Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Proof of a fundamental result in self-similar traffic modeling
ACM SIGCOMM Computer Communication Review
Heavy-tailed probability distributions in the World Wide Web
A practical guide to heavy tails
Evidence for long-tailed distributions in the internet
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Self-Similar Network Traffic and Performance Evaluation
Self-Similar Network Traffic and Performance Evaluation
Review: Application classification using packet size distribution and port association
Journal of Network and Computer Applications
Traffic modeling and classification using packet train length and packet train size
IPOM'06 Proceedings of the 6th IEEE international conference on IP Operations and Management
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The increasing role of information technology has resulted in high demand for Internet services. Consequently, Internet traffic modeling has become an essential field of study. Due to the inadequacy of existing traffic models, new traffic models are needed to allow proper network design and dimensioning. In this paper the patterns of Internet traffic types are discussed. Based on the real network trace collected from the Access Network, Pearson System Analysis is done to identify a univariate distributions for each traffic type. But chi-square test of fit failed. The experimental results using the statistical pattern matching technique, Vector Quantization gives models fitting each Internet service traffic. These models fit with 0% error. The training is done with five days of data, testing with 30 days of data. These models can be used in performance analysis or simulation of traffic types, and to identify the misuse of a traffic type.