Network Function Migration in Softwarization Based Networks with Mobile Edge Computing

Abstract

Network Function Virtualization has been widely acknowledged as one of the fundamental technologies for 5G and beyond by consolidating network functions into general-purpose hardware. To support a specific type of service, a virtualized network topology, such as Service Function Chain, is constructed by logically connecting a set of virtual network functions (VNFs). Meanwhile, Mobile Edge Computing (MEC) provides cloud resources at the edge of networks, meeting the stringent service requirements of many emerging mobile applications. With the widespread of new compute-intensive and Internet of Things applications, the amount of service flows in edge networks is rapidly increasing, causing network congestion easily because of the sinking of the computing capabilities. Moreover, due to the limited coverage of edge servers and erratic user mobility, it is difficult to maintain satisfactory service performance. Therefore, in order to support diverse services with various Quality of Service requirements, an online dynamic VNF migration is imperative in mobile networks. In this paper, we investigate the NF migration in softwarization based mobile networks with MEC, with the aim to minimize the number of link congestion in the networks. We first formulate the link congestion problem as a multidimensional knapsack problem, which is proved NP-hard. Then we resort to deep reinforcement learning to solve the online migration problem. Numerical results show that the proposed migration strategy can significantly reduce the number of link congestion and end-to-end service delay in comparison with the state-of-the-art solutions.