Capacitated Shortest Path Tour-Based Service Chaining Adaptive to Changes of Service Demand and Network Topology

To achieve sustainable networking, network service providers have expressed significant interest in employing automated network operations that integrate network functions virtualization (NFV), software-defined networking (SDN), and machine learning (ML). In the context of NFV/SDN, a certain network service is regarded as a sequence of virtual network functions (VNFs) forming a service chain. The service chaining (SC) problem aims at establishing an appropriate service path from an origin node to a destination node where the VNFs are executed at intermediate nodes in the required order under resource constraints on nodes and links. SDN enables programmable configurations on forwarding devices (i.e., switches and routers) for traffic forwarding between VNFs. In our previous work, we formulated the SC problem as an integer linear program (ILP) based on the capacitated shortest path tour problem (CSPTP), which is an extended version of SPTP with additional node and link capacity constraints. Furthermore, we developed Lagrangian heuristics to solve the problem by considering the balance between optimality and computational complexity. In this paper, we propose a deep reinforcement learning (DRL) framework coupled with the graph neural network (GNN) to realize CSPTP-based SC that adapts to changes of service demand and/or network topology. Numerical results show that the proposed framework achieves nearly optimal SC with higher learning speed compared to the conventional deep Q-Network based approach. Moreover, it performs well when confronted with variations in service demand and exhibits competitive performance compared to the ILP solutions across the majority of 243 real-world topologies.