Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2019
Abstract
Monitoring mobile network performance is critical for optimizing the QoE of mobile apps. Until now, few studies have considered the actual network performance that mobile apps experience in a per-app or per-server granularity. In this paper, we analyze a two-year-long dataset collected by a crowdsourcing per-app measurement tool to gain new insights into mobile network behavior and application performance. We observe that only a small portion of WiFi networks can work in high-speed mode, and more than one-third of the observed ISPs still have not deployed 4G networks. For cellular networks, the DNS settings on smartphones can have a significant impact on mobile app network performance. Moreover, we notice that instant messaging (IM) and voice over IP (VoIP) services nowadays are not as performant as Web services, because the traffic using XMPP experiences longer latencies than HTTPS. We propose an automatic performance degradation detection and localization method for finding possible network problems in our huge, imbalanced and sparse dataset. Our evaluation and case studies show that our method is effective and the running time is acceptable.
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
IWQoS '19: Proceedings of the International Symposium on Quality of Service, Phoenix, June 24-25
First Page
1
Last Page
10
ISBN
9781450367783
Identifier
10.1145/3326285.3329039
Publisher
ACM
City or Country
New York
Citation
ZHANG, Shiwei; LI, Weichao; WU, Daoyuan; JIN, Bo; CHANG, Rocky K. C.; GAO, Debin; WANG, Yi; and MOK, Ricky K. P..
An empirical study of mobile network behavior and application performance in the wild. (2019). IWQoS '19: Proceedings of the International Symposium on Quality of Service, Phoenix, June 24-25. 1-10.
Available at: https://ink.library.smu.edu.sg/sis_research/4721
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3326285.3329039