An Approach for Solving Very Large Scale Instances of the Design Distribution Problem for Distributed Database Systems
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In this paper we deal with the solution of very large instances of the design distribution problem for distributed databases. Traditionally the capacity for solving large scale instances of NP-hard problems has been limited by the available computing resources and the efficiency of the solution algorithms. In contrast, in this paper we present a new solution approach that permits to solve larger instances using the same resources. This approach consists of the application of a systematic method for transforming an instance A into a smaller instance A’ that has a large representativeness of instance A. For validating our approach we used a mathematical model developed by us, whose solution yields the design of a distributed database that minimizes its communication costs. The tests showed that the solution quality of the transformed instances was on the average 10.51% worse than the optimal solution; however, the size reduction was 97.81% on the average. We consider that the principles used in the proposed approach can be applied to the solution of very large instances of NP-hard problems of other problem types.
KeywordsLarge Instance Geometric Program Original Instance Large Scale Instance Distribute Database System
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- 3.Barr, R.S., Golden, B.L., Kelly, J., Steward, W.R., Resende, M.G.C.: Guidelines for Designing and Reporting on Computational Experiments with Heuristic Methods. In: Proceedings of International Conference on Metaheuristics for Optimization, pp. 1–17. Kluwer Publishing, Norwell (2001)Google Scholar
- 4.Michalewicz, Z., Fogel, D.B.: How to Solve It: Modern Heuristics. Springer, Heidelberg (1999)Google Scholar
- 5.Pérez, J., Pazos, R.A., Frausto, J., Romero, D., Cruz, L.: Vertical Fragmentation and Allocation in Distributed Databases with Site Capacity Restrictions Using the Threshold Accepting Algorithm. In: Cairó, O., Cantú, F.J. (eds.) MICAI 2000. LNCS, vol. 1793, pp. 75–81. Springer, Heidelberg (2000)CrossRefGoogle Scholar
- 6.Pérez, J., Pazos, R.A., Frausto, J., Rodríguez, G., Cruz, L., Fraire, H., Mora, G.: Self-Tuning Mechanism for Genetic Algorithms Parameters, an Application to Data-Object Allocation in the Web. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3046, pp. 77–86. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 7.Ceri, S., Navathe, S., Wiederhold, G.: Distribution Design of Logical Database Schemes. In: Proc. IEEE Transactions on Software Engineering, vol. SE-9(4), pp. 487–503 (1983)Google Scholar
- 8.Navathe, S., Ceri, S., Wiederhold, G., Dou, L.: Vertical Partitioning Algorithms for Database Design. In: ACM Trans. On Database Systems, vol. 9(4), pp. 680–710 (1984)Google Scholar
- 12.Visinescu, C.: Incremental Data Distribution on Internet-Based Distributed Systems: A Spring System Approach: Master of Mathematics in Computer Science thesis, supervised by Tamer Ozsu; University of Waterloo (2003)Google Scholar
- 14.Stamatopoulos, C.: Observations on the Geometrical Propeties of Accuracy Growth in Sampling with Finite Populations. FAO Fisheries Technical Paper 388, Food and Agricultura Organization of the United Nations, Rome (1999) ISSN 0249-9345 Google Scholar