Accurate Long-tailed Network Traffic Approximation and Its Queueing Analysis by Hyper-Erlang Distributions

Authors:
Junfeng Wang;Hongxia Zhou;Lei Li;Fanjiang Xu
Affiliations:
Institute of Software, Chinese Academy of Sciences;University of Electronic Science and Technology of China;Institute of Software, Chinese Academy of Sciences;Institute of Software, Chinese Academy of Sciences
Venue:
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Year:
2005

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Abstract

Internet traffic has been proven to be long-tailedness and often modeled by Lognormal distribution, Weibull or Pareto distributions theoretically. However, these mathematical models hinder us in traf- fic analysis and 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 wellknown 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.