Abstract
Because of the irregular characteristic of Grid environment, we are unable to predict the performance using the traditional method. In this paper, we propose a novel method for predicting the performance in Grid Computing environment. The method, based on frequencies of application attributes appeared in discernibility matrix collected during a period of time; predict the applications performance that the traditional methods can’t obtain. We use the novel method in Data Ming Grid and obtain better result than traditional methods. The results of the experiment show that the use of reduct algorithm can process uncertain problem in Data Mining Grid. The theoretical foundation of ruduct provides a feasible solution to the problem of predicting Data Mining Grid.
Programs Supported by Ningbo Natural Science Foundation (2008A610028).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cannataro, M., Talia, D., Trunfio, P.: KNOWLEDGE GRID: High Performance Knowledge Discovery Services on the Grid. In: Lee, C.A. (ed.) GRID 2001. LNCS, vol. 2242, Springer, Heidelberg (2001)
Foster, I., Kesselman, C. (eds.): The Grid: Blueprint for a Future Computing Inf. Morgan Kaufmann Publishers, San Francisco (1999)
Chervenak, A., Foster, I., Kesselman, C., Salisbury, C., Tuecke, S.: The Data Grid: towards an architecture for the distributed management and analysis of large scientific datasets. J. of Network and Comp. Appl. (2001)
Downey, A.B.: Predicting Queue Times on Space-Sharing Parallel Computers. In: Proc. 11th Int’l ParallelProcessing Symp (IPPS 1997), IEEE CS Press, Los Alamitos (1997)
Gibbons, R.: A Historical Application Profiler for Use by Parallel Schedulers. In: Feitelson, D.G., Rudolph, L. (eds.) IPPS-WS 1997 and JSSPP 1997. LNCS, vol. 1291, Springer, Heidelberg (1997)
Smith, W., Taylor, V., Foster, I.: Predicting Application Runtimes Using Historical Information. In: Feitelson, D.G., Rudolph, L. (eds.) IPPS-WS 1998, SPDP-WS 1998, and JSSPP 1998. LNCS, vol. 1459, pp. 122–142. Springer, Heidelberg (1998)
Smith, W., Taylor, V., Foster, I.: Using Runtime Predictions to Estimate Queue Wait Times and Improve Scheduler Performance. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 1999, IPPS-WS 1999, and SPDP-WS 1999. LNCS, vol. 1659, pp. 202–229. Springer, Heidelberg (1999)
Orlando, S., Palmerini, P., Perego, R., Silvestri, F.: Scheduling high performance data mining tasks on a data grid environment. In: Proceedings of Europar (2002)
Damangir, S., Jafarijashemi, G., Zohoor, H.: Modelling of a complex system using the dynamic rule prediction. WSEAS Transactions on Systems 5(12), 2833–2838 (2006)
Saulia, L., Chen, P., Koji, N.: Intelligent method of sinkage prediction for tracked vehicles using possibility theory and fuzzy neural network. WSEAS Transactions on Systems 6(6), 1110–1115 (2007)
Olej, V.: Design of the models of neural networks and the Takagi-Sugeno fuzzy inference system for prediction of the gross domestic product development. WSEAS Transactions on Systems 4(4), 314–319 (2005)
Allen, G., Benger, W., Goodale, T., Hege, H., Lanfermann, G., Merzky, A., Radke, T., Seidel, E., Shalf, J.: The Cactus Code: A Problem Solving Environment for the Grid. In: Proceedings of the Ninth International Symposium on High Performance Distributed Computing (HPDC). IEEE Press, Pittsburgh
Marzullo, K., Ogg, M., Ricciardi, A., Amoroso, A., Calkins, F., Rothfus, E.: NILE: Wide-Area Computing for High Energy Physics. In: Proceedings of 7th ACM SIGOPS European Workshop, Connemara, Ireland, September 2-4, 1996, ACM Press, New York (1996)
Globus Toolkit, http://www.globus.org/ogsa/releases/alpha/
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, K. (2008). Predicting Grid Performance Based on Novel Reduct Algorithm. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-85565-1_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85564-4
Online ISBN: 978-3-540-85565-1
eBook Packages: Computer ScienceComputer Science (R0)