Article

Run-time modeling and estimation of operating system power consumption

Authors:

Lizy Kurian JohnAuthors Info & Claims

SIGMETRICS '03: Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems

Pages 160 - 171

https://doi.org/10.1145/781027.781048

Published: 10 June 2003 Publication History

Abstract

The increasing constraints on power consumption in many computing systems point to the need for power modeling and estimation for all components of a system. The Operating System (OS) constitutes a major software component and dissipates a significant portion of total power in many modern application executions. Therefore, modeling OS power is imperative for accurate software power evaluation, as well as power management (e.g. dynamic thermal control and equal energy scheduling) in the light of OS-intensive workloads. This paper characterizes the power behavior of a commercial OS across a wide spectrum of applications to understand OS energy profiles and then proposes various models to cost-effectively estimate its run-time energy dissipation. The proposed models rely on a few simple parameters and have various degrees of complexity and accuracy. Experiments show that compared with cycle-accurate full-system simulation, the model can predict cumulative OS energy to within 1% accuracy for a set of benchmark programs evaluated on a high-end superscalar microprocessor. When applied to track run-time OS energy profiles, the proposed routine level OS power model offers superior accuracy than a simpler, flat OS power model, yielding per-routine estimation error of less than 6%. The most striking observation is the strong correlation between power consumption and the instructions per cycle (IPC) during OS routine executions. Since tools and methodology to measure IPC exist on modern microprocessors, the proposed models can estimate OS power for run-time dynamic thermal and energy management.

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Cited By

Raskind JBabakol TMahmoud KLiu Y(2024)VESTA: Power Modeling with Language Runtime EventsProceedings of the ACM on Programming Languages10.1145/36564028:PLDI(621-646)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3656402
Wedyan FMorrison RAbuomar O(2023)Integration and Unit Testing of Software Energy Consumption2023 Tenth International Conference on Software Defined Systems (SDS)10.1109/SDS59856.2023.10329262(60-64)Online publication date: 23-Oct-2023
https://doi.org/10.1109/SDS59856.2023.10329262
Kim TShin Y(2022)ThermalBleed: A Practical Thermal Side-Channel AttackIEEE Access10.1109/ACCESS.2022.315659610(25718-25731)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3156596
Show More Cited By

Index Terms

Run-time modeling and estimation of operating system power consumption
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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cover image ACM Conferences

SIGMETRICS '03: Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems

June 2003

338 pages

ISBN:1581136641

DOI:10.1145/781027

General Chairs:
Bill Cheng
TeleGIF
,
Satish Tripathi
University of California at Riverside
,
Program Chairs:
Jennifer Rexford
AT&T Labs -- Research, Florham Park, NJ
,
William H. Sanders
University of Illinois at Urbana-Champaign

ACM SIGMETRICS Performance Evaluation Review Volume 31, Issue 1
June 2003
325 pages
ISSN:0163-5999
DOI:10.1145/885651
Issue’s Table of Contents

Copyright © 2003 ACM.

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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Published: 10 June 2003

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SIGMETRICS03: International Conference on Measurement and Modeling of Computer Systems

June 11 - 14, 2003

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SIGMETRICS '03 Paper Acceptance Rate 26 of 222 submissions, 12%;

Overall Acceptance Rate 459 of 2,691 submissions, 17%

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Cited By

Raskind JBabakol TMahmoud KLiu Y(2024)VESTA: Power Modeling with Language Runtime EventsProceedings of the ACM on Programming Languages10.1145/36564028:PLDI(621-646)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3656402
Wedyan FMorrison RAbuomar O(2023)Integration and Unit Testing of Software Energy Consumption2023 Tenth International Conference on Software Defined Systems (SDS)10.1109/SDS59856.2023.10329262(60-64)Online publication date: 23-Oct-2023
https://doi.org/10.1109/SDS59856.2023.10329262
Kim TShin Y(2022)ThermalBleed: A Practical Thermal Side-Channel AttackIEEE Access10.1109/ACCESS.2022.315659610(25718-25731)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3156596
Kruglov ASucci GDlamini GKruglov ASucci G(2022)System Energy Consumption MeasurementDeveloping Sustainable and Energy-Efficient Software Systems10.1007/978-3-031-11658-2_3(27-38)Online publication date: 27-Jul-2022
https://doi.org/10.1007/978-3-031-11658-2_3
Botev JRodríguez Lera F(2021)Immersive Robotic Telepresence for Remote Educational ScenariosSustainability10.3390/su1309471713:9(4717)Online publication date: 23-Apr-2021
https://doi.org/10.3390/su13094717
Ismail LAbed E(2019)Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and EvaluationIEEE Access10.1109/ACCESS.2019.29568817(175003-175019)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2956881
Soltane MRoose PMakhlouf DOkba K(2019)Predictions and Modeling Energy Consumption for IT Data CenterAdvanced Intelligent Systems for Sustainable Development (AI2SD’2018)10.1007/978-3-030-12065-8_1(1-11)Online publication date: 5-Feb-2019
https://doi.org/10.1007/978-3-030-12065-8_1
Lee SKim H(2018)An energy-efficient low-memory image compression system for multimedia IoT productsEURASIP Journal on Image and Video Processing10.1186/s13640-018-0333-32018:1Online publication date: 17-Sep-2018
https://doi.org/10.1186/s13640-018-0333-3
Hähnel MSmejkal TWolter KKnottenbelt Wvan Hoorn ANambiar MKoziolek H(2018)Modular Energy Modeling using Energy/UtilityCompanion of the 2018 ACM/SPEC International Conference on Performance Engineering10.1145/3185768.3186311(73-78)Online publication date: 2-Apr-2018
https://dl.acm.org/doi/10.1145/3185768.3186311
Ferroni MCorna ADamiani ABrondolin RColmenares JHofmeyr SKubiatowicz JSantambrogio M(2017)Power Consumption Models for Multi-Tenant Server InfrastructuresACM Transactions on Architecture and Code Optimization10.1145/314896514:4(1-22)Online publication date: 14-Nov-2017
https://dl.acm.org/doi/10.1145/3148965
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