In general, network traffic data has a heavy-tailed probability distribution. The Entropy-Based Heavy Tailed Distribution Transformation (EHTDT) has been developed to convert the heavy tailed network traffic data distribution into a transformed probability distribution. In practice, the entropy distribution of the transformed probability distribution exhibits a type of linearity that gives rise to an eigenstructure that allows the characterization of network traffic data to effectively lossily compress network traffic data via the Rate Controlled Eigen-Based Coding. The aforementioned eigenstructure is motivated by singular value decomposition theory. A very high compression ratio can be achieved by the proposed method. Results of applying the methods to real network traffic data network traffic data are presented.
- Design Engineering Division and Computers in Engineering Division
Entropy-Based Heavy Tailed Distribution Transformation for Network Traffic Analysis
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Han, KJ. "Entropy-Based Heavy Tailed Distribution Transformation for Network Traffic Analysis." Proceedings of the ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 3: 30th Computers and Information in Engineering Conference, Parts A and B. Montreal, Quebec, Canada. August 15–18, 2010. pp. 761-766. ASME. https://doi.org/10.1115/DETC2010-28522
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