Towards AI-enabled Microservice Architecture for Network Function Virtualization

Abstract

Network Function Virtualization (NFV) enables operators to flexibly deploy network services on commodity servers in an on-demand and agile manner. This has recently attracted significant attention from industry and academia. However, there are important challenges for NFV deployment, including performance bottlenecks, degraded fault tolerance, upgrading complexities, and security threats. To overcome these challenges, the microservice approach, which has been applied successfully in cloud computing, has motivated the research communities to apply its principles in NFV domains. In this paper, we first compare the challenges of employing the microservices approach in both the cloud computing and the NFV domains, and then discuss the need for AI-enabled microservices architecture for NFV. Performance evaluation of the microservices approach in NFV is performed on bare-metal machine setups. The results compare the pros and cons of the microservice approach and show the need for AI to handle the complex decisions associated with decomposing network functions into microservices or vice versa. We also propose an AI-enabled microservice architecture and present its potential use cases for personalized live streaming, smart public safety, and enterprise VPN (Virtual Private Network). Open questions and future work are also presented.