Advertisement

A Panoramic View of 3G Data/Control-Plane Traffic: Mobile Device Perspective

  • Xiuqiang He
  • Patrick P. C. Lee
  • Lujia Pan
  • Cheng He
  • John C. S. Lui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7289)

Abstract

Users can access the Internet via 3G/4G cellular data networks using various types of user devices (e.g., smartphones, tablets, datacards). We conduct a detailed measurement study on the impact of different device types on the data/control-plane performance of a commercial, city-wide 3G cellular data network in China. We present a methodology that correlates different data/control-plane datasets collected at different points in the network core, and identify more than 60K devices of different types per day on average. For the devices we identify, we investigate how their commonly used Internet applications and internal heartbeat mechanisms lead to distinct data/control-plane behaviors. For example, we observe that datacard devices contribute a large volume of IP traffic in the data plane, while smartphones introduce significant resource overhead in the signaling control plane. Our measurement study provides insights for network operators to strategize pricing and resource allocation for the data/control planes of their cellular data networks with regard to the market penetrations of various device types.

Keywords

mobile device traffic data/control-plane 3G networks measurement 

References

  1. 1.
    Cisco. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2010–2015 (February 2011)Google Scholar
  2. 2.
    D’Alconzo, A., Coluccia, A., Ricciato, F., Romirer-Maierhofer, P.: A Distribution-Based Approach to Anomaly Detection for 3G Mobile Networks. In: IEEE Globecom (2009)Google Scholar
  3. 3.
  4. 4.
    Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)CrossRefGoogle Scholar
  5. 5.
    Dreger, H., Feldmann, A., Mai, M., Paxson, V., Sommer, R.: Dynamic Application-Layer Protocol Analysis for Network Intrusion Detection. In: Proc. of USENIX Security Symp. (2006)Google Scholar
  6. 6.
    Falaki, H., Lymberopoulos, D., Mahajan, R., Kandula, S., Estrin, D.: A First Look at Traffic on Smartphones. In: Proc. of ACM IMC (November 2010)Google Scholar
  7. 7.
    Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R., Estrin, D.: Diversity in Smartphone Usage. In: Proc. of ACM MobiSys (June 2010)Google Scholar
  8. 8.
    Gember, A., Anand, A., Akella, A.: A Comparative Study of Handheld and Non-handheld Traffic in Campus Wi-Fi Networks. In: Spring, N., Riley, G.F. (eds.) PAM 2011. LNCS, vol. 6579, pp. 173–183. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Huang, J., Xu, Q., Tiwana, B., Mao, Z.M., Zhang, M., Bahl, P.: Anatomizing Application Performance Differences on Smartphones. In: Proc. of ACM MobiSys (2010)Google Scholar
  11. 11.
    Huawei. MSCG hierarchical DPI solution (2011), http://www.huawei.com/products/datacomm/catalog.do?id=1219
  12. 12.
    IDC. Worldwide Smartphone Market Expected to Grow 55% in 2011 and Approach Shipments of One Billion in 2015, According to IDC (June 2011), http://www.idc.com/getdoc.jsp?containerId=prUS22871611
  13. 13.
  14. 14.
    Kilpi, J., Lassila, P.E.: Micro- and Macroscopic Analysis of RTT Variability in GPRS and UMTS Networks. In: Boavida, F., Plagemann, T., Stiller, B., Westphal, C., Monteiro, E. (eds.) NETWORKING 2006. LNCS, vol. 3976, pp. 1176–1181. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Kingsley-Hughes, A.: iOS 4.2 Supports Network Controlled Fast Dormancy (December 2010), http://www.zdnet.com/blog/hardware/ios-42-supports-network-controlled-fast-dormancy/10586
  16. 16.
    Lee, P.P.C., Bu, T., Woo, T.: On the detection of signaling DoS attacks on 3G/WiMax wireless networks. Computer Networks 53(15), 2601–2616 (2009)MATHCrossRefGoogle Scholar
  17. 17.
    Maier, G., Schneider, F., Feldmann, A.: A First Look at Mobile Hand-Held Device Traffic. In: Krishnamurthy, A., Plattner, B. (eds.) PAM 2010. LNCS, vol. 6032, pp. 161–170. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Paul, U., Subramanian, A.P., Buddhikot, M.M., Das, S.R.: Understanding Traffic Dynamics in Cellular Data Networks. In: Proc. of IEEE INFOCOM (2011)Google Scholar
  19. 19.
    Qian, F., Wang, Z., Gerber, A., Mao, Z.M., Sen, S., Spatscheck, O.: Characterizing Radio Resource Allocation for 3G Networks. In: Proc. of ACM IMC (2010)Google Scholar
  20. 20.
  21. 21.
    Ricciato, F., Vacirca, F., Karner, M.: Bottleneck detection in UMTS via TCP passive monitoring: a real case. In: Proc. of ACM CoNEXT (October 2005)Google Scholar
  22. 22.
    Ridoux, J., Nucci, A., Veitch, D.: Seeing the difference in IP traffic: wireless versus wireline. In: Proc. of IEEE INFOCOM (2006)Google Scholar
  23. 23.
    Xu, Q., Erman, J., Gerber, A., Mao, Z.M., Pang, J., Venkataraman, S.: Identifying Diverse Usage Behaviors of Smartphone Apps. In: Proc. of ACM IMC (November 2011)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Xiuqiang He
    • 1
  • Patrick P. C. Lee
    • 2
  • Lujia Pan
    • 1
  • Cheng He
    • 1
  • John C. S. Lui
    • 2
  1. 1.Noah’s Ark Lab, Huawei ResearchChina
  2. 2.Dept of Computer Science & EngineeringThe Chinese University of Hong KongHong Kong

Personalised recommendations