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MopEye: Opportunistic Monitoring of Per-app Mobile Network Performance
Speaker (s): 
WU Daoyuan
PhD Candidate
School of Information Systems
Singapore Management University |
Date:
Time:
Venue:
| | June 30, 2017, Friday
10:00am - 11:00am
Meeting Room 4.4, Level 4
School of Information Systems
Singapore Management University
80 Stamford Road
Singapore 178902
We look forward to seeing you at this research seminar. 
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About the Talk 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. [MopEye's homepage is available at https://mopeye.github.io/] This is a pre-conference talk for 2017 USENIX Annual Technical Conference (ATC'17) .
About the Speaker Daoyuan WU is a PhD candidate in Cybersecurity at School of Information Systems, Singapore Management University. He is advised by Associate Professor Debin Gao and Professor Robert H. Deng. His research focuses on mobile security, covering topics such as vulnerability discovery, program analysis, network security, and crowdsourcing measurement. |
