# EMaaS: Energy Measurements as a Service for Mobile Applications

arXiv: https://arxiv.org/abs/1902.02605 · HTML: https://mlsanity.com/paper/1902.02605/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [Data-Centric Green AI: An Exploratory Empirical Study](https://mlsanity.com/paper/2204.02766/) (2022)
- [Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?](https://mlsanity.com/paper/2109.15284/) (2021)
- [The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems](https://mlsanity.com/paper/2407.02914/) (2024)

## Abstract (arXiv)

Measuring energy consumption is a challenging task faced by developers when building mobile apps. This paper presents EMaaS: a system that provides reliable energy measurements for mobile applications, without requiring a complex setup. It combines estimations from an energy model with --- typically more reliable, but also expensive --- hardware-based measurements. On a per scenario basis, it decides whether the energy model is able to provide a reliable estimation of energy consumption. Otherwise, hardware-based measurements are provided. In addition, the system is accessible to the community of mobile software practitioners/researchers in the form of a Software as a Service. With this service, we aim at solving current problems in the field of energy efficiency in mobile software engineering: the complexity of hardware-based power monitor tools, the reliability of energy models, and the continuous need of data to build energy models.