A VNF sharing method based on node selection probability using reinforcement learning in air-ground network

Abstract

By decoupling the software function on hardware devices, Network Function Virtualization(NFV) provides a new service architecture named Service Function Chain(SFC), which combines multiple Virtual Network Functions(VNFs) in a specific order. In order to reduce network resources consumption and improve the resource utilization, VNF sharing provides an effective solution for this requirement. However, traditional sharing methods lack a dynamic processing mechanism to select the deployment and shared node location according to the network state dynamically. Moreover, how to further optimize the utilization of network resources is challenging. This paper proposed a VNF sharing evaluation mechanism to evaluate and decide whether to share a VNF, then a node priority calculation mechanism was designed and mapped on node selection probability, which can select appropriate VNF to deploy or share VNF according to network state and resource requirements of SFC, finally, a reinforcement learning approach was utilized to update the selection probability of nodes and complete the VNF sharing process in air-ground network. The experimental results indicate that compared with other five benchmark algorithms, the proposed algorithm can reduce the transmission delay effectively, at the same time, it can improve node and link load resource utilization and acceptance rate of SFC after the VNF sharing.