An Improved Adaptive Service Function Chain Mapping Method Based on Deep Reinforcement Learning

With the vigorous development of the network functions virtualization (NFV), service function chain (SFC) resource management, which aims to provide users with diversified customized services of network functions, has gradually become a research hotspot. Usually, the network service desired by the user is randomness and timeliness, and the formed service function chain request (SFCR) is dynamic and real-time, which requires that the SFC mapping can be adaptive to satisfy dynamically changing user requests. In this regard, this paper proposes an improved adaptive SFC mapping method based on deep reinforcement learning (ISM-DRL). Firstly, an improved SFC request mapping model is proposed to abstract the SFC mapping process and decompose the SFC mapping problem into the SFCR mapping problem and the VNF reorchestration problem. Secondly, we use the deep deterministic policy gradient (DDPG), which is a deep learning framework, to jointly optimize the effective service cost rate and mapping rate to approximate the optimal mapping strategy for the current network. Then, we design four VNF orchestration strategies based on the VNF request rate and mapping rate, etc., to enhance the matching degree of the ISM-DRL method for different networks. Finally, the results show that the method proposed in this paper can realize SFC mapping processing under dynamic request. Under different experimental conditions, the ISM-DRL method performs better than the DDDPG and DQN methods in terms of average effective cost utilisation and average mapping rate.