Do You Have the Energy for This Meeting? An Empirical Study on the Energy Consumption of the Google Meet and Zoom Android apps

This repository is a companion page of the paper "Do You Have the Energy for This Meeting? An Empirical Study on the Energy Consumption of Android Videoconferencing Apps", submitted for review at the 9th IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft), 2022.

The study aims to investigate the energy consumption of two popular Android videoconferencing apps, namely Zoom and Google Meet. In addition we investigate the impact on energy consumption of different features and settings, namely number of participants, and camera, michropone, and virtual background use.

This repository is devided into two main parts: the actual experiment (android-runner) and the analysis of the measured data (data-analysis).

How to cite us

The scientific article describing design, execution, and main results of this study is available here. If this study is helping your research, consider to cite it is as follows, thanks!

@inproceedings{wattenbach2022do,
  title={{Do You Have the Energy for This Meeting? An Empirical Study on the Energy Consumption of the Google Meet and Zoom Android apps}},
  author={Wattenbach, Leonhard and Basel, Aslan and Maria Fiore, Matteo and Ding, Henley and Verdecchia, Roberto and Malavolta, Ivano},
  booktitle={Proceedings of International Conference on Mobile Software Engineering and Systems (MOBILESoft 2022).}
  year={2022},
  publisher={IEEE/ACM}
}

android-runner

Python framework for automatically executing measurement-based experiments on native and web apps running on Android devices

Data analysis

The data analysis can again be split up into two parts: the preprocessing and the actual analysis.

Preprocessing

In order to be able to evaluate the measured data, it is reasonable to extract the relevant information that Batterystats has written into multiple files spread over multiple folders. We used a Python script to do this. Before running, a few parameters have to be adapted in this script:

• phone: The ID of the used phone as specified in Monkeyrunner (e.g. motog5)
• raw_data_path: The path to the folder containing the raw data generated by the experiment.

There are two preprocessing scripts available: one for the preliminary experiment (preprocessing-pre.py) and one for the main experiment (preprocessing-main.py). They slightly differ, since the folder structure and the interesting attributes also differ. Both are generating a CSV file called results-pre.csv or results-main.csv respectively in the raw data’s parent folder. This CSV file contains the following attributes:

Attribute	Description	Values
app	The examined videoconferencing app	meet or zoom
number_of_participants (only in main)	Number of participants in the video conference	2 or 5
camera (only in main)	Activation state of the camera	True or False
microphone (only in main)	Activation state of the microphone	True or False
background (only in main)	Activation state of the virtual background	True or False
duration_in_minutes (only in preliminary)	Duration of the video conference in minutes	Numerical value
joules	Consumed energy in Joules	Numerical value

Folder structure

Inside the specified raw_data_path folder must be the following folder structure for the preprocessing script to find the measurements and used treatments.

Preliminary experiment:

• Folders with arbitrary names, all containing:
  • Config file: config.json
  • Folder: data
    • Folder: Name of the phone (as specified in the variable phone)
      • Folder: Either com-google-android-apps-meetings or us-zoom-videomeetings
        • Folder: batterystats
          • CSV files starting with Joule containing the energy measurements

Main experiment:

• Folders with arbitrary names ending with either -2p (for two participants) or -5p (for five participants), all containing:
  • Folders with arbitrary names ending with a hypen (-) followed by a subset of the letters c (iff the camera was active), b (iff the virtual background was active) and m (iff the microphone was active) – for example: somefoldername-cbm
    • Config file: config.json
    • Folder: data
      • Folder: Name of the phone (as specified in the variable phone)
        • Folder: Either com-google-android-apps-meetings or us-zoom-videomeetings
          • Folder: batterystats
            • CSV files starting with Joule containing the energy measurements

Acutal analysis

To analyse and visualize the preprocessed data, we used R. With R it is very simple to read CSV files and perform analyses on the read data. For creating plots, we used ggplot2.