Divide-and-conquer scheme for strictly optimal retrieval of range queries

Abstract

Declustering distributes data among parallel disks to reduce retrieval cost using I/O parallelism. Many schemes were proposed for single copy declustering of spatial data. Recently, declustering using replication gained a lot of interest and several schemes with different properties were proposed. It is computationally expensive to verify optimality of replication schemes designed for range queries and existing schemes verify optimality for up to 50 disks. In this article, we propose a novel method to find replicated declustering schemes that render all spatial range queries optimal. The proposed scheme uses threshold based declustering, divisibility of large queries for optimization and optimistic approach to compute maximum flow. The proposed scheme is generic and works for any number of dimensions. Experimental results show that using 3 copies there exist allocations that render all spatial range queries optimal for up to 750 disks in 2 dimensions and with the exception of several values for up to 100 disks in 3 dimensions. The proposed scheme improves search for strictly optimal replicated declustering schemes significantly and will be a valuable tool to answer open problems on replicated declustering.