Conference Paper

Online Prediction of Battery Lifetime for Embedded and Mobile Devices.

Conference: Power-Aware Computer Systems, Third International Workshop, PACS 2003, SanDiego, CA, USA, December 1, 2003, Revised Papers
Source: DBLP
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    • ", and an application-level tool [28]. The lifetime prediction techniques [29], [30] enable OS to estimate the residual lifetime of a battery based on the energy consumption rate of running tasks and the available energy in the battery. "
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    ABSTRACT: We propose a novel scheduling scheme that determines the instant operation modes of multiple tasks. The tasks have probabilistic execution times and are executed on discrete operation modes providing different utilities with different energy consumptions. We first design an optimal offline scheduling scheme that stochastically maximizes the cumulative utility of the tasks under energy constraints, at the cost of heavy computational overhead. Next, the optimal offline scheme is modified to an approximate online scheduling scheme. The online scheme has little runtime overhead and yields almost the maximum utility, with an energy budget that is given at runtime. The difference between the maximum utility and the output utility of the online scheme is bounded by a controllable input value. Extensive evaluation shows that the output utility of the online scheme approaches the maximum utility in most cases, and is much higher than that of existing methods by up to 50% of the largest utility difference among available operation modes.
    Full-text · Article · Nov 2009 · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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    • "These devices commonly implement energy-efficient processors such as those from the StrongARM [10] and XScale [11] family of processors. Our prior work employed internal battery monitors to measure and characterize power and energy consumption of resource constrained devices [18] [26] [25]. Unfortunately, all extant internal battery monitors or mobile devices are coarse grained, not program specific, and inaccurate. "
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    ABSTRACT: Understanding the full-system power and energy behavior of real, resource-constrained, battery-powered devices is vital to accurately characterize, model, and develop effective techniques for extending battery life. Unfortunately, extant approaches to measuring and characterizing power and energy consumption focus on high-end processors, do not consider the complete device, employ inaccu-rate (program-only) simulation, rely on inaccurate, course-grained battery level data from the device, or employ expensive power mea-surement tools that are difficult to share across research groups and students. In this paper, we present RPM, a remote performance monitor-ing system, that enables fine grained characterization of embedded computers. RPM consists of a tightly connected set of components which (1) control lab equipment for power measurements and anal-ysis, (2) configure target system characteristics at run-time (such as CPU and memory bus speed), (3) collect target system data using on-board hardware performance monitors (HPMs) and (4) provide a remote access interface. Users of RPM can submit and config-ure experiments that execute programs on the RPM target device (currently a Stargate sensor platform that is very similar to an HP iPAQ) to collect very accurate power, energy, and CPU per-formance data with high resolution. We use RPM to investigate whether CPU-based performance data in the form of HPM metrics or program phase behavior cor-relates well with full-system energy or power behavior. Prior work shows that both accurately estimate processor power consumption for high-end CPUs. In resource-constrained devices, such as the one we study, however, the processor consumes a much smaller portion of the total power in the system than for high-end proces-sors. Our experimentation with RPM for the Stargate and set of embedded system benchmarks, show that CPU-based metrics do not correlate well with full-system energy and power consumption. Moreover, we find that full-system energy and power varies signifi-cantly with the type of memory device and file system.
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    ABSTRACT: In this paper, we propose a system for context- aware battery management that warns the user when it detects that the phone battery can run out before the next charging opportunity is encountered. At the heart of this system, are algorithms that predict: (1) when the next charging opportunity will be available, (2) how much call-time will be required by the user in the interim, and (3) how long the battery will last if the current set of applications continue to execute. We propose algorithms that process user's location traces and call-logs for making some of these predictions. We also propose a technique to predict battery consumption of applications. We present the design of the system and demonstrate its feasibility by experimentally showing that each of the prediction algorithms can perform with fairly high accuracy.
    Full-text · Conference Paper · Mar 2008
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