On Deploying and Executing Data-Intensive Code on SMart Autonomous Storage (SmAS) Disks
ADBIS-DASFAA '00 Proceedings of the East-European Conference on Advances in Databases and Information Systems Held Jointly with International Conference on Database Systems for Advanced Applications: Current Issues in Databases and Information Systems
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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.