David Essary

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We demonstrate that predictive grouping is an effective mechanism for reducing disk arm movement, thereby simultaneously reducing energy consumption and data access latency. We further demonstrate that predictive grouping has untapped dramatic potential to further improve access performance and limit energy consumption. Data retrieval latencies are(More)
With growing storage capacities, the amount of required metadata for tracking all blocks in a system becomes a daunting task. On mobile systems, the problem is compounded by a need to make the best use of available resources. Our previous work demonstrated a system software effort in the area of predictive data grouping for reducing power and latency on(More)
With growing disk and storage capacities, the amount of required metadata for tracking all blocks in a system becomes a daunting task by itself. In previous work, we have demonstrated a system software effort in the area of predictive data grouping for reducing power and latency on hard disks. The structures used, very similar to prior efforts in(More)
The divergence of processor and storage system speeds is one of the most intensely investigated problems in computing. Yet the performance disparity remains, and further, storage energy consumption is rapidly becoming a new critical problem. While smarter caching and predictive techniques do much to alleviate this disparity, the problem persists, and data(More)
EFFECTIVE GROUPING FOR ENERGY AND PERFORMANCE: CONSTRUCTION OF ADAPTIVE, SUSTAINABLE, AND MAINTAINABLE DATA STORAGE David S. Essary, PhD University of Pittsburgh, 2011 The performance gap between processors and storage systems has been increasingly critical over the years. Yet the performance disparity remains, and further, storage energy consumption is(More)
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