An Evaluation of Architectural Alternatives for Rapidly Growing Datasets: Active Disks, Clusters, SMPs

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 con gurations were able to achieve signi cantly better performance than SMP con gurations. Active Disk con gurations were able to match (and in some cases improve upon) the performance of commodity cluster con gurations.