Abstract
Service Function Chaining (SFC) is the process of decomposing a network service into multiple functions that successively process packets to deliver the end-to-end service. In a multi-domain context, SFC placement is a challenging problem due to limited knowledge of the infrastructure of the local domains, which complicates the process of finding the optimal placement solutions. On the other hand, Reinforcement Learning has gained momentum as a tool for decision-making, allowing agents to construct and improve policies using feedback from the environment. In this paper, we leverage Deep Reinforcement Learning (DRL) to perform SFC placement on multiple domains. We devise a hierarchical architecture where the local domain agents and the multi-domain agent are trained using different DRL models to perform SFC and sub-SFC placement while satisfying the SLA requirements.