Digital Twin-Assisted VNF Migration Algorithm Based on Spatiotemporal Large Model Resource Demand Prediction

To address the issues of lagging virtual network function (VNF) migration caused by the dynamic changes in resource requirements in Industrial Internet of Things (IIoT) and the unsatisfactory prediction effect due to the neglect of the connection relationships between nodes in resource requirement prediction, a digital twin(DT)-assisted VNF migration algorithm with spatiotemporal large language model-based resource demand prediction is proposed. Firstly, a large language model-based resource prediction method that combines graph convolutional networks and multi-head self-attention mechanism is introduced to effectively capture spatio-temporal correlations and predict future resource requirements. Next, in order to ensure a deterministic quality of service (QoS) and optimize migration decisions, a DT-assisted VNF migration model composed of energy consumption, latency, and load balancing is constructed, and a joint optimization model for VNF migration and DT re-association aimed at maximizing the long-term utility of the system is established. Finally, a migration algorithm based on heterogeneous multi-agent proximal policy optimization is proposed to solve the VNF migration problem according to the resource requirements predicted by the model. To address the problem of excessive synchronization delays of DT nodes originally associated with nodes after migration, a counterfactual multi-agent algorithm is proposed to solve the problem of DT re-association. Simulation results show that the proposed algorithm improves prediction accuracy, ensures load balancing, and reduces system energy consumption and synchronization delays.