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. Copyright 2005 ACM.

Power conservation in systems is critical for mobile, sensor network, and other power-constrained environments. While disk spin-down policies can contribute greatly to reducing the power consumption of the storage subsystem, the reshaping of the access workload can actively increase such energy savings. Traditionally reshaping of the access workload is a result of caches passively modifying the workload with the aim of increasing hit ratios and reducing access latency. In contrast, we present the a shifting predictive policy that actively reshapes the workload with the primary goal of conserving disk energy consumption. By reshaping the disk workload to explicitly lengthen idle periods, the disk can remain spun-down longer, saving more energy. We show that our approach can save up to 75% of disk energy compared to the common fixed-timeout spin-down policies. Our shifting algorithm dynamically shifts to the most energy efficient cache prefetching policy based on the current workload. This best shifting prefetching policy is shown to use 15% to 35% less energy than traditional disk spin-down strategies and 5% to 10% less energy than the use of a fixed (non-shifting) prefetching policy. © 2006 IEEE.