DT-Assisted VNF Migration in SDN/NVF-Enabled IoT Networks via Multiagent Deep Reinforcement Learning

Network function virtualization (NFV) and software-defined networking (SDN) provide high-quality services to users of the Internet of Things (IoT). However, dynamic changes in network traffic and service function chain (SFC) resource requirements may result in virtual network function (VNF) migration issue and real-time triggering of VNF migration can cause service delay issues in SDN/NVF-enabled IoT networks. In this article, we propose a digital twin (DT)-assisted VNF migration strategy to effectively address this issue. The digital twin of VNF (DT-VNF) is integrated with a multitask DT migration model based on bidirectional-gated recurrent units (DTMBi-GRUs) to achieve accurate resource demands prediction. Based on this, the VNF migration strategy is formulated in advance to avoid network performance degradation. We focus on the post-migration effects on services, networks, and DTs, so migration plans for DT-VNF and a reassociation scheme are formulated to enable real-time monitoring of post-migration VNF by DT-VNF. Then, an optimization problem is formulated to minimize average network energy consumption, network resource differences, and SDN synchronization delay in order to obtain optimal strategies. In addition, considering the problem’s complexity, it is decoupled into the VNF migration problem and the DT association and migration problem. The collaborative solution involves employing the multiagent proximal policy optimization (MAPPO) and asynchronous advantage actor–critic (A3C). Simulation results confirm the superiority of the proposed algorithms over baseline algorithms.