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A programming environment with runtime energy characterization for energy-aware applications

Published: 27 August 2007 Publication History

Abstract

System-level power management has been studied extensively. For further energy reduction, the collaboration from user applications becomes critical. This paper presents a programming environment to ease the construction of energy-aware applications. We observe that energy-aware programs may identify different ways (called options) to achieve the desired functionalities and choose the most energy-efficient option at runtime. Our framework provides a programming interface to obtain the estimated energy consumption for choosing a particular option. The energy is estimated based on runtime energy characterization that records a set of runtime conditions correlated with the energy consumption of the options. We provide the procedure and general guidelines for using the environment to construct energy-aware programs. The prototype demonstrates that (a) energy-aware applications can be programmed easily with our interface, (b) accurate estimates are achieved by integrating multiple runtime conditions, and (c) the framework can make multiple devices collaborate for significant energy savings (15% to 41%) with negligible time and energy overhead (<0.35%).

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  • (2019)Provisioning of energy consumption information for mobile adsPervasive and Mobile Computing10.1016/j.pmcj.2019.01.00253(49-61)Online publication date: Feb-2019
  • (2016)Improving the Eco-Efficiency of High Performance Computing Clusters Using EEClusterEnergies10.3390/en90301979:3(197)Online publication date: 14-Mar-2016
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    cover image ACM Conferences
    ISLPED '07: Proceedings of the 2007 international symposium on Low power electronics and design
    August 2007
    432 pages
    ISBN:9781595937094
    DOI:10.1145/1283780
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 27 August 2007

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    Author Tags

    1. energy characterization
    2. energy-aware application
    3. programming environment

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    View all
    • (2019)Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence TechniquesEnergies10.3390/en1211212912:11(2129)Online publication date: 3-Jun-2019
    • (2019)Provisioning of energy consumption information for mobile adsPervasive and Mobile Computing10.1016/j.pmcj.2019.01.00253(49-61)Online publication date: Feb-2019
    • (2016)Improving the Eco-Efficiency of High Performance Computing Clusters Using EEClusterEnergies10.3390/en90301979:3(197)Online publication date: 14-Mar-2016
    • (2016)Energy-Aware Programming Model for Distributed Infrastructures2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)10.1109/PDP.2016.39(413-417)Online publication date: Feb-2016
    • (2016)Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clustersEngineering Applications of Artificial Intelligence10.1016/j.engappai.2015.10.00348:C(95-105)Online publication date: 1-Feb-2016
    • (2015)A software tool to efficiently manage the energy consumption of HPC clusters2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2015.7338079(1-8)Online publication date: Aug-2015
    • (2015)Energy-Aware Profiling for Cloud Computing EnvironmentsElectronic Notes in Theoretical Computer Science (ENTCS)10.1016/j.entcs.2015.10.021318:C(91-108)Online publication date: 25-Nov-2015
    • (2015)Energy-efficient allocation of computing node slots in HPC clusters through parameter learning and hybrid genetic fuzzy system modelingThe Journal of Supercomputing10.1007/s11227-014-1320-971:3(1163-1174)Online publication date: 1-Mar-2015
    • (2014)A Cool Scheduler for Multi-Core Systems Exploiting Program PhasesIEEE Transactions on Computers10.1109/TC.2012.28363:5(1061-1073)Online publication date: 1-May-2014
    • (2012)Estimating Android applications' CPU energy usage via bytecode profilingProceedings of the First International Workshop on Green and Sustainable Software10.5555/2663779.2663780(1-7)Online publication date: 3-Jun-2012
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