Hypergraph based declustering for multi-disk databases

Abstract

In very large distributed database systems, the data is declustered in order to exploit parallelism while processing a query. Declustering refers to allocating the data into multiple disks in such a way that the tuples belonging to a relation are distributed evenly across disks. There are many declustering strategies proposed in the literature, however these strategies are domain specific or have deficiencies. We propose a model that exactly fits the problem and show that iterative improvement schemes can capture detailed per-relation basis declustering objective. We provide a two phase iterative improvement based algorithm and appropriate gain functions for these algorithms. The experimental results show that the proposed algorithm provides a significant performance improvement compared to the state-of-the-art graph-partitioning based declustering strategy.