A Systematic Network Traffic Emulation Framework for Digital Twin Network

Abstract

Digital twin network has gradually become an important tool platform for network management and optimization. With bandwidth growing larger and speed getting higher in physical network, how to efficiently and accurately emulate network traffic in digital twin network is an important research problem. This paper proposes a complete flow emulation framework for digital twin network for the first time, which leverages unified ID and deterministic network technology to keep physical network traffic in consistency with that in digital twin network. Aiming at growing bandwidth, we use flow sampling to reduce large transmission data. Furthermore, we describe in detail an application case based on the proposed traffic emulation framework, namely, network delay measurement. Analysis results show that our proposal can achieve high delay measurement accuracy without affecting the physical network.