A DRL Approach with Network Service Deployment Transformer for Reliable SFC Deployment

To provide dedicated protection in Network Function Virtualization (NFV), a reliability-aware Service Function Chain (SFC) can be deployed using two disjoint paths: a primary path and a backup path. In the event of network failures along the primary path, the working traffic is switched to the backup path to maintain service continuity. The process of accommodating reliability-aware SFCs is commonly referred to as SFC Deployment and Protection (SFCDP), which is proven to be NP-hard. In this work, we introduce a novel Network Service Deployment (NSD) transformer that can effectively incorporate and utilize fine-grained information regarding the network re-sources and the SFC requests. We develop a deep reinforcement learning framework based on NSD transformer (DRL-NSD) to effectively optimize the process of SFCDP. We conduct extensive simulations to validate the NSD transformer and compare the proposed NSD transformer with two benchmark neural network architectures. Our experimental results demonstrate that the NSD transformer outperforms the benchmarks across a variety of network topologies and network load settings.