In recent years, various researchers have shown that network traffic that is due to world-wide web transfers shows characteristics of self-similarity and it has been argued that this can be explained by the heavy-tailedness of many of the involved distributions. Considering these facts, developing methods that are able to handle self-similarity and heavy-tailedness is of great importance for network capacity planning purposes. However, heavy-tailed distributions cannot be used so easily for analytical or numerical evaluation studies. To overcome this problem, in this paper, we approximate the empirical distributions by analytically more tractable, that is, hyper-exponential distributions. For that purpose, we present a new fitting algorithm based on the expectation-maximisation and show it to perform well both for pure traffic statistics as well as in queuing studies.
- Traffic characterisation
- Hyper-exponential distributions
- Queuing analysis
- World Wide Web
- Heavy-tailed distributions
El Abdouni Khayari, R., Sadre, R., & Haverkort, B. R. H. M. (2003). Fitting World-Wide Web Request Traces with the EM-Algorithm. Performance Evaluation, 52(2-3), 175-191. https://doi.org/10.1016/S0166-5316(02)00179-7