AndroWatts: Unpacking the Power Consumption of Mobile Device’s Components

Power efficiency is crucial for mobile devices, where software inefficiencies can rapidly drain battery life and reduce device longevity. To help developers diagnose inefficiency issues and optimize power use, this paper introduces a novel methodology for estimating power consumption at a hardware component level in mobile devices. Unlike existing approaches that rely on coarse-grained battery discharge measurements, our approach consists of modeling per-component power usage as an inverse problem, utilizing linear statistical models and system metrics to estimate the contributions of individual components.In this paper, we model the total power usage using system metrics, we evaluate the accuracy of per-component power estimations, and we investigate the impact of dataset size on the model’s performance. Our empirical evaluation shows that linear models achieve high predictive accuracy, with R-squared values exceeding 0.90 for total power usage, and robust correlations are observed between predicted and ground-truth power consumption for key components such as the CPU and GPU. We also observe that the amount of measures required to build such a model is of the order of magnitude of the hundred rather than the thousand.

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Publication Details

Published
2025-04-27
DOI
https://doi.org/10.1109/mobilesoft66462.2025.00015
Primary Topic
Green IT and Sustainability
Type
article
Field-Weighted Citation Impact
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article

AndroWatts: Unpacking the Power Consumption of Mobile Device’s Components

Clément Quinton, Édouard Guégain, Romain Rouvoy, Rémy Raes et al.
Green IT and Sustainability
article

AndroWatts: Unpacking the Power Consumption of Mobile Device’s Components

Clément Quinton, Édouard Guégain, Romain Rouvoy, Rémy Raes, Noé Chachignot
article en

Abstract

Power efficiency is crucial for mobile devices, where software inefficiencies can rapidly drain battery life and reduce device longevity. To help developers diagnose inefficiency issues and optimize power use, this paper introduces a novel methodology for estimating power consumption at a hardware component level in mobile devices. Unlike existing approaches that rely on coarse-grained battery discharge measurements, our approach consists of modeling per-component power usage as an inverse problem, utilizing linear statistical models and system metrics to estimate the contributions of individual components.In this paper, we model the total power usage using system metrics, we evaluate the accuracy of per-component power estimations, and we investigate the impact of dataset size on the model’s performance. Our empirical evaluation shows that linear models achieve high predictive accuracy, with R-squared values exceeding 0.90 for total power usage, and robust correlations are observed between predicted and ground-truth power consumption for key components such as the CPU and GPU. We also observe that the amount of measures required to build such a model is of the order of magnitude of the hundred rather than the thousand.

Institut national de recherche en sciences et technologies du numérique (FR)
Affordable and clean energy
Openalex Percentile: Top 99%
Green IT and Sustainability
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