Abstract
In this paper we address the problem of allocation scheme design of large database-objects in the Web environment, which may suffer significant changes in usage and access patterns and scaling of data. In these circumstances, if the design is not adjusted to new changes, the system can undergo severe degradations in data access costs and response time. Since this problem is NP-complete, obtaining optimal solutions for large problem instances requires applying approximate methods. We present a mathematical model to generate a new object allocation scheme and propose a new method to solve it. The method uses a Hopfield neural network with the mean field annealing (MFA) variant. The experimental results and a comparative study with other two methods are presented. The new method has a similar capacity to solve large problem instances, regular level of solution quality and excellent execution time with respect to other methods.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Oszu, M., Valduries, P.: Principles of Distributed Database Systems. Prentice-Hall, Englewood Cliffs (1991)
Pérez, J., Pazos, R., Frausto, J., Romero, D., Cruz, L.: Vertical Fragmentation and Allocation in Distributed Databases with Site Capacity Restrictions Using Threshold Accepting Algorithm. In: Cairó, O., Cantú, F.J. (eds.) MICAI 2000. LNCS(LNAI), vol. 1793, pp. 75–81. Springer, Heidelberg (2000)
Pérez, J., Pazos, R., Vélez, L., Rodríguez, G.: Automatic Generation of Control Parameters for the Threshold Accepting Algorithm. In: Coello Coello, C.A., de Albornoz, Á., Sucar, L.E., Battistutti, O.C. (eds.) MICAI 2002. LNCS (LNAI), vol. 2313, pp. 118–127. Springer, Heidelberg (2002)
Pérez, J., Pazos, R., Romero, D., Santaolaya, R., Rodríguez, G., Sosa, V.: Adaptive and Scalable Allocation of Data-Objects in the Web. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds.) ICCSA 2003. LNCS, vol. 2667, pp. 134–143. Springer, Heidelberg (2003)
Díaz, A., González, J., Laguna, M., Moscato, P., Tseng, F., Glover, F., Ghaziri, F.: Optimización Heurística y Redes Neuronales. Editorial Paraninfo 235 (1996)
Hilera, J., Martínez, V.: Redes Neuronales Artificiales, Fundamentos, Modelos y Aplicaciones. Editorial Alfaomega 390 (1995)
Peterson, C., Söderberg, B.: Neural Optimization. The Handbook of Brain Research and Neural Networks. 2nd edn. MIT Press, Cambridge (1995)
Smith, K.: Neural Networks for Combinatorial Optimization: A review of More Than a Decade of Research. Journal of Computing 11(1) (1999)
Smith, K.: Neural Techniques for Combinatorial Optimization with Applications. IEEE Transactions on Neural Networks 9(6) (1998)
Shaharuddin, S., Zomaya, A.: Multiprocessor Scheduling Using Mean Field Annealing, http://ipdps.eece.unm.edu/1998/biosp3/bipp9.pdf
Bultan, T., Aykanat, C.: A New Mapping Heuristic Based on Mean Field Annealing. Intel Supercomputer Systems Division and Research Council
Vélez, L.: Esquema de Enfriamiento Adaptivo para el Algoritmo de Aceptación por Umbral Aplicado al Diseño de Bases de Datos Distribuidas. MS Thesis, Instituto Tecnológico de León, León (2000)
Ceri, S., Pernici, B., Wiederhold, G.: Distributed Database Design Methodologies. Proc. IEEE 75(5), 533–546 (1987)
Ohlsson, M., Peterson, M., Söderberg, C. B.: Neural Networks for Optimization Problems with Inequality Constraints- the Knapsack Problem. Department of Theorical Physics. University of Lund, Sweden
Colunga, A., Muñoz, L., Ramírez, M.: Agente Inteligente de Aprendizaje Reforzado Aplicado al Diseño de Bases de Datos Distribuidas. BS Thesis, Instituto Tecnológico de Cd. Madero (2001)
González, J., Fraire, H., Moreno, A., Muñoz, L., Ramírez, M.: Agente Inteligente de Aprendizaje Reforzado Aplicado al Diseño de Base de Datos Distribuidas. In: Procs. IEEE, CIECE (2001)
Laurence, C.: Evaluación del Modelo DFAR Usando Tabu Search. Technical report, Instituto Tecnológico de Cd. Madero (2001)
Pérez, J., Pazos, R.A., Romero, D., Cruz, L.: Análisis de Complejidad del Problema de la Fragmentación Vertical y Reubicación Dinámica en Bases de Datos Distribuidas. In: Proceeding of 7mo. Congreso Internacional de Investigación en Ciencias Computacionales (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pérez O., J., Pazos R., R.A., Fraire H., H.J., Cruz R., L., Pecero S., J.E. (2003). Adaptive Allocation of Data-Objects in the Web Using Neural Networks. In: Cappelli, A., Turini, F. (eds) AI*IA 2003: Advances in Artificial Intelligence. AI*IA 2003. Lecture Notes in Computer Science(), vol 2829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39853-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-39853-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20119-9
Online ISBN: 978-3-540-39853-0
eBook Packages: Springer Book Archive