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A general model for long-tailed network traffic approximation

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Abstract

The long-tailed distribution characterizes many Internet traffic properties which are often modeled by Lognormal distribution, Weibull or Pareto distribution theoretically. However, it is rather difficult to directly apply these models in traffic analysis and performance evaluation studies due to their complex representations and theoretical properties.

This paper proposes a Hyper-Erlang Model (Mixed Erlang distribution) for such long-tailed network traffic approximation. It fits network traffic with long-tailed characteristic into a mixed Erlang distribution directly to facilitate our further analysis. Compared with the well-known hyperexponential based method, the mixed Erlang model is more accurate in fitting the tail behavior and also computationally efficient. Further investigations on the M/G/1 queueing behavior also prove the efficiency of the Mixed Erlang based approximation.

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Authors and Affiliations

  1. College of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, P.R. China

    Junfeng Wang & Mingtian Zhou

  2. Institute of Software, Chinese Academy of Sciences, Beijing, 100080, P.R. China

    Junfeng Wang & Lei Li

  3. Communication Institute, Chongqing, 400035, P.R. China

    Hongxia Zhou

Authors
  1. Junfeng Wang
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  2. Hongxia Zhou
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  3. Mingtian Zhou
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  4. Lei Li
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Corresponding author

Correspondence to Junfeng Wang.

Additional information

An abbreviated version of this manuscript has been presented at the 3rd International Symposium on Parallel and Distributed Processing and Applications (ISPA’05), Nanjing, P.R.China, November 2005.

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Wang, J., Zhou, H., Zhou, M. et al. A general model for long-tailed network traffic approximation. J Supercomput 38, 155–172 (2006). https://doi.org/10.1007/s11227-006-7944-7

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  • DOI: https://doi.org/10.1007/s11227-006-7944-7

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