CORA: Feasibility-Safe Learned Refinement for Virtual Network Function Migration and Reconfiguration

Network function virtualization (NFV) delivers network services through service function chains (SFCs) composed of virtual network functions (VNFs). When a physical machine fails or becomes overloaded, the affected VNF instances must be migrated while satisfying CPU and memory capacities, link bandwidth, processing stability, end-to-end SFC delay, and utilization thresholds. Existing heuristic and learned constructors typically make one-shot deployment decisions, which may produce feasible yet locally inefficient placements or fail to fully remove the triggering overload. We introduce Constraint-aware Optimization with Reconfiguration Actions (CORA), a start-provider-agnostic learned post-optimizer for VNF reconfiguration. CORA first applies bounded repair and admits only complete deployments that become strictly feasible. It then represents the deployment as a heterogeneous PM–VNFI–SFC graph and searches over move and swap actions filtered by exact constraint checks. Every searched state remains feasible, while incumbent preservation guarantees that the returned deployment is no worse than the repaired start. On 30 frozen fat-tree-8 test instances spanning three migration regimes, CORA improves 20/29 (69.0%) paired feasible MSH-OR starts and 26/27 (96.3%) paired feasible MAT-GNN starts, with no paired degradation. The corresponding mean changes in the joint delay–load objective are -0.00253 and -0.01158, with both problem-seed bootstrap 95% confidence intervals lying strictly below zero.