Deep Reinforcement Learning-Based End-to-End Network Slicing Deployment

Network slicing promotes the development of different industries by dividing multiple logical networks on the same physical network to provide the customized services. However, the complex environment of network slicing deployment makes it difficult to obtain the accurate mathematical models, so that the traditional rule-based heuristic algorithms are difficult to process them efficiently. Therefore, we combine the Graph Convolutional Networks (GCN) and the Deep Deterministic Policy Gradient (DDPG) algorithms and propose the GCN-DDPG (G-DDPG) algorithm in this paper to solve the end-to-end network slicing deployment problem, while taking into account the constraints of Virtual Network Function (VNF) placement, VNF sharing, tolerable latency, and node and link resources limitations. First, the end-to-end network slicing deployment optimization is formulated as a problem of maximizing the weighted sum of system resource utilization and acceptance rate. Second, the physical network features extracted by GCN are combined with the state information of end-to-end network slicing requests as the state space of the optimization problem, and a G-DDPG algorithm is proposed to solve it. Finally, the simulation results demonstrate that our proposed method superior to benchmark solutions in terms of the resource utilization of the system, acceptance rate of end-to-end network slicing.