Q-learning based Service Function Chaining using VNF Resource-aware Reward Model

Abstract

With the advent of the 5G network era, it is required to flexibly build and manage networks to meet rapidly changing service requirements. Software-defined networking (SDN) and network function virtualization (NFV) are key technologies that enable flexible network management by transforming networks into software-based networks. Besides, NFV has the advantage of virtualizing network functions, operating those on commercial (COTS) servers, and managing the network functions dynamically. However, the numerous virtual networks and resources created by NFV can cause problems complicating network management. To solve the problems, research on managing complex NFV environments using artificial intelligence (AI) has recently attracted attention. In particular, service function chaining (SFC) is one of the essential NFV technologies, and it is required to create an efficient SFC path in dynamic networks. In this paper, we propose a method of finding optimal SFC path considering the resource utilization of virtual network function (VNF) and VNF placement by using Q-learning, one of the reinforcement learning (RL) algorithms.