An Attention Based Deep Reinforcement Learning Method for Virtual Network Function Placement

摘要

Network Function Virtualization (NFV) decouples network functions from the dedicated hardware and produces Virtual Network Functions (VNFs) in software. The VNFs are placed on hardware and are linked together to build a service chain. The design of an efficient VNF placement algorithm is crucial. The rapid development of machine learning, especially Deep Reinforcement Learning (Deep RL), allows us to address this problem. In this paper, we present an attention based sequence to sequence Deep RL method for VNF placement. Our approach is a policy based method optimized by REINFORCE with baseline. Our model receives physical hosts and service chain as input and produces the output sequence step by step with attention encoder and decoder. We demonstrate that our method outperforms the existing learning method and greedy heuristic.