Carnegie Mellon University
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Active Storage For Large-Scale Data Mining and Multimedia Applications

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journal contribution
posted on 1999-09-01, 00:00 authored by Erik Reidel, Garth Gibson, Christos Faloutsos
The increasing performance and decreasing cost of processors and memory are causing system intelligence to move into peripherals from the CPU. Storage system designers are using this trend toward “excess” compute power to perform more complex processing and optimizations inside storage devices. To date, such optimizations have been at relatively low levels of the storage protocol. At the same time, trends in storage density, mechanics, and electronics are eliminating the bottleneck in moving data off the media and putting pressure on interconnects and host processors to move data more efficiently. We propose a system called Active Disks that takes advantage of processing power on individual disk drives to run application-level code. Moving portions of an application’s processing to execute directly at disk drives can dramatically reduce data traffic and take advantage of the storage parallelism already present in large systems today. We discuss several types of applications that would benefit from this capability with a focus on the areas of database, data mining, and multimedia. We develop an analytical model of the speedups possible for scanintensive applications in an Active Disk system.We also experiment with a prototype Active Disk system using relatively low-powered processors in comparison to a database server system with a single, fast processor. Our experiments validate the intuition in our model and demonstrate speedups of 2x on 10 disks across four scan-based applications. The model promises linear speedups in disk arrays of hundreds of disks, provided the application data is large enough.




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