Digital Twin Networks: Learning Dynamic Network Behaviors from Network Flows

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.