Deep Reinforcement Learning for Resource Demand Prediction and Virtual Function Network Migration in Digital Twin Network

The Internet of Things (IoT) enables intelligent services varying with the complex and realtime environment to achieve network benefits, where network function virtualization (NFV) can dynamically provide virtualized network functions (VNFs) for IoT devices. In the NFV-enabled IoT architecture, a service function chain (SFC) consists of an ordered set of VNFs. However, the energy consumption of the VNF migration and SFC reconfiguration is one major issue owing to the dynamic characteristic of the IoT network. In this article, we propose a new paradigm digital twin (DT) to create the virtual twin of physical objects in the IoT network, then, we formalize the problem as a mathematical model, which aims to minimize the energy consumption. To this end, we prove this problem is NP-hard and propose an algorithm bidirectional gated recurrent unit (Bi-GRU) based on federated learning to predict the resource requirement. Further more, according to the prediction result, which utilizing the deep reinforcement learning (DRL) algorithm for decision making of the VNF migration. Simulation results show that our proposed method can effectively reduce the number of VNFs to be migrated and economize the energy consumption of the DT IoT network.