In the 6G era, integrating Mobile Edge Computing (MEC) and Digital Twin (DT) technologies presents a transformative approach to enhance network performance through predictive, adaptive control for energy-efficient, low-latency communication. This paper presents the EcoEdgeTwin model, an innovative framework that harnesses the synergy between MEC and DT technologies to ensure efficient network operation. We optimize the utility function within the EcoEdgeTwin model to balance enhancing users' Quality of Experience (QoE) and minimizing latency and energy consumption at edge servers. This approach ensures efficient and adaptable network operations, utilizing DT to synchronize and integrate real-time data seamlessly. Our framework achieves this by implementing robust mechanisms for task offloading, service caching, and cost-effective service migration. Additionally, it manages energy consumption related to task processing, communication, and the influence of DT predictions, all essential for optimizing latency and minimizing energy usage. Through the utility model, we also prioritize QoE, fostering a user-centric approach to network management that balances network efficiency with user satisfaction. A cornerstone of our approach is integrating the advantage actor-critic algorithm, marking a pioneering use of deep reinforcement learning for dynamic network management. This strategy addresses challenges in service mobility and network variability, ensuring optimal network performance matrices. Our extensive simulations demonstrate that compared to benchmark models lacking DT integration, EcoEdgeTwin framework significantly reduces energy usage and latency while enhancing QoE.

翻译：在6G时代，集成移动边缘计算（MEC）与数字孪生（DT）技术为实现能效优先、低延迟通信的预测性自适应控制网络性能提供了变革性方案。本文提出EcoEdgeTwin模型这一创新框架，通过挖掘MEC与DT技术的协同潜力保障网络高效运行。我们在EcoEdgeTwin模型中优化效用函数，以平衡增强用户服务质量体验（QoE）与降低边缘服务器延迟及能耗之间的目标。该方法通过DT实现实时数据的无缝同步与集成，确保网络运行的高效性与自适应性。框架通过实施任务卸载、服务缓存及成本效益型服务迁移等稳健机制，同时管理任务处理、通信及DT预测影响相关的能耗——这些要素对优化延迟与降低能源消耗至关重要。通过效用模型，我们还优先考虑QoE，构建兼顾网络效率与用户满意度的用户中心型网络管理方案。本方法的核心是集成优势演员-评论员算法，标志着深度强化学习在动态网络管理中的开创性应用。该策略有效应对服务移动性与网络可变性挑战，确保网络性能指标的最优化。广泛仿真结果表明，相较于缺乏DT集成的基准模型，EcoEdgeTwin框架在增强QoE的同时显著降低了能耗与延迟。