A Model for Traffic Forwarding through Service Function Chaining using Deep Reinforcement Learning Techniques

Abstract

The development of new communication networks to offer innovative services has increased the volume of data. With the introduction of Deep Reinforcement Learning and Service Function Chaining architecture, new research opportunities have emerged to propose solutions to the new challenges. This work proposes a model through computational simulations how these techniques can be applied. The model was evaluated using two variations of the Deep Q-Network algorithm over the CIC-Darknet dataset. Results showed that both variations are a promising mechanism to make the networks more autonomous and intelligent. to demonstrate