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Modeling and Simulation of Traffic with Integrated Services at Media Getaway Nodes in Next Generation Networks

机译:下一代网络中媒体逃逸节点处集成服务的流量建模和仿真

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This paper is devoted to modeling and simulation of traffic with integrated services at media gateway nodes in the next generation networks, based on Markov reward models (MRM). The bandwidth sharing policy with the partial overlapped transmission link is considered. Calls arriving to the link that belong to I BR and ABR traffic classes, are presented as independent Poisson processes and Markov processes with constant intensity and/or random input stream, and exponential service delay time that is defined according to MRM. Traffic compression is calculated using clustering and learning vector quantification (e.g., self-organizing neural map). Numerical examples and simulation results are provided for communication networks of various sizes. Compared with the other methods for traffic compression calculations, the suggested approach shows substantial reduction in numerical complexity.
机译:本文致力于基于马尔可夫奖励模型(MRM)的下一代网络中媒体网关节点处集成服务的流量建模和仿真。考虑具有部分重叠的传输链路的带宽共享策略。到达属于I BR和ABR流量类别的链路的呼叫以具有恒定强度和/或随机输入流以及根据MRM定义的指数服务延迟时间的独立泊松过程和马尔可夫过程表示。使用聚类和学习向量量化(例如,自组织神经图)来计算流量压缩。为各种规模的通信网络提供了数值示例和仿真结果。与其他用于流量压缩计算的方法相比,建议的方法显示出数值复杂度的大幅降低。

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