Network function virtualization (NFV) enables flexible service deployment by implementing network functions as software, with service function chains (SFCs) linking virtual network functions (VNFs) in a specific order to deliver end-to-end services. However, ensuring SFC resilience against large-scale disasters that can disrupt entire disaster zones (DZs) remains a significant challenge. In this paper, we study the multipath disaster-resilient SFC deployment problem, aiming to minimize the total bandwidth and computing resource overhead by jointly optimizing VNF placement, multipath routing, and protection mechanisms, subject to DZ-disjoint constraints. We formulate this problem as a Mixed-Integer Nonlinear Programming (MINLP) model and prove it to be NP-hard. To solve it efficiently, we propose a two-stage adaptive deployment approach; the first stage employs heuristic rules to generate a set of candidate paths satisfying DZ-disjoint constraints, and the second stage leverages deep reinforcement learning to intelligently place VNFs along these candidate paths, approximating the global optimum. Simulation results on real network topologies demonstrate that, compared to traditional dedicated protection strategies and a state-of-the-art exact algorithm, the proposed approach reduces resource overhead by up to 20% while effectively guaranteeing SFC disaster resilience, exhibiting good scalability and online deployment potential.
Yun et al. (Thu,) studied this question.