MopEye: Opportunistic Monitoring of Per-app Mobile Network Performance


Daoyuan Wu, Singapore Management University; Rocky K. C. Chang, Weichao Li, and Eric K. T. Cheng, The Hong Kong Polytechnic University; Debin Gao, Singapore Management University


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 user 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.

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@inproceedings {203263,
author = {Daoyuan Wu and Rocky K. C. Chang and Weichao Li and Eric K. T. Cheng and Debin Gao},
title = {MopEye: Opportunistic Monitoring of Per-app Mobile Network Performance},
booktitle = {2017 {USENIX} Annual Technical Conference ({USENIX} {ATC} 17)},
year = {2017},
isbn = {978-1-931971-38-6},
address = {Santa Clara, CA},
pages = {445--457},
url = {},
publisher = {{USENIX} Association},