# Digital Twin-Empowered Deep Reinforcement Learning for Intelligent VNF Migration in Edge-Core Networks

## TL;DR

This paper introduces a Digital Twin-enhanced deep reinforcement learning framework for optimizing virtual network function migration in edge-core networks, achieving lower latency and energy use through environment simulation and adaptive decision-making.

## Contribution

It presents a novel DT-empowered DRL approach with a multi-task VAE and LSTM to improve training efficiency and migration performance in network management.

## Key findings

- Significant reduction in end-to-end delay.
- Notable decrease in energy consumption.
- Faster policy convergence compared to traditional methods.

## Abstract

The growing demand for services and the rapid deployment of virtualized network functions (VNFs) pose significant challenges for achieving low-latency and energy-efficient orchestration in modern edge-core network infrastructures. To address these challenges, this study proposes a Digital Twin (DT)-empowered Deep Reinforcement Learning framework for intelligent VNF migration that jointly minimizes average end-to-end (E2E) delay and energy consumption. By formulating the VNF migration problem as a Markov Decision Process and utilizing the Advantage Actor-Critic model, the proposed framework enables adaptive and real-time migration decisions. A key innovation of the proposed framework is the integration of a DT module composed of a multi-task Variational Autoencoder and a multi-task Long Short-Term Memory network. This combination collectively simulates environment dynamics and generates high-quality synthetic experiences, significantly enhancing training efficiency and accelerating policy convergence. Simulation results demonstrate substantial performance gains, such as significant reductions in both average E2E delay and energy consumption, thereby establishing new benchmarks for intelligent VNF migration in edge-core networks.