[2511.01343] CNFP: Optimizing Cloud-Native Network Function Placement with Diffusion Models on the Cloud Continuum

Computer Science > Machine Learning arXiv:2511.01343 (cs) [Submitted on 3 Nov 2025 (v1), last revised 3 Mar 2026 (this version, v2)] Title:CNFP: Optimizing Cloud-Native Network Function Placement with Diffusion Models on the Cloud Continuum Authors:Álvaro Vázquez Rodríguez, Manuel Fernández-Veiga, Carlos Giraldo-Rodríguez View a PDF of the paper titled CNFP: Optimizing Cloud-Native Network Function Placement with Diffusion Models on the Cloud Continuum, by \'Alvaro V\'azquez Rodr\'iguez and 2 other authors View PDF HTML (experimental) Abstract:The placement of Cloud-Native Network Functions across the Cloud-Continuum represents a core challenge in the orchestration of current 5G and future 6G networks. The process entails the implementation of interdependent computing tasks, which are structured as Service Function Chains, over distributed cloud infrastructures. This is achieved while satisfying strict resource, bandwidth, connectivity, and end-to-end latency constraints. It is widely acknowledged that classical approaches, including mixed-integer (non)linear programming, heuristics, and reinforcement learning, face practical limitations in terms of scalability, robust constraint handling, and generalization to unseen network conditions. In this study, a diffusion-based theoretical and algorithmic framework for CNF placement is proposed, based on Denoising Diffusion Probabilistic Models. The placement process is reconceptualised as a conditional graph-to-assignment generat...