Dynamic Data Allocation Methods in Distributed Database System

Abstract

Allocation of data or fragments in distributed database is a critical design issue and requires the most effort. It has the greater impact on the quality of the final solution and hence the operational efficiency of the system. Performance of the distributed database system is heavily dependent on allocation of data among the different sites over the network. The static allocation provides only the limited response to the change in workload. So, choosing an appropriate technique for allocation in the distributed database system is an important design issue. In this paper, a new dynamic data allocation for distributed database system has been proposed. The proposed methods reallocates data with respect to the changing data access patterns with time constraint. This methods will decrease the movement of data over the network and also improve the overall performance of the system. A new dynamic data allocation algorithm for non-replicated distributed database systems (DDS), namely the NNA algorithm. This algorithm reallocates data with respect to changing data access pattern for each fragment. In this algorithm, data is moved to a node which is the neighborhood and also placed in the path to the node with maximum access counter. This algorithm is very suitable for DDS in the networks which have low bandwidth and frequent requests for a data fragment come from different sites by providing data clustering. The simulation results show that for complex and large networks where the request for fragments generates more frequently or the fragment size is large, the NNA algorithm provides better response time and spends less time for moving fragments in the network.