Priority-awareness VNF migration method based on deep reinforcement learning

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 improve the reliability and Quality of Service (QoS) of network services, VNF migration provides an effective solution for this requirement. However, traditional migration methods lack an appropriate VNF selection mechanism and consider a single network state. Moreover, how to determine the accurate migration location dynamically with machine learning is challenging. This paper proposed a novel PAVM algorithm for reliable and optimal VNF migration, which distinguishes two kinds of VNF migration schemes for network congestion and node failure states respectively, and a node and VNF priority-awareness mechanism is designed, which can select appropriate VNF on suitable node to migrate. Moreover, PAVM utilizes deep reinforcement learning algorithm to choose target node location of VNF migration, which jointly utilizes delay, load balancing of network as feedback factors to optimize the QoS. The experimental results indicate that compared with other three benchmark algorithms, PAVM can effectively reduce the transmission delay and improve node and link load balancing after the VNF migration.