Traffic Distribution Forecasting in Packet-Switching Networks

F. Zandi
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Abstract

This paper presents a traffic distribution forecasting model in packet-switching networks with mapping these networks into multi-commodity networks. Firstly, the radial basis function (RBF) networks is applied to monitor and learn the real traffic distribution at present time. Then, a quadratic model is used to calibrate these functions for precise traffic distribution forecasting. The implementation of the proposed model is demonstrated through the use of a numerical example.
分组交换网络中的流量分布预测
本文提出了一种分组交换网络流量分布预测模型,并将分组交换网络映射到多商品网络中。首先,利用径向基函数(RBF)网络对当前的真实流量分布进行监测和学习;然后,利用二次元模型对这些函数进行校正,实现流量分布的精确预测。通过一个数值算例说明了所提模型的实现。
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