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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 considered a major bottleneck, and with growing volumes of data and increased storage needs it is only growing in significance. Data storage infrastructure is therefore a growing consumer of energy at data-center scales, while the individual disk is already a significant concern for mobile computing (accounting for almost a third of a mobile system's energy demands). While improving responsiveness of storage subsystems and hence reducing latencies in data retrieval is often considered contradictory with efforts to reduce disk energy consumption, we demonstrate that predictive data grouping has the potential to simultaneously work towards both these goals. Predictive data grouping has advantages in its applicability compared to both prior approaches to reducing latencies and to reducing energy usage. For latencies, grouping can be performed opportunistically, thereby avoiding the serious performance penalties that can be incurred with prior applications of access prediction (such as predictive prefetching of data). For energy, we show how predictive grouping can even save energy use for an individual disk that is never idle. Predictive data grouping with effective replication results in a reduction of the overall mechanical movement required to retrieve data. We have built upon our detailed measurements of disk power consumption, and have estimated both the energy expended by a hard disk for its mechanical components, and that needed to move the disk arm. We have further compared, via simulation, three models of predictive grouping of on-disk data, including an optimal arrangement of data that is guaranteed to minimize disk arm movement. These experiments have allowed us to measure the limits of performance improvement achievable with optimal data grouping and replication strategies on a single device, and have further allowed us to demonstrate the potential of such schemes to reduce energy consumption of mechanical components by up to 70%.

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Cited by 6 publications

References 29 publications

ISRA-Based Grouping: A Disk Reorganization Approach for Disk Energy Conservation and Disk Performance Enhancement

ISRA-Based Grouping: A Disk Reorganization Approach for Disk Energy Conservation and Disk Performance Enhancement

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Predictive data grouping

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