Efficient flow migration for NFV with Graph-aware deep reinforcement learning

Network Function Virtualization (NFV) enables flexible deployment of network services as applications. However, it is a big challenge to guarantee the Quality of Service (QoS) under unpredictable network traffic while minimizing the processing resources. One typical solution is to realize NF scale-out, scale-in and load balancing by elastically migrating the related traffic flows. However, it is difficult to optimally migrate flows considering the resources and QoS constraints. In this paper, we propose DeepMigration to efficiently and dynamically migrate traffic flows among different NF instances. DeepMigration is a Deep Reinforcement Learning (DRL)-based solution coupled with Graph Neural Network (GNN). By taking advantages of the graph-based relationship deduction ability from our customized GNN and the self-evolution ability from the experience training of DRL, DeepMigration can accurately model the cost (e.g., migration latency) and the benefit (e.g., reducing the number of NF instances) of flow migration among different NF instances and employ dynamic and effective flow migration policies generated by the neural networks to improve the QoS. Experiment results show that DeepMigration reduces the migration latency and saves up to 71.6% of the computation time than the state-of-the-art.