Implantação de uma rede multi serviço

Characterizing and Modeling Internet Traffic Dynamics of Cellular Devices
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Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA ‡ AT&T Labs – Research, Florham Park, NJ, USA

M. Zubair Shafiq†

Lusheng Ji‡

Alex X. Liu†

Jia Wang‡

{shafiqmu,alexliu}@cse.msu.edu, {lji,jiawang}@research.att.com

ABSTRACT
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.

surge as the result of dramatic growth in the popularity of smart phones strongly suggests that the trend of cellular data growth will continue to accelerate as technology and application availabilities further improve [1]. To cope with the explosive cellular data volume growth and best serve their customers, cellular network operators need to design and manage cellular core network architectures accordingly. To achieve this, the first step is to understand the spatial and temporal patterns of Internet traffic carried by cellular networks. Understanding the spatial and temporal patterns of traffic can