Modeling On Virtual Network Embedding Using Reinforcement Learning

It is well known that virtual network (VN) embedding (VNE) aims to solve how to efficiently allocate physical resources to a VN. However, this issue has been proved to be an NP-hard problem. Besides, as most of the existing approaches are based on heuristic algorithms, which is easy to fall into local optimal. To address the challenge, we formalize the problem as a mixed integer programming problem and propose a novel VNE method based on reinforcement learning in this article. And to solve the problem, we introduce a pointer network to generate virtual node mapping strategies through an attention mechanism, and design a reward function related to link resource consumption to build the connection between node mapping and link mapping stages of VNE. In addition, we present a policy gradient optimization mechanism to leverage the reward information obtained from the sampled solutions, and design an active search based process to automatically update the parameters of the neural network and to obtain near-optimal embedding solution. The experimental results show that the proposed method can improve the performance in average physical node utilization and long-term revenue to cost ratio comparing than that of the existing models.