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, 536–547 (2013)
Chakrabarti, A.: Taxonomy of grid security issues. In: Grid Computing Security, pp. 33–47. Springer (2007)
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)
Chakrabarti, A., Damodaran, A., Sengupta, S.: Grid computing security: a taxonomy. IEEE Secur. Priv. 44–51 (2008)
Kar, S., Sahoo, B.: An anamaly detection system for DDOS attack in grid computing. Int. J. Comput. Appl. Eng. Technol. Sci. (IJ-CA-ETS), 1, 553–557 (2009)
Naqvi, S., Riguidel, M.: Threat model for grid security services. In: Advances in Grid Computing-EGC 2005, pp. 1048–1055. Springer (2005)
Barua, A., Mudunuri, L.S., Kosheleva, O.: Why trapezoidal and triangular membership functions work so well: towards a theoretical explanation. J. Uncertain Syst. 8 (2013)
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). Neuro-Fuzzy Risk Prediction Model for Computational Grids. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. Advances in Intelligent Systems and Computing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-29504-6_13
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