Crowdsourcing mobile user's network performance has become an effective way of understanding and improving mobile network performance and user quality-of-experience. However, the current measurement method is still based on the landline measurement paradigm in which a measurement app measures the path to fixed (measurement or web) servers. In this work, we introduce a new paradigm of measuring per-app mobile network performance. We design and implement MopEye, an Android app to measure network round-trip delay for each app whenever there is app traffic. This opportunistic measurement can be conducted automatically without users intervention. Therefore, it can facilitate a large-scale and long-term crowdsourcing of mobile network performance. In the course of implementing MopEye, we have overcome a suite of challenges to make the continuous latency monitoring lightweight and accurate. We have deployed MopEye to Google Play for an IRB-approved crowdsourcing study in a period of ten months, which obtains over five million measurements from 6,266 Android apps on 2,351 smartphones. The analysis reveals a number of new findings on the per-app network performance and mobile DNS performance.
@article{arxiv.1703.07551,
title = {MopEye: Opportunistic Monitoring of Per-app Mobile Network Performance},
author = {Daoyuan Wu and Rocky K. C. Chang and Weichao Li and Eric K. T. Cheng and Debin Gao},
journal= {arXiv preprint arXiv:1703.07551},
year = {2017}
}
Comments
This paper has been accepted by 2017 USENIX Annual Technical Conference, ATC'17 (https://www.usenix.org/conference/atc17/technical-sessions/presentation/wu)