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By Yagang Zhang
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The total traffic trace is one-hour long and the two clients considered are active for all the duration of the measurement. Neither the aggregated connection arrival process nor the single clients show evident non stationarity. 2, the slope of Modσ2 vs τ in the log-log plot can be used as a measure of the power-law exponent. In order to avoid border effects and poor confidence Automatic Internet Traffic Classification for Early Application Identification 150 50 0 Connections/s 250 All clients 0 1000 2000 3000 Time, s 30 20 10 0 Connections/s 40 Client 1 0 1000 2000 3000 Time, s 15 10 5 0 Connections/s 20 Client 2 0 1000 2000 3000 Time, s Fig.
Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification, IMC ’04: Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, ACM, New York, NY, USA, pp. 135–148. Verticale, G. (2009). An empirical study of self-similarity in the per-user-connection arrival process, Telecommunications, 2009. AICT ’09. Fifth Advanced International Conference on, pp. 101–106. Verticale, G. & Giacomazzi, P. (2008). Performance evaluation of a machine learning algorithm for early application identification, Computer Science and Information Technology, 2008.
37(1): 5–16. Crovella, M. & Bestavros, A. (1997). Self-similarity in World Wide Web traffic: evidence and possible causes, Networking, IEEE/ACM Transactions on 5(6): 835–846. Leland, W. , Taqq, M. , Willinger, W. & Wilson, D. V. (1993). On the self-similar nature of Ethernet traffic, in D. P. ), ACM SIGCOMM, San Francisco, California, pp. 183–193. Moore, A. W. & Zuev, D. (2005). Internet traffic classification using bayesian analysis techniques, SIGMETRICS ’05: Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems, ACM, New York, NY, USA, pp.
Application of Machine Learning by Yagang Zhang