Network Traffic Generation: A Combination of Stochastic and Self-similar
Network traffic generation is a vital part of traffic research as the exponential growth of the number of servers, as well as the number of users. Various researchers have reported traffic analysis that demonstrates different results of traffic modeling, such as Poisson distribution or considerable burstiness on a range of time scales with properties of self-similarity. Due to the distinct standpoint about the network traffic distribution, traffic generators have been developed dissimilar. In order to simulate the network traffic all-around, we present a technology of traffic generation which compose of stochastic and self-similar, and provide details on algorithm and implementation. In this paper, a new model for multi-patterns network traffic generation is presented. This model is based on three elements including the latency of network frames, Hurst exponent and network traffic types. This paper analyses the three parameters and finds a way to describe the relations among these. We choose multifrartal wavelet model as the basis method, and perfect it applicable to multi-patterns network traffic generation. In this research, a network traffic generation system based on programming multicore processor is build and the test result is given.
Multi-patterns Hurst Multifractal Wavelet Model Multi-core Network Traffic
Zhao Rongcai Zhang Shuo
Department of Computer and Science China National Digital Switching System Center (NDSC) Zhengzhou, Department of Computer and Science China National Digital Switching System Center (NDSC) Zhenezhou,
国际会议
The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)
沈阳
英文
171-175
2010-03-27（万方平台首次上网日期，不代表论文的发表时间）