Abstract
This paper presents enhanced approach to profiling traffic in mobile packet services such as HSDPA. Deriving accurate and meaningful profiles of traffic generated by packet services can greatly improve dimensioning of the infrastructure for packet mobile networks. Traffic profiles are derived by clustering of daily aggregates of the traffic volume. In this work we propose a new definition of distance between the clustered vectors of daily aggregated traffic. This enhancement allows to derive clusters with desired characteristics in terms of both similar shape of the daily traffic profile and similar busy hour characteristics of each profile. The proposed method is used to obtain traffic profiles from several mobile networks in Europe and Asia. We discuss the differences in characteristics of profiles obtained as a function describing BTS in the context of load shape and its busy hour.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
3GPP TS 25.401, Technical Specification Group Radio Access Network: UTRAN Overall Description
Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn.
Leung, K.K., Massey, W.A., Whitt, W.: Traffic Models for Wireless Communication Networks. IEEE Journal on Selected Areas in Communications 12(8) (1994)
Li, X., Bigos, W., Goerg, C., Timm-Giel, A., Klug, A.: Dimensioning of the IP-based UMTS Radio Access Network with DiffServ QoS Support. In: Proc. of the 19th ITC Specialist Seminar on Network Usage and Traffic (ITC SS 19), Technische Universität Berlin, and Deutsche TelekomLaboratories (2008)
Maciejewski, H., Sztukowski, M., Chowanski, B.: Traffic Profiling in Mobile Networks Using Machine Learning Techniques. In: Snasel, V., Platos, J., El-Qawasmeh, E. (eds.) ICDIPC 2011, Part I. CCIS, vol. 188, pp. 132–139. Springer, Heidelberg (2011)
McGregor, A., Hall, M., Lorier, P., Brunskill, J.: Flow Clustering Using Machine Learning Techniques. In: Barakat, C., Pratt, I. (eds.) PAM 2004. LNCS, vol. 3015, pp. 205–214. Springer, Heidelberg (2004)
Sztukowski, M., Maciejewski, H., Chowanski, B., Koonert, M.: Dimensioning of Packet Networks Based on Data-Driven Traffic Profile Modeling. In: Proc. of the First European Teletraffic Seminar (ETS 2011), Poznan (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sztukowski, M., Maciejewski, H., Cader, A. (2012). Enhanced Approach of Traffic Profiling for Dimensioning of Mobile Wireless Networks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_85
Download citation
DOI: https://doi.org/10.1007/978-3-642-29350-4_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29349-8
Online ISBN: 978-3-642-29350-4
eBook Packages: Computer ScienceComputer Science (R0)