On using Deep Reinforcement Learning for Multi-Domain SFC placement

Abstract

Service Function Chaining (SFC) has emerged as a promising technology for 5G and beyond. It leverages Network Function Virtualization (NFV) and Software Defined Networking (SDN) and allows the decomposition of a given service into a set of blocks that successively process data. The SFC placement issue has been extensively studied in the literature, and different solutions have been proposed using mathematical models and heuristics. More recently, Reinforcement Learning (RL) has emerged as a tool for decision-making that allows agents to elaborate policies based on the environment's feedback. In this paper, we study the benefits of using Deep Reinforcement Learning methods for the multi-domain SFC placement problem. We propose a Deep Deterministic Policy Gradient (DDPG) approach, where Linear Physical Programming is employed to generate rewards that reflect the solution's quality in terms of cost and latency. Through our experiments, we are able to demonstrate the efficiency of our approach with results that satisfy the SLA requirements.