An Evaluation of Architectural Alternatives for Rapidly Growing
Datasets, Active Disks, Clusters, SMPs
An Evaluation of Architectural Alternatives for Rapidly Growing
Datasets, Active Disks, Clusters, SMPs
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Date
1998-12-08
Authors
Uysal, Mustafa
Acharya, Anurag
Saltz, Joel
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Abstract
Growth and usage trends for several large datasets indicate that there is a
need for architectures that scale the processing power as the dataset
increases. In this paper, we evaluate three architectural alternatives for
rapidly growing and frequently reprocessed datasets: active disks,
clusters, and shared memory multiprocessors (SMPs). The focus of this
evaluation is to identify potential bottlenecks in each of the alternative
architectures and to determine the performance of these architectures for
the applications of interest. We evaluate these architectural alternatives
using a detailed simulator and a suite of nine applications. Our results
indicate that for most of these applications Active Disk and cluster
configurations were able to achieve significantly better performance than
SMP configurations. Active Disk configurations were able to match (and in
some cases improve upon) the performance of commodity cluster
configurations. (Also cross-referenced as UMIACS-TR-98-68)