An approach for solving very large scale instances of the design distribution problem for distributed database systems

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.