Source Traffic Analysis
5. CONCLUSIONS AND FUTURE WORK
This article brings the focus of traffic analysis to the study of the behavior of network traffic sources.
It provides a compressed version of the models that can be used to simulate the main traffic classes a source can produce, along with a compilation of the parameters that define them. After summarizing
ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 6, No. 3, Article 21, Publication date: August 2010.
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the literature, we presented the results obtained from the analysis conducted over real traffic traces, collected in different scenarios and containing traffic from several classes, that characterize the traffic generated by an individual source (in the sense of end user or terminal traffic source). By fitting some well-known distributions to the experimental data, we tried to indicate the models that best describe the processes representing each traffic class. We have concluded that the Weibull distribution was ca-pable of adapting itself to the majority of the empirical sequences of interarrival times and of bits per time unit. On the other hand, the distribution of the packet sizes is, most of the times, dominated by probability peaks around well defined values. It is thus suggested that the computational synthe-sis of such aspect should be made recurring either to previously collected data, or to the bi/trimodal distributions specified in this article (see Table VII).
It was also concluded that, for most of the analyzed traces the values of the bit count per time unit were positively correlated, at least upto the aggregation scale of 40s. This observation not only corroborates previous studies about the presence of self-similarity in network aggregation points, as it also suggests that one might want to simulate some dependence when modeling the aforementioned aspect of source traffic. The referred property achieves higher expression in scenarios where several streams are concurring for the same resource or large files are being exchanged, as is the case of file sharing traffic.
In the future, we plan to describe the impact of this work in simulations concerning prediction and trends of network traffic volume. It is also our intention to formalise the simulation procedure of the several traffic classes, and of their aggregation, in a separate publication. We found that the ACF of some of the traces could be satisfactorily approximated by a power law like the one in (3), which simplifies the simulation of that particular aspect, since it enables the usage of self-similar or multifractal series generators followed by their suitable transformation into the observed marginal distribution. That will be the subject of a more detailed research work, and of a future publication.
ACKNOWLEDGMENTS
The authors are thankful to all the anonymous reviewers who contributed constructively for the im-provement of this work.
REFERENCES
ANSARI, N., LIU, H., SHI, Y. Q.,ANDZHAO, H. 2002. On modeling MPEG video traffics. IEEE Trans. Broadcast. 48, 4, 337–347.
BARFORD, P.ANDCROVELLA, M. 1998. Generating representative Web workloads for network and server performance evalua-tion. ACM SIGMETRICS Perform. Evalu. Rev. 26, 1, 151–160.
BERAN, J., SHERMAN, R., TAQQU, M. S.,ANDWILLINGER, W. 1995. Long-range dependence in variable bit-rate video traffic. IEEE Trans. Comm. 43,234, 1566–1579.
BOLOTIN, V. A. 1994. Modeling call holding time distributions for CCS network design and performance analysis. IEEE J.
Selec. Areas Comm. 12,3, 433–438.
CAO, J., CLEVELAND, W. S., LIN, D.,ANDSUN, D. X. 2002. Internet traffic tends toward poisson and independent as the load increases. In Nonlinear Estimation and Classification, C. Holmes, D. Denison, M. Hansen, B. Yu, and B. Mallick, Eds. Springer, NY, 83–109.
CASILARI, E., CANO-GARC´IA, J. M., GONZALEZ´ -CANETE˜ , F. J.,ANDSANDOVAL, F. 2004. Modelling of individual and aggregate Web traffic. In High Speed Networks and Multimedia Communications. Lecture Notes in Computer Science, vol. 3079. Springer, 84–95.
CASILARI, E., MONTES, H.,ANDSANDOVAL, F. 2002. Modelling of voice traffic over IP networks. In Proceedings of the 3rd International Symposium On Communication Systems, Networks and Digital Signal Processing. 411–414.
CISCODOCUMENTSERVER. 2002. Traffic analysis for VoIP. Tech. rep.http://www.cisco.com/univercd/cc/td/doc/cisintwk/
intsolns/voipsol/ta_is%d.htm.
COX, D. R. 1984. Long-range dependence: A review. In Statistics: An Appraisal. Iowa State University Press, 55–74.
ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 6, No. 3, Article 21, Publication date: August 2010.
Source Traffic Analysis • 21:23
CROVELLA, M. E.ANDBESTAVROS, A. 1995. Explaining World Wide Web traffic self-similarity. Tech. rep. 1995-015, Computer Science Department, Boston University.
DAWOOD, A. M.ANDGHANBARI, M. 1999. Content-based MPEG video traffic modelling. IEEE Trans. Multimedia 1, 1, 77–87.
FELDMANN, A., GILBERT, A. C.,ANDWILLINGER, W. 1998. Data networks as cascades: Investigating the multifractal nature of Internet WAN traffic. ACM SIGCOMM Comput. Comm. Rev. 28, 4, 42–55.
FREEMAN, R. L. 2004. Telecommunication System Engineering, 4th Ed. Wiley-IEEE Press.
GARRETT, M. W.ANDWILLINGER, W. 1994. Analysis, modeling and generation of self-similar VBR Video Traffic. ACM SIG-COMM Comput. Comm. Rev. 24,4, 269–280.
HEYMAN, D. P. 1997. The GBAR source model for VBR videoconferences. IEEE/ACM Trans. Netw. 5, 4, 554–560.
HEYMAN, D. P., TABATABAI, A.,ANDLAKSHMAN, T. V. 1992. Statistical analysis and simulation study of video teleconference traffic in ATM networks. IEEE Trans. Circ. Syst. Video Techn. 2, 1, 49–59.
HUANG, C., DEVETSIKIOTIS, M., LAMBADARIS, I.,ANDKAYE, A. R. 1995. Modeling and simulation of self-similar variable bit rate compressed video: A unified approach. ACM SIGCOMM Comput. Comm. Rev. 25, 4, 114–125.
HUGHES, C. J., GHANBARI, M.,ANDPEARSON, D. E. 1993. Modeling and subjective assessment of cell discard in ATM video.
IEEE Trans. Image Process. 2,2, 212–222.
ISHAC, J. 2001. FTP traffic generator. Tech. rep., Case Western Reserve University.
JIANG, W.ANDSCHULZRINNE, H. 2000. Analysis of on-off patterns in VoIP and their effect on voice traffic aggregation. In Proceedings of the 9th IEEE International Conference on Computer Communications and Networks. 82–87.
KRUNZ, M. M.ANDMAKOWSKI, A. M. 1998. Modeling video traffic using M/G/Inf input processes: A compromise between Markovian and LRD models. IEEE J. Select. Areas Comm. 16, 5, 733–748.
LELAND, W. E., TAQQU, M. S., WILLINGER, W.,ANDWILSON, D. 1994. On the self-similar nature of ethernet traffic (extended version). IEEE/ACM Trans. Netw. 2, 1, 1–15.
LIU, H., ANSARI, N.,ANDSHI, Y. Q. 1999. Markov-modulated self-similar processes: MPEG coded video traffic modeler and synthesizer. In Proceedings of The Global Telecommunications Conference (GLOBECOM’99). Vol. 2., 1184–1188.
LIU, X.-G.ANDBABIARZ, J. 2007. Simulation results for explicit PCN marking and flow termination (Preemption). White paper, Nortel.
MAGLARIS, B., ANASTASSIOU, D., SEN, P., KARLSSON, G.,ANDROBBINS, J. D. 1988. Performance models of statistical multiplexing in packet video communications. IEEE Trans. Comm. 36, 7, 834–844.
MELAMED, B., RAYCHAUDHURI, D., SENGUPTA, B.,ANDZDEPSKI, J. 1994. TES-based video source modeling for performance evaluation of integrated networks. IEEE Trans. Comm. 42, 10, 2773–2777.
PAXSON, V.ANDFLOYD, S. 1995. Wide-area traffic: The failure of poisson modeling. IEEE/ACM Trans. Netw. 3, 3, 226–244.
RAMAMURTHY, G.ANDSENGUPTA, B. 1992. Modeling and analysis of a variable bit rate video multiplexer. In Proceedings of the INFOCOM Annual Joint Conference of the IEEE Computer and Communications Societies. Vol. 2. 817–827.
REININGER, D., MELAMED, B., RAYCHAUDHURI, D.,ANDSENGUPTA, B. 1994. Variable bit rate video: characteristics, modeling and multiplexing. In Proceedings of the 14th International Teletraffic Congress. Vol. 1a. 295–306.
ROSE, O. 1995a. Simple and efficient models for variable bit rate MPEG video traffic. Tech. rep., Institute of Computer Science, University of Wuerzburg.
ROSE, O. 1995b. Statistical properties of MPEG video traffic and their impact on traffic modelling in ATM systems. In Proceedings of the 20th Conference on Local Computer Networks. 397–406.
ROSE, O.ANDFRATER, M. R. 1993. A comparison of models for VBR video traffic sources in B-ISDN. Tech. rep., University of Wuerzburg.
SEGER, J. 2003. Modelling approach for VoIP traffic aggregations for transferring tele-traffic trunks in QoS enabled IP-Backbone Environment. In Proceedings of the 1st International Workshop on Inter-domain Performance and Simulation.
SEN, P., MAGLARIS, B., RIKLI, N.-E.,ANDANASTASSIOU, D. 1989. Models for packet switching of variable-bit-rate video sources.
IEEE J. Select. Areas Comm. 7,5, 865–869.
TCPDUMP. 2008. TCPDUMP public repository.
WILLINGER, W., PAXSON, V.,ANDTAQQU, M. S. 1998. Self-similarity and heavy tails: Structural modeling of network traffic. In A Practical Guide to Heavy Tails: Statistical Techniques and Applications, R. J. Adler, R. E. Feldman, and M. S. Taqqu Eds., Birkhuser, Boston, Chapter Applications, 27–53.
WILLINGER, W., TAQQU, M., SHERMAN, R.,ANDWILSON, D. 1997. Self-similarity through high-variability: Statistical analysis of ethernet LAN traffic at the source level. IEEE/ACM Tran. Netw. 5, 1, 71–86.
Received February 2008; revised October 2008, February 2009, June 2009; accepted August 2009
ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 6, No. 3, Article 21, Publication date: August 2010.
Classi cation of Peer-to-Peer Traf c by Exploring the Heterogeneity of Traf c Features Through Entropy