VNF Migration in Digital Twin Network for NFV Environment

Abstract

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtual Network Functions (VNFs), adapting services to the complex and dynamic network environment to enhance network performance. However, VNF migration and energy consumption pose significant challenges due to the dynamic nature of the physical network. In order to maximize the acceptance rate of Service Function Chain Requests (SFCR), and reduce VNF migration and energy consumption as much as possible, we summarize several related factors such as the node hosting state, link hosting state, energy consumption, migrated nodes, and whether the mapping is successful. We define the Markov decision process by considering the factors mentioned above. Next, we design the VNF migration algorithm utilizing actor–critic models, graph convolution networks, and LSTM networks. In order to reduce the risk of trial and error during training and prediction in deep reinforcement learning scenarios, we designed a network architecture based on a digital twin (DT). In simulation experiments, compared with the FF algorithm that greedily selects the first available node, our AC_GCN algorithm significantly improves the acceptance rate of SFC requests by 2.9 times more than the FF algorithm in small topology experiments, and 27 times more than the FF algorithm in large topology experiments. Compared with the deep reinforcement learning (DRL) algorithm, which does not consider all the above factors together, for the small topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of request acceptance rate by 13%, underperforms compared to the DRL algorithm in terms of energy consumption by 3.8%, and underperforms compared to the DRL algorithm in terms of the number of migrated nodes for 22%; for the large topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of the request acceptance rate by 7.7%, outperforms the DRL algorithm in terms of energy consumption by 0.4%, and outperforms the DRL algorithm in terms of the number of migrated nodes by 1.6%.