In this paper, we demonstrate how component-based middleware can reduce the energy usage of closed-source applications. We rst describe how the Puppeteer system exploits well-dened interfaces exported by applications to modify their behavior. We then present a detailed study of the energy usage of Microsoft's PowerPoint application and show that adaptive policies can reduce energy expenditure by 49% in some instances. In addition, we use the results of the study to provide general advice to developers of applications and middleware that will enable them to create more energy-ecient software. 1

Energy conservation without performance degradation is an important goal for batteryoperated computers, such as laptops and hand-held assistants. In this paper 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% to 89% with a degradation in performance of at most 8%.

Power dissipation in mobile computers is crucial and researchers have expended significant effort to improve power management for the hard drive, which accounts for a large percentage of the power consumed by the system. A new class of secondary storage devices based on microelectromechanical systems (MEMS) promises to consume an order of magnitude less power with 10--20 times shorter latency and 10 times greater storage densities. Though MEMS storage devices promise to provide a more energy-efficient storage solution for mobile computing applications, little research has been conducted on how to manage the power consumption of these devices. In this paper we examine the power model of a MEMSbased storage device and perform a quantitative analysis of the power distribution among different working modes. Based on our analysis, we present three strategies to reduce power consumption: aggressive spin-down, merging of sequential requests, and subsector accesses. We show that immediate spin-down can save 50% of the total energy consumed by the device at the cost of increased response time. Merging of sequential requests can save up to 18% of the servicing energy and reduce response time by about 20%. Transferring less data for small requests such as those for metadata can save 40% of the servicing energy. Finally, we show that by applying all three power management strategies simultaneously the total power consumption of MEMS-based storage devices can be reduced by about 54% with no impact on I/O performance. This research is supported by the National Science Foundation under grant number CCR-073509 and the Institute for Scientific Computation Research at Lawrence Livermore National Laboratory under grant number SC-20010378.

Advances in hardware imaging technology and user demand for convenient mobile electronic image capture are fueling the development of inexpensive image capture devices that can acquire images rivaling the image quality of photographic film. Improvements in the hardware imaging technology have to be matched with intelligent image storage mechanisms that are aware of local storage and battery constraints. In this paper, we explore using a dynamic, informed image transcoding technique to manage the consumed battery and storage resources in digital cameras. Such application aware technologies are fundamental for the mass consumer acceptance of these newer digital technologies. We show that this technique can allow the camera to store an order of magnitude more images. For a moderate number of images (e.g. 40), transcoding techniques can also maintain high quality images. The availability of fast wireless networks can allow the camera to capture 58 high quality images (51 uploaded) before ...

Energy conservation without performance degradation is an important goal for battery-operated computers, such as laptops and handheld assistants. In this paper we determine the potential benefits of application-supporteddevice managementfor optimizing energy and performance. In particular, we consider application transformations that increase device idle times and inform the operating systemabout the length of eachupcoming period of idleness. We usemodeling and experimentation to assess the potential energy and performance benefits of this type of application support for a laptop disk. Our main modeling results show that these benefits are significant. Our experimental results demonstrate that unless applications are transformed, they cannot accrue any of the predicted benefits. Overall, we find that the transformations can reduce disk energy consumption by as much as 89% with only a small degradation in performance.

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.

Data compression has been claimed to be an attractive solution to save energy consumption in high-end servers and data centers. However, there has not been a study to explore this. In this paper, we present a comprehensive evaluation of energy consumption for various file compression techniques implemented in software. We apply various compression tools available on Linux to a variety of data files, and we try them on server class and workstation class systems. We compare their energy and performance results against raw reads and writes. Our results reveal that software based data compression cannot be considered as a universal solution to reduce energy consumption. Various factors like the type of the data file, the compression tool being used, the read-to-write ratio of the workload, and the hardware configuration of the system impact the efficacy of this technique. In some cases, however, we found compression to save substantial energy and improve performance.

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The recent proliferation of streaming multimedia on a variety of mobile devices has severely tested their battery lifetime. The long running nature of typical streaming applications results in significant energy consumption by the wireless network interface card (WNIC) in these mobile devices. In this paper we explore linear prediction-based client-side strategies that reduce the WNIC energy consumption to receive multimedia streams by judiciously transitioning the WNIC to a lower power consuming sleep state during the no-data intervals in the multimedia stream, without explicit support from the multimedia servers themselves. Experimental results on popular streaming formats such as Microsoft Media, Real and Apple QuickTime show that a linear prediction-based strategy performs better than history-based strategies that use simple temporal averaging.

The web is emerging as the primary data dissemination and e-commerce mechanism. Users access the Internet from a wide variety of devices with different resource constraints in terms of their processing, storage, network and display capabilities. Users are not only accessing the Internet from traditional desktops, but they are also using mobile devices such as palmtops and laptops as well as newer consumer devices such as webtops and navigation systems. Popular web ...