智能电网中两阶段网络切片资源分配技术

为满足网络切片在智能电网中的多样化需求，提出了一个在智能电网中基于云-边协同的切片资源分配模型。为优化网络切片分配，提出一种两阶段的切片分配模型：在第一阶段中，以用户体验最优为目标，建立了本地边缘网络的资源分配问题的优化模型，并采用拉格朗日乘子法对此最优问题进行了求解；在第二阶段中，首先将网络切片资源分配系统建模成Markov决策过程，然后提出使用深度增强学习方法对核心云的切片自适应地进行资源分配。实验结果表明所提的两阶段切片资源优化分配模型可有效减少网络延迟，提高用户满意度。

Two-phase resource allocation technology for network slices in smart grid

To satisfy the diverse demands of network slicing in smart grid, a slicing resource allocation model based on cloud-edge collaboration in smart grid was proposed. Furthermore, a two-phase cooperative slice allocation model was developed to optimize the allocation of the network slices. In the first phase, an optimization model for the resource allocation in local edge network was established to optimize the user experience, and the optimization problem was solved with the Lagrange multiplier method. In the second phase, the system was modeled as a Markov decision process, and then the deep reinforcement learning was adopted to adaptively allocate the resources to the slices of the core cloud. Experimental results show that the proposed two-phrase slice resource allocation model can effectively reduce the network delay and improve the user satisfaction.