Automatic Virtual Network Embedding Based on Deep Reinforcement Learning
Zhongxia Yan, Jingguo Ge, Yulei Wu, Hongbo Zheng, Liangxiong Li, Tong Li · 2019
The performance of virtual network embedding determines the effectiveness and efficiency of a virtualized network, making it a critical part of the network virtualization technology. However, most existing algorithms fail to provide automatic embedding solutions in an acceptable running time. In this paper, we combine reinforcement learning with a novel neural network structure and propose a new virtual network embedding algorithm. The proposed algorithm can learn to embed virtual networks automatically. Extensive simulation results show that our algorithm achieves the best performance on most metrics compared with the existing typical and stateof-the-art solutions.