ABSTRACT
Traffic shaping effect may have significant impact on end-to-end Quality of Service (QoS) provisioning. Therefore, it should be carefully studied in order to allow the creation of appropriate traffic models to be used for simulations. First, to demonstrate the traffic shaping effect, we present statistical analyses on real-time measurements of diverse traffic sources (voice and video over IP) in a 3G network. By comparing the statistical distributions of the packet inter-arrival times for both the forward and backward direction, we can demonstrate directly the end-to-end traffic shaping effect introduced by the IP core network. Hence, we argue that distributed QoS management approach is needed. Additionally, we give the mean, variance, mean standard deviation, skewness, and kurtosis of the inter-arrival times, which can be used as input for simulation models. The accurate validation of the probability distributions is ensured by the Wolfram Mathematica and Crystal Ball statistical tools. Second, for the same set of measurements, we propose and defend with evaluations the use of the gamma distribution as best fitting function to traffic dynamics. Our proposal is applicable for traffic environments found in delay-tolerant networks, opportunistic networks, Internet of Things, sensor networks etc.
- Goleva, R. and Mirtchev, S. 2010. Traffic modeling in Disruption-Tolerant Networks. In Annual Seminar of the PTT College, Modeling and Control of Information Processes (CTP, Sofia) ISSN: 1314--2771, 6--20.Google Scholar
- Halas, M., Javorcek, L., Ková, A. 2012. Impact of SRTP Protocol on VoIP Call Quality. In: Workshop of the 12th International Conference KTTO 2012, November 14--16, 2012, Malenovice, Czech Republic, pp. 36--40, ISBN 978--80--248--2810--7. (MPNS)Google Scholar
- Voznak, M., Rozhon, J., and Rezac, F. 2012. Relation between Computational Power and Time Scale for Breaking Authentication in SIP Protocol, In: Workshop of the 12th International Conference KTTO 2012, November 14--16, 2012, Malenovice, Czech Republic, pp. 36--40, ISBN 978--80--248--2810--7. (MPNS)Google Scholar
- Utpal, P., Subramanian, A.P., Buddhikot, M.M., Das, S.D. 2011. Understanding Traffic Dynamics in Cellular Data Networks, Infocon 2011, 2011.Google Scholar
- Svoboda, P. 2008. Measurement and modelling of Internet traffic over 2.5 and 3G cellular core networks. Ph.D. dissertation (Vienna University of Technology).Google Scholar
- Shafiq, M., Lusheng, Z., Ji Alex X., Liu Jeffrey Pang, Jia Wang, A First Look at Cellular Machine-to-Machine Traffic-Large Scale Measurement and Characterization, SIGMETRICS'12, June 11-15, 2012, London, England, UK. Copyright 2012 ACM 978--1--4503--1097-0/12/06. Google ScholarDigital Library
- Mavromoustakis, C. X. and Zerfiridis, K. G. 2010. On the diversity properties of wireless mobility with the user-centered temporal capacity awareness for EC in wireless devices. In: Proceedings of the Sixth IEEE International Conference on Wireless and Mobile Communications, (ICWMC 2010, Valencia, Spain), 367--372. Google ScholarDigital Library
- Samanta, R.J., Bhattacharjee P., and Sanyal, G. 2010. Modeling Cellular Wireless Networks Under Gamma Inter-Arrival and General Service Time Distributions, International Journal of Electrical and Computer Engineering 5:2 2010Google Scholar
- Bulakci, Saleh, A.B., Redana, S., Raaf, B., and Hämäläinen, J. 2013. Resource sharing in LTE-Advanced relay networks: uplink system performance analysis, Trans. Emerging Tel. Tech. 2013; 24:32--48Google ScholarCross Ref
- Wang, Y.C., Chuang, C.H., Tseng, Y.C., and Shen, C.C. 2013. A lightweight, self-adaptive lock gate designation, scheme for data collection in long-thin wireless sensor networks, Wirel. Commun. Mob. Comput. 2013; 47--62Google ScholarCross Ref
- Mirtchev, S. and Goleva, R. 2009. Discrete time single server queueing model whit a multimodal packet size distribution. In: Proceedings of a Conjoint Seminar "Modeling and Control of Information Processes" (T. Atanasova (ed), Sofia, Bulgaria) ISBN: 978--954--9332--55--1, 83--101.Google Scholar
- Mirtchev, S. and Goleva, R. 2009. A discrete time queuing model with a constant packet size. In ICEST 2009 (V. Tarnovo, Bulgaria).Google Scholar
- Schmeink, A., 2011. On fair rate adaption in interference-limited systems, Eur. Trans. Telecomms. 2011; 22:200--210Google Scholar
- Eslami, M., Elliott, R.C., Krzymie W.A., and Al-Shalash, N. 2012. Location-assisted clustering and scheduling for coordinated homogeneous and heterogeneous cellular networks (pages 84--101), published online: 26 NOV 2012 | DOI: 10.1002/ett.2577Google Scholar
- Harsini, J.S., and Lahouti, F. 2012. Effective capacity optimization for multiuser diversity systems with adaptive transmission (pages 567--584), Article first published online: 16 APR 2012 | DOI: 10.1002/ett.25117Google Scholar
- Mirtchev, S. and Goleva, R. 2008. Evaluation of Pareto/D/1/k queue by simulation. In International Book Series "Information Science&Computing" No.1 (Vol.2).Google Scholar
- Mirtchev, S., Goleva, R. and Alexiev, V. 2010. Evaluation of single server queueing system with Polya arrival process and constant service time. In: Proceedings of the International Conference on Information Technologies 203--212Google Scholar
- Ong, E. and Khan, J. Y. 2009. A unified QoS-inspired load optimization framework for multiple access points based wireless LANs. In WCNC 2009 proceedings. Google ScholarDigital Library
- Sun, H. and Williamson, C. 2009. Downlink performance for mixed Web/VoIP traffic in 1xEVDO revision a networks. In ICC 2008 proceedings.Google Scholar
- Buhagiar, J. K. and Debono, C.J.2009. Exploiting adaptive window techniques to reduce TCP congestion in mobile peer networks. In WCNC 2009 proceedings. Google ScholarDigital Library
- Goleva, R., Atamian, D., Mirtchev, S., Dimitrova, D., Grigorova, L., 2012. Traffic sources measurement and analysis in UMTS. In HP-MOSys'12, Cyprus. Google ScholarDigital Library
Index Terms
- Traffic shaping measurements and analyses in 3G network
Recommendations
Traffic shaping in aggregate-based networks: implementation and analysis
The Differentiated Services architecture allows providing scalable Quality of Service by means of aggregating flows to a small number of traffic classes. Among these classes a Premium Service is defined, for which end-to-end delay guarantees are of ...
Comments