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.

翻译：服务需求的日益增长与虚拟化网络功能（VNF）的快速部署，对在现代边-核网络基础设施中实现低延迟与高能效的编排提出了重大挑战。为应对这些挑战，本研究提出了一种数字孪生赋能的深度强化学习框架，用于智能VNF迁移，旨在联合最小化平均端到端延迟与能耗。通过将VNF迁移问题建模为马尔可夫决策过程并利用优势演员-评论家模型，所提框架能够实现自适应的实时迁移决策。该框架的一个关键创新在于集成了一个由多任务变分自编码器与多任务长短期记忆网络构成的数字孪生模块。该组合共同模拟环境动态并生成高质量的合成经验，显著提升了训练效率并加速了策略收敛。仿真结果表明了显著的性能提升，例如平均端到端延迟与能耗均大幅降低，从而为边-核网络中的智能VNF迁移确立了新的基准。