On Combining Reinforcement Learning and Monte Carlo for Dynamic Virtual Network Embedding

Demo paperInternational audienceNetwork slicing is one of the key building blocks in the evolution towards "zero touch networks". Indeed, this will allow 5G and beyond 5G networks to deploy services dynamically, on the same substrate network, regardless of their constraints. In this demo, we introduced a platform for dynamic virtual network embedding, a problem class known to be NP-hard. The proposed solution is based on a combination of a deep reinforcement learning strategy and a Monte Carlo (MC) approach. The idea here is to learn to generate, using a Deep Q-Network (DQN), a distribution of the placement solution, on which a MC-based search technique is applied. This makes the agent's exploration of the solution space more efficient