Expecting the unexpected: adaptation for predictive energy conservation

The use of access predictors to improve storage device performance has been investigated for both improving access times, as well as a means of reducing energy consumed by the disk. Such predictors also offer us an opportunity to demonstrate the benefits of an adaptive approach to handling unexpected workloads, whether they are the result of natural variation or deliberate attempts to generate a problematic workload. Such workloads can pose a threat to system availability if they result in the excessive consumption of potentially limited resources such as energy. We propose that actively reshaping a disk access workload, using a dynamically self-adjusting access predictor, allows for consistently good performance in the face of varying workloads. Specifically, we describe how our Best Shifting prefetching policy, by adapting to the needs of the currently observed workload, can use 15% to 35% less energy than traditional disk spin-down strategies and 5% to 10% less energy than the use of a fixed prefetching policy.

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