Towards a Robust Transport Network With Self-adaptive Network Digital Twin

TL;DR

This paper presents a self-adaptive Network Digital Twin architecture for transport networks that maintains accurate delay predictions under traffic variability by using telemetry and concept drift detection to improve resilience and synchronization.

Contribution

It introduces a novel self-adaptive NDT architecture focusing on the operational phase, enhancing resilience against traffic variability through telemetry and concept drift detection techniques.

Findings

Achieves at least 64% improvement in delay prediction after traffic drift.

Achieves at least 21% improvement in jitter prediction after traffic drift.

Demonstrates effectiveness across various network topologies and traffic patterns.

Abstract

The ability of the Network digital twin (NDT) to remain aware of changes in its physical counterpart, known as the physical twin (PTwin), is a fundamental condition to enable timely synchronization, also referred to as twinning. In this way, considering a transport network, a key requirement is to handle unexpected traffic variability and dynamically adapt to maintain optimal performance in the associated virtual model, known as the virtual twin (VTwin). In this context, we propose a self-adaptive implementation of a novel NDT architecture designed to provide accurate delay predictions, even under fluctuating traffic conditions. This architecture addresses an essential challenge, underexplored in the literature: improving the resilience of data-driven NDT platforms against traffic variability and improving synchronization between the VTwin and its physical counterpart. Therefore, the contributions of this article rely on NDT lifecycle by focusing on the operational phase, where telemetry modules are used to monitor incoming traffic, and concept drift detection techniques guide retraining decisions aimed at updating and redeploying the VTwin when necessary. We validate our architecture with a network management use case, across various emulated network topologies, and diverse traffic patterns to demonstrate its effectiveness in preserving acceptable performance and predicting quality of service (QoS) metrics under unexpected traffic variation, such as delay and jitter. The results in all tested topologies, using the normalized mean square error as the evaluation metric, demonstrate that our proposed architecture, after a traffic concept drift, achieves a performance improvement in per-flow delay and jitter prediction of at least 64% and 21%, respectively, compared to a configuration without NDT synchronization.