Digital Twin Networks: Learning Dynamic Network Behaviors from Network Flows

Guozhi Lin, Jingguo Ge, Yulei Wu, Hui Li, Liangxiong Li
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引用次数: 1

Abstract

The Digital Twin Network (DTN) is a key enabling technology for efficient and intelligent network management in modern communication networks. Learning dynamic net-work behaviors at the flow granularity is a core element for realizing DTN with accurate network modelling. However, it is challenging due to the complexity of network architectures and the proliferation of emerging network applications. In this paper, we devise a Packet-Action Sequence Model to represent all possible packets behaviors in a unified way. Besides, we propose a novel and effective algorithm to assess whether the behavior pattern is time dependent or independent by using the temporal characteristics of packets in a network flow, so as to learn the key factors of packets that contribute to network behaviors. Based on two typical scenarios, i.e., packet caching and routing, the experimental results verify that the proposed algorithm can identify network behavior patterns and learn key factors affecting the behaviors with over 99 % accuracy.
数字孪生网络:从网络流中学习动态网络行为
数字孪生网络(DTN)是现代通信网络中实现高效、智能网络管理的关键使能技术。在流粒度上学习动态网络行为是实现DTN准确网络建模的核心要素。然而,由于网络体系结构的复杂性和新兴网络应用的激增,这是一个挑战。在本文中,我们设计了一个包动作序列模型,以统一的方式表示所有可能的数据包行为。此外,我们提出了一种新颖有效的算法,利用网络流中数据包的时间特征来评估行为模式是时间依赖还是独立的,从而了解数据包中影响网络行为的关键因素。基于分组缓存和路由两种典型场景,实验结果验证了该算法能够识别网络行为模式,并以99%以上的准确率学习影响行为的关键因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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