Recently, mobile edge computing(MEC) has attracted considerable research effort, because mobile users can offload some tasks to edge servers that are closer to users than cloud servers for a better computing experience, which can bring enormous potential in next-generation wireless networks(5G). However, when users are moving, they may be far away from the edge server that undertakes the offloading task, resulting in unavoidable service discontinuity and degrading the user experience. Service migration mechanism is very crucial in 5G mobile Internet. In this paper, we propose a novel service migration scheme to support mobility. Our scheme is realized from three aspects: (1) we consider mobile user services can be deployed container virtual machine in corresponding edge servers, and develop container migration strategies to satisfy the trade-off between the users’ aware delay and system energy consumption; (2) we further propose a deep reinforcement learning algorithm (DRL) based such multi-user server migration strategy (DRLMSM) to effectively achieve fast decision-making; (3) we build the architecture in Software Defined Network (SDN) framework to verify the practicality and effectiveness of DRLMSM. We conduct extensive experiments, which shows that our DRLMSM scheme outperforms the classical reinforcement learning(RL) algorithm and some other baseline algorithm.

最近，移动边缘计算（MEC）吸引了大量研究工作，因为移动用户可以将一些任务卸载到比云服务器更接近用户的边缘服务器上以获得更好的计算体验，这可以为下一代无线网络带来巨大潜力。 5G）。但是，用户在移动时，可能会远离承担分流任务的边缘服务器，导致不可避免的服务中断，降低用户体验。业务迁移机制在5G移动互联网中非常关键。在本文中，我们提出了一种新的服务迁移方案来支持移动性。我们的方案是从三个方面实现的：（1）我们考虑移动用户服务可以在相应的边缘服务器上部署容器虚拟机，并制定容器迁移策略，以满足用户感知延迟和系统能耗之间的权衡；（2）我们进一步提出了一种基于这种多用户服务器迁移策略（DRLMSM）的深度强化学习算法（DRL），以有效实现快速决策；(3) 我们在软件定义网络 (SDN) 框架中构建架构，以验证 DRLMSM 的实用性和有效性。我们进行了广泛的实验，这表明我们的 DRLMSM 方案优于经典的强化学习 (RL) 算法和其他一些基线算法。(3) 我们在软件定义网络 (SDN) 框架中构建架构，以验证 DRLMSM 的实用性和有效性。我们进行了广泛的实验，这表明我们的 DRLMSM 方案优于经典的强化学习 (RL) 算法和其他一些基线算法。(3) 我们在软件定义网络 (SDN) 框架中构建架构，以验证 DRLMSM 的实用性和有效性。我们进行了广泛的实验，这表明我们的 DRLMSM 方案优于经典的强化学习 (RL) 算法和其他一些基线算法。