Novel applications such as the Metaverse have highlighted the potential of beyond 5G networks, which necessitate ultra-low latency communications and massive broadband connections. Moreover, the burgeoning demand for such services with ever-fluctuating users has engendered a need for heightened service continuity consideration in B5G. To enable these services, the edge-cloud paradigm is a potential solution to harness cloud capacity and effectively manage users in real time as they move across the network. However, edge-cloud networks confront a multitude of limitations, including networking and computing resources that must be collectively managed to unlock their full potential. This paper addresses the joint problem of service placement and resource allocation in a network-cloud integrated environment while considering capacity constraints, dynamic users, and end-to-end delays. We present a non-linear programming model that formulates the optimization problem with the aiming objective of minimizing overall cost while enhancing latency. Next, to address the problem, we introduce a DDQL-based technique using RNNs to predict user behavior, empowered by a water-filling-based algorithm for service placement. The proposed framework adeptly accommodates the dynamic nature of users, the placement of services that mandate ultra-low latency in B5G, and service continuity when users migrate from one location to another. Simulation results show that our solution provides timely responses that optimize the network's potential, offering a scalable and efficient placement.

翻译：诸如元宇宙等新型应用凸显了超5G网络的潜力，这类网络要求超低延迟通信和大规模宽带连接。此外，由于用户需求的持续波动，超5G网络中对服务连续性保障的需求日益迫切。为支撑这些服务，边缘-云范式通过利用云容量并实时管理移动用户，成为潜在解决方案。然而，边缘-云网络面临诸多限制，包括需协同管理的网络与计算资源，以释放其全部潜力。本文解决了云网融合环境下服务部署与资源分配的联合问题，同时考虑容量约束、动态用户及端到端延迟。我们提出一个非线性规划模型，以最小化总成本并降低延迟为目标来形式化优化问题。随后，为求解该问题，引入一种基于深度双Q网络（DDQL）的技术，利用循环神经网络（RNN）预测用户行为，并采用注水算法进行服务部署。所提框架能够灵活适应动态用户特性、满足超5G中超低延迟服务部署需求，并在用户迁移时保障服务连续性。仿真结果表明，该方案能提供及时响应以优化网络潜能，实现可扩展且高效的服务部署。