Abstract
Adaptive scheduling strategies are about considering the state of computational grids to obtain efficient and reliable schedules and to prevent the system performance deterioration. In this work, emerging adaptive strategies in grid computing, namely Fuzzy Rule-Based Systems (FRBS) -based strategies and a new adaptive scheduling approach, gaussian scheduling founded on Gaussian Mixture Models (GMMs) are compared. Both types of strategies focus on modeling the state of resources and select the most convenient site of the grid at every scheduling step given the current conditions. FRBSs provide a fuzzy characterization of the grid state and the inference of a suitability index based on their own knowledge given in the form of fuzzy IF-THEN rules. Besides, a GMM can be trained to model a complex probability density distribution indicating the suitability of every site in the grid to be the target of the schedule with the current conditions of its resources. This way the GMM scheduler assigns a probability to every state of the site where a higher probability is associated to a higher suitability of selection. Simulations based on real grid facilities are conducted to test the FRBS and GMM-based models and results are analyzed in terms of accuracy and convergence behaviour of their associated learning processes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cordón, O., Herrera, F., Hoffmann, F., Magdalena, L.: Genetic fuzzy systems: Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific Pub. Co. Inc. (2001)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience (2000)
Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers Inc., San Francisco (2003)
C.N.G. Infrastructure: Metacentrum data sets meta (2009), http://www.fi.muni.cz/~xklusac/index.php?page=
Klusáček, D., Matyska, L., Rudová, H.: Alea – Grid Scheduling Simulation Environment. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2007. LNCS, vol. 4967, pp. 1029–1038. Springer, Heidelberg (2008)
Klusacek, D., Rudova, H.: Improving QoS in computational Grids through schedule-based approach. In: Scheduling and Planning Applications Workshop at the Eighteenth International Conference on Automated Planning and Scheduling (ICAPS 2008), Sydney, Australia (2008)
Mohammed, A.B., Altmann, J., Hwang, J.: Cloud computing value chains: Understanding businesses and value creation in the cloud. In: Neumann, D., Baker, M., Altmann, J., Rana, O. (eds.) Economic Models and Algorithms for Distributed Systems, Autonomic Systems, pp. 187–208. Birkhäuser Basel (2010)
Prado, R.P., García-Galán, S., Expósito, J.E.M., Yuste, A.J., Bruque, S.: Learning of Fuzzy Rule-Based Meta-schedulers for Grid Computing with Differential Evolution. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. CCIS, vol. 80, pp. 751–760. Springer, Heidelberg (2010)
Prado, R., García-Galán, S., Yuste, A., Muñoz Expósito, J.: Genetic fuzzy rule-based scheduling system for grid computing in virtual organizations. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 1–17 (2010)
Šustr, Z., Sitera, J., Mulač, M., Ruda, M., Antoš, D., Hejtmánek, L., Holub, P., Salvet, Z., Matyska, L.: MetaCentrum, the Czech Virtualized NGI (2009)
Xhafa, F., Abraham, A.: Computational models and heuristic methods for grid scheduling problems. Future Generation Computer Systems 26(4), 608–621 (2010)
Yu, G., Sun, J., Li, C.: Machine performance assessment using gaussian mixture model (gmm). In: 2nd International Symposium on Systems and Control in Aerospace and Astronautics, ISSCAA 2008, pp. 1–6 (2008), doi:10.1109/ISSCAA.2008.4776183
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Prado, R.P. et al. (2012). Gaussian Mixture Models vs. Fuzzy Rule-Based Systems for Adaptive Meta-scheduling in Grid/Cloud Computing. In: Casillas, J., Martínez-López, F., Corchado Rodríguez, J. (eds) Management Intelligent Systems. Advances in Intelligent Systems and Computing, vol 171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30864-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-30864-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30863-5
Online ISBN: 978-3-642-30864-2
eBook Packages: EngineeringEngineering (R0)