G.Y. Lazarou, X. Xia, and V.S. Frost (USA)
Network Traffic Modeling, Variability, Burstiness
In this paper, we propose a novel and mathematically rig orous measure of variability, called the index of variabil ity (Hv( )), that fully and accurately captures the degree of variability of a typical network traffic process at each time scale and is analytically tractable for many popular traffic models. Using this proposed measure, we then an alyzed two traffic models: the Two-State Markov Mod ulated Poisson Process (MMPP) and the renewal process with hyperexponential interarrival time distributions of or der two (RPH2). Two-state MMPP models are popular in modeling the superposition of packet voice streams. The results show that the traffic variability can exhibit a non monotonic behavior. In addition, the results suggest that renewal processes with interarrival times hyperexponen tially distributed are suitable for modeling network traffic processes with high variability over a broad range of time scales.
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