Understanding and Predicting Data Hotspots in Cellular Networks

The unprecedented growth in mobile data usage is posing significant challenges to cellular operators. One key challenge is how to provide quality of service to subscribers when their residing cell is experiencing a significant amount of traffic, i.e. becoming a traffic hotspot. In this paper, we perform an empirical study on data hotspots in today’s cellular networks using a 9-week cellular dataset with 734K+ users and 5327 cell sites. Our analysis examines in details static and dynamic characteristics, predictability, and causes of data hotspots, and their correlation with call hotspots. We show that using standard machine learning methods, future hotspots can be accurately predicted from past observations. We believe the understanding of these key issues will lead to more efficient and responsive resource management and thus better QoS provision in cellular networks. To the best of our knowledge, our work is the first to empirically characterize traffic hotspots in today’s cellular networks. The final publication is available at http://link.springer.com/article/10.1007/s11036-015-0648-6 A. Nika · A. Ismail · B. Y. Zhao · H. Zheng Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA 93106-5110, USA E-mail: [email protected] A. Ismail E-mail: [email protected] B. Y. Zhao E-mail: [email protected] H. Zheng E-mail: [email protected] S. Gaito · G. P. Rossi Università degli Studi di Milano, Milan, Italy E-mail: [email protected] G. P. Rossi E-mail: [email protected]