Network Traffic Self-similarity Analysis And Research

The popularization and enhancement of new network applications and the demand of broad band service make network traffic increasing rapidly such as multimedia and VoIP etc, so that traditional traffic model character will no longer be adapted to current network traffic. The effective method for studying self-similar traffic is to build models so that models could describe network character more authentic, and be applied to simulation research. The self-similar traffic becomes a hotspot for researchers.In the paper, we introduce research background, mathematic definition, character of self-similar traffic, including several common methods of creating self-similar traffic and estimating Hurst parameters, Then we analyze the cause of self-similar traffic appearance, the relation between TCP and self-similar, influence to network performance of self-similar traffic in detail. The paper includes three experimentations:1. The research on the traffic of campus network. We have measured local network traffic with inspect system of network center, we deal with packet arrival process and adopt V/T method to estimate Hurst parameter. We validate that the packet arrival process of local traffic is self-similar;The value of Hurst parameter is decreasing with the traffic state from busyness to idle;In the busy state, the longer time scale of capturing traffic, the higher the value of Hurst parameter.2. Simulation experimentation on self-similar traffic. In order to model self-similar traffic source and make simulation in NS, we use the method of FFT-FGN to synthesize sample path. Comparing with the measured actual traffic based on the same estimating method of Hurst. We validate the effectiveness of the way for creating self-similar traffic. It can depict the traffic character availably. For a true self-similar process, the variance-time plot for a given value of H will coincide with the corresponding line for H = 0.7, but for H ≧ 0.75, the variance-time plot was sometimes lower (i.e. steeper-sloped) than expected.3. Study on influences to network performance of self-similar traffic. Based on the simulation experimentation, we analyze influences self-similar traffic makes on the packet drop rate at different protocols and network loads;then compare the test withthe one on Poission traffic. We also seeing about how the packet drop rate make influence on self-similar character with adding the packet loss module in the simulation. The results are as follows: With the increasing of TCP and UDP loads and the degree of self-similar character, the packet drop rate grows on, further more, this extend of drop rate is higher than the one on Poission model. It shows traditional models have deficiency in describing network character well and truly;and also approve self-similar character has more influences on network performance.