Efficient range query processing over DHTs based on the balanced Kautz tree

Abstract

SUMMARY Distributed Hash Tables (DHTs) are scalable, self-organizing, and adaptive to underlying topology changes, thus being a promising infrastructure for hosting large-scale distributed applications. The ever-wider use of DHT infrastructures has found more and more applications that require support for range queries. Recently, a number of DHT-based range query schemes have been proposed. However, most of them suffer from high query delay or imbalanced load distribution. To address these problems, in this paper we first present an efficient indexing structure called Balanced Kautz (BK) tree that uniformly maps the m-dimensional data space onto DHT nodes, and then propose a BK tree-based range query scheme called ERQ that processes range queries in a parallel fashion and guarantees to return the results in a bounded delay. In a DHT with N nodes, ERQ can answer any range of query in less than log N (2 log log N +1) hops in a load-balanced manner, irrespective of the queried range, the whole space size, or the number of queried attributes. The effectiveness of our proposals is demonstrated through experiments.

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