Digital-Twin-Assisted VNF Mapping and Scheduling in SDN/NFV-Enabled Industrial IoT
Lun Tang, Wen Wen, Jinyu Li, Dongxu Fang, Li Li, Qianbin Chen
Abstract
Network function virtualization (NFV) and software defined network (SDN) technologies enable flexible traffic scheduling and improve the efficiency of physical resources. However, for latency-sensitive Industrial Internet of Things(IIoT) services in Industry 4.0 and beyond, the inability of the SDN controller to synchronize the resource demand information of virtual network functions (VNFs) in a timely manner can lead to delays in VNF mapping and scheduling strategies. To address this issue, we propose a digital twin-assisted VNF mapping and scheduling algorithm that combines digital twin to assist the SDN controller in collecting data of VNFs. Firstly, we designed a digital twin-assisted and SDN/NFV-based network slicing architecture. Secondly, to reduce the synchronization delays of digital twins of VNFs, we propose a twin service node reassociation mechanism. Next, a digital twin-assisted VNF mapping and scheduling model is constructed under constraints such as CPU, storage, bandwidth resources, and quality of service (QoS) to maximize the service provider’s profit. Finally, we propose digital twin-assisted VNF mapping and scheduling algorithms based on greedy and tabu search to solve the problem. Leveraging digital twins, the SDN controller can obtain accurate resource demand information of VNFs, thereby solving the delay issue in VNF mapping and scheduling strategies. Simulation results indicate that the proposed algorithms yield favorable outcomes in terms of total profit, network service acceptance rate, average system delay of digital twins, and QoS satisfaction.