A Scaling Analysis of UMTS Traffic
This paper reports on the results of a scaling analysis of traffic traces captured at several Gn Interfaces of an opera-tional 3G network. Using the Abry Veitch test and Variance Time Plots we find that the analyzed traffic is characterized by a non self-similar process for small time scales and strong self-similarity for larger time scales. The reasons for the time scale dependency of traffic self similarity are analysed in detail. We find that for smaller time scales the packet arrival times are independent explaining the weak correlation of the data. For larger timescales the arrival of TCP connections in the UMTS network can be considered independent. This property is intuitive because the only link where TCP connections could influence themselves is the channel originating at the mobile terminal. As a further finding we observe that the cut off point between weak and strong correlation is around the mean RTT of TCP flows.
Additionally, we propose a model which allows generating traffic similar to the measured UMTS traffic. The model contains only three parameters allowing to influence all observed changes in Log Diagram and Variance Time Plot. Comparing the output of the model with the real traffic traces we find that the model matches reality accurately.
KeywordsMobile Terminal Long Range Dependent Hurst Parameter Large Time Scale Small Time Scale
Unable to display preview. Download preview PDF.
- 3.Garrett, M., Willinger, W.: Analysis, Modeling and Generation of Self-Similar VBR Video Traffic. In: Proceedings SIGCOMM 1994, pp. 269–280 (August 1994)Google Scholar
- 4.Mayor, G., Silvester, J.: Time Scale Analysis of an ATM Queueing System with Long-Range Dependent Traffic. In: INFOCOM 1997 (1997)Google Scholar
- 8.Uhlig, S., Bonaventure, O., Rapier, C.: 3D-LD: a Graphical Wavelet-Based Method for Analyzing Scaling Processes. In: Proc. of the 15th ITC Specialist Seminar (July 2002)Google Scholar
- 12.Stallings, W.: High-Speed Networks: TCP/IP and ATM Design Principles. Prentice-Hall, Englewood Cliffs (1998)Google Scholar
- 13.Rolls, D.A.: Limit theorems and estimation for structural and aggregate teletraffic models, Ph.D. thesis, Queen’s University at Kingston (2003)Google Scholar
- 14.Karagiannis, T., Faloutsos, M.: SELFIS: A Tool For Self-Similarity and Long-Range Dependence Analysis. In: 1st Workshop on Fractals and Self-Similarity in Data Mining: Issues and Approaches (in KDD), Edmonton, Canada, July 23 (2002)Google Scholar
- 16.Roughan, M., Veitch, D.: Measuring long-range dependence under changing traffic conditions. In: Proceedings of INFOCOM 1999, pp. 1513–1521 (1999)Google Scholar
- 18.Stoev, S., Taqqu, M., Park G., Michailidis G., Marron J.S.: LASS: a Tool for The Local Analysis of Self-Similarity. Available as SAMSI Tech. Rep. No 2004-7 (preprint 2007)Google Scholar
- 19.Dang, T.D., Molnar, S.: On The Effects of Non-Stationarity in Long Range Dependent Tests. Tech. Rep. Budapest University of Technology and Economics (1999)Google Scholar
- 20.Molnar, S., Dang, T.D.: Pitfalls in Long Range Dependence Testing and Estimation. In: GLOBECOM (2000)Google Scholar
- 21.METAWIN home page: http://www.ftw.at/ftw/research/projects/ProjekteFolder/N2
- 22.Vacirca, F., Ziegler, T., Hasenleithner, E.: An Algorithm to Detect TCP Suprious Timeouts and its Application to Operational UMTS/GPRS Networks. Computer Networks Journal (2006)Google Scholar
- 25.Barford, P., Bestavro, A., Bradley, A., Crovella, M.E.: Changes in Web Client Access Patterns: Characteristics and Caching Implications. World Wide Web, Special Issue on Characterization and Performance Evaluation 2, 15–28 (1999)Google Scholar
- 26.Janowski, L., Ziegler, T., Hasenleithner, E.: A Scaling Analysis of UMTS Traffic, work report, www.kt.agh.edu.pl/~janowski/AScalingUMTS.pdf