Gaussian Mixture Models vs. Fuzzy Rule-Based Systems for Adaptive Meta-scheduling in Grid/Cloud Computing
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.
KeywordsGrid Computing Gaussian Mixture Model Schedule Strategy Grid Schedule Central Process Unit
Unable to display preview. Download preview PDF.
- 1.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)Google Scholar
- 2.Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience (2000)Google Scholar
- 3.Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers Inc., San Francisco (2003)Google Scholar
- 4.C.N.G. Infrastructure: Metacentrum data sets meta (2009), http://www.fi.muni.cz/~xklusac/index.php?page=
- 6.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)Google Scholar
- 7.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)Google Scholar
- 8.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)CrossRefGoogle Scholar
- 9.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)Google Scholar
- 10.Šustr, Z., Sitera, J., Mulač, M., Ruda, M., Antoš, D., Hejtmánek, L., Holub, P., Salvet, Z., Matyska, L.: MetaCentrum, the Czech Virtualized NGI (2009)Google Scholar
- 12.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.2008.4776183Google Scholar