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Segment-based adaptive hyper-Erlang model for long-tailed network traffic approximation

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Abstract

Modeling the long-tailedness property of network traffic with phase-type distributions is a powerful means to facilitate the consequent performance evaluation and queuing based analysis. This paper improves the recently proposed Fixed Hyper-Erlang model (FHE) by introducing an adaptive framework (Adaptive Hyper-Erlang model, AHE) to determine the crucially performance-sensitive model parameters. The adaptive model fits long-tailed traffic data set directly with a mixed Erlang distribution in a new divide-and-conquer manner. Compared with the well-known hyperexponential based models and the Fixed Hyper-Erlang model, the Adaptive Hyper-Erlang model is more flexible and practicable in addition to its accuracy in fitting the tail behavior.

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, 610064, China

    Junfeng Wang

  2. State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China

    Jin Liu

  3. Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing, 100080, China

    Chundong She

Authors
  1. Junfeng Wang
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  2. Jin Liu
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  3. Chundong She
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Correspondence to Junfeng Wang.

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Wang, J., Liu, J. & She, C. Segment-based adaptive hyper-Erlang model for long-tailed network traffic approximation. J Supercomput 45, 296–312 (2008). https://doi.org/10.1007/s11227-008-0173-5

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  • DOI: https://doi.org/10.1007/s11227-008-0173-5

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