Study of Scalable Declustering Algorithms for Parallel Grid Files
Study of Scalable Declustering Algorithms for Parallel Grid Files
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Date
1998-10-15
Authors
Moon, Bongki
Acharya, Anurag
Saltz, Joel
Advisor
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Abstract
Efficient storage and retrieval of large multidimensional datasets is
an important concern for large-scale scientific computations such as
long-running time-dependent simulations which periodically generate
snapshots of the state.
The main challenge for efficiently handling such datasets
is to minimize response time for multidimensional range queries.
The grid file is one of the well known access methods for
multidimensional and spatial data.
We investigate effective and scalable declustering techniques
for grid files with the primary goal of minimizing response time
and the secondary goal of maximizing the fairness of data distribution.
The main contributions of this paper are (1) analytic and experimental
evaluation of existing index-based declustering techniques and their
extensions for grid files, and (2) development of a proximity-based
declustering algorithm called {\em minimax} which is experimentally
shown to scale and to consistently achieve better response time
compared to available algorithms while maintaining perfect disk distribution.
(Also cross-referenced as UMIACS-TR-96-4)