A Deep Q-Learning Based VNF Migration Strategy for Elastic Control in SDN/NFV Network

Network functions virtualization (NFV) solves the problem of tight coupling between network functions and hardware. While the Service Function Chaining (SFC) is the basic business model and provides customized services for network functions in NFV. However, the existing network function deployment and migration mechanisms are relatively static and rigid. For example, problems such as network load imbalance, deployment nodes or virtual network function instances failing, and virtual function elastic scaling cannot be performed. Aiming at the above problems, this paper proposes the decision-making optimization algorithm for virtual network function migration based on deep reinforcement learning. The algorithm takes network and node resources as constraints, establishes a migration decision model including state space, behavior space, and reward mechanism, and realizes flexible adaptation of virtual network functions. In the algorithm, migration decision-making based on Deep Q Network (DQN) includes offline training and online decision-making. In the offline training stage, DQN is used to train the strategy network model. The online migration stage is based on the trained strategy network model, which uses the current network state as the model input to dynamically generate the migration strategy. Finally, the network performs the migration of virtual network functions online. This algorithm optimizes the node load and solves the problem of service interruption caused by the excessive load of a single node, failure of links, and virtual function instances in the network. Finally, we build a practical prototype system and demonstrate the performance of the proposed DQN approach. According to the network status, the prototype system realizes the on-demand dynamic migration of virtual network functions. And the experimental results verify that the proposed elastic adaptation mechanism can effectively solve the migration problems, improve the quality of service, and enhance the scalability and flexibility of the network.