Deep Reinforcement Learning Based Migration Mechanism for Service Function Chain in Operator Networks

Abstract

To improve the service experience provided by the operator network, this paper studies the online migration of Service Function Chain(SFC). Based on the Markov Decision Process(MDP), modeling analysis is performed on the migration of multiple Virtual Network Functions(VNF) in SFC. By combining reinforcement learning and deep neural networks, a double Deep Q-Network(double DQN) based service function chain migration mechanism is proposed. This method can make online migration decisions and avoid over-estimation. Experimental result shows that when compared with the fixed deployment algorithm and the greedy algorithm, the double DQN based SFC migration mechanism has obvious advantages in end-to-end delay and network system revenue, which can help the mobile operator to improve the quality of experience and the efficiency of resources usage.