  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.
