SFC Path Selection based on Combination of Topological Analysis and Demand Prediction

The 5th generation (5G) or beyond 5G (B5G) mobile networks offer a range of services over the existing infrastructure. Service function chaining (SFC) provides a platform for flexible resource management by dynamically allocating and adjusting resources to virtual network functions (VNFs). To satisfy the quality of service (QoS) requirements, the system requires the migration of VNFs from the current server to other servers that have sufficient resources. Previously, we discussed an artificial intelligence (AI)-based VNF migration scheduling mechanism for multiple service function (SF) chains. However, our previous approach focused only on SFC management in small-scale networks. To manage largescale networks, we must consider SFC path selection against interference among multiple SFC management Als. To avoid interference, in this study, we propose a supervisory mechanism to establish an SFC management system consisting of multiple Als. The supervisor determines the path recommendation ratio for each chain group governed by the management Als. The recommendation ratio is determined by the topological features and predicted resource demands of the chains. By using the recommendation ratio, SFC management may reduce the load of VNF-providing servers by approximately 20% compared with the case where no supervisor is used.