Improved Bounds and Schemes for the Declustering Problem

The declustering problem is to allocate given data on parallel working storage devices in such a manner that typical requests find their data evenly distributed on the devices. Using deep results from discrepancy theory, we improve previous work of several authors concerning range queries to higher-dimensional data. We give a declustering scheme with an additive error of O d (log d−1 M ) independent of the data size, where d is the dimension, M the number of storage devices and d − 1 does not exceed the smallest prime power in the canonical decomposition of M into prime powers. In particular, our schemes work for arbitrary M in dimensions two and three. For general d, they work for all M ≥ d − 1 that are powers of two. Concerning lower bounds, we show that a recent proof of a Ω d (log d−1 2 M ) bound contains an error. We close the gap in the proof and thus establish the bound. * supported by the DFG-Graduiertenkolleg 357 "Effiziente Algorithmen und

show abstract

Improved Bounds and Schemes for the Declustering Problem

Cited by 3 publications

References 18 publications

On a Non-monotonicity Effect of Similarity Measures

On a Non-monotonicity Effect of Similarity Measures

Latin squares and low discrepancy allocation of two-dimensional data

Improved bounds and schemes for the declustering problem

Contact Info

Product

Resources

About