Digital Twin Assisted VNF Migration Through Resource Prediction in SDN/NVF-Enabled IoT Networks

Network slicing (NS) enables flexible allocation of the Internet of Things (IoT) network resources through Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies. However, dynamic variations in network traffic and resource demands can lead to node overload and failures, necessitating timely virtual network function (VNF) migration to safeguard IoT business Quality of Service (QoS). Addressing the issue of deteriorating QoS due to untimely VNF migration, we propose a digital twin (DT)-assisted VNF migration strategy based on resource demand prediction. Firstly, a prediction model combining convolutional neural networks, gated recurrent units, and attention mechanisms is proposed to forecast VNF resource requirements. Secondly, the granularity of DT synchronization information is adjusted to resolve issues of delay and high cost during digital twin construction. Then, a VNF migration model is constructed to maximize DT utility while reducing network costs and average resource variance. Finally, a multiagent algorithm combining long short-term memory (LSTM) and double deep Q-network (DDQN) is proposed to perform VNF migration based on their priority-driven predicted future resource demands, and a multiagent algorithm based on dueling double deep Q-network (D3QN) and deep deterministic policy gradient (DDPG) is proposed to address the DT association problem with a mixed action space. The simulation results demonstrate that the proposed algorithm can reduce the synchronization latency of the DT, the violation rate of service level agreements, and the service outage time rate, while improving the network load balancing capability.