Power, energy, and thermal concerns have constrained embedded systems designs. Computing capability and storage density have increased dramatically, enabling the emergence of handheld devices from special to general purpose computing. In many mobile systems, the disk is among the top energy consumers. Many previous optimizations for disk energy have assumed uniprogramming environments. However, many optimizations degrade in multiprogramming because programs are unaware of other programs (execution context). We introduce a framework to make programs aware of and adapt to their runtime execution context. We evaluated real workloads by collecting user activity traces and characterizing the execution contexts. The study confirms that many users run a limited number of programs concurrently. We applied execution context optimizations to eight programs and tested ten combinations. The programs ran concurrently while the disk’s power was measured. Our measurement infrastructure allows interactive sessions to be scripted, recorded, and replayed to compare the optimizations’ effects against the baseline. Our experiments covered two write cache policies. For write-through, energy savings was in the range 3–63 % with an average of 21%. For writeback, energy savings was in the range-33–61 % with an average of 8%. In all cases, our optimizations incurred less than 1 % performance penalty.

Previous work has shown that intra-program optimizations, i.e., optimizations performed on individual programs in isolation, can be very effective in reducing disk energy in streaming applications. This paper investigates the potential additional benefits of inter-program optimizations where sets of programs are optimized together. Experimental results on different subsets of three streaming applications show that 7–49 % additional energy savings (27.3 % on average) can be obtained with negligible performance penalties using two novel inter-program optimizations, namely execution context sensitive buffer size selection and inverse barrier synchronization. These figures were obtained via physical measurements on two laptop disks. Categories and Subject Descriptors: D.3.3 [Frameworks]:

Energy conservation without performance degradation is an important goal for battery-operated computers, such as laptops and hand-held assistants. We study application-supported device management for optimizing energy and performance. In particular, we consider application transformations that increase device idle times and inform the operating system about the length of each upcoming, period of idleness. We use modeling and experimentation to assess the potential energy and performance benefits of this type of application support for a laptop disk. Furthermore, we propose and evaluate a compiler framework for performing the transformations automatically. Our main modeling results show that the transformations are potentially beneficial. However, our experimental results with six real laptop applications demonstrate that, unless applications are transformed, they cannot accrue any of the predicted benefits. In addition, they show that our compiler can produce almost the same performance and energy results as hand-modifying applications. Overall, we find that the transformations can reduce disk energy consumption from 55 percent to 89 percent with degradation in performance of at most 8 percent.