Elements of information theory
Elements of information theory
Self-configuring network traffic generation
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Characterization of CDMA2000 Cellular Data Network Traffic
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Realistic and responsive network traffic generation
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
A study of the short message service of a nationwide cellular network
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Youtube traffic characterization: a view from the edge
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Proceedings of the 18th international conference on World wide web
Modeling channel popularity dynamics in a large IPTV system
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Measuring serendipity: connecting people, locations and interests in a mobile 3G network
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Proceedings of the 8th international conference on Mobile systems, applications, and services
AccuLoc: practical localization of performance measurements in 3G networks
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Leveraging diversity to optimize performance in mobile networks
Proceedings of the 2013 workshop on Student workhop
Traffic pattern based virtual network embedding
Proceedings of the 2013 workshop on Student workhop
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Understanding Internet traffic dynamics in large cellular networks is important for network design, troubleshooting, performance evaluation, and optimization. In this paper, we present the results from our study, which is based upon a week-long aggregated flow level mobile device traffic data collected from a major cellular operator's core network. In this study, we measure and characterize the spatial and temporal dynamics of mobile Internet traffic. We distinguish our study from other related work by conducting the measurement at a larger scale and exploring mobile data traffic patterns along two new dimensions -- device types and applications that generate such traffic patterns. Based on the findings of our measurement analysis, we propose a Zipf-like model to capture the volume distribution of application traffic and a Markov model to capture the volume dynamics of aggregate Internet traffic. We further customize our models for different device types using an unsupervised clustering algorithm to improve prediction accuracy.