On Using Deep Reinforcement Learning for VNF Forwarding Graphs Placement

The placement of Virtual Network Function - Forwarding Graphs (VNF-FGs) is one of the fundamental operations in the next generation networks. However, embedding a VNF -FG is a complicated process which has been proved as an NP-hard problem. In this demo, we leverage one of the most advanced approaches in Deep Reinforcement Learning (DRL) in order to efficiently solve the VNF - FG embedding problem. In particular, we have proposed an extension of this approach in order to better explore the space of solutions while making it safer. The experimental platform we have developed, which is based on Mininet and containers, allows us to clearly show the advantage of our approach over existing approaches.