FlexVNE: A LLM-Powered Flexible Framework for Virtual Network Embedding in Cloud-Network Convergence

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Abstract
In cloud-network convergence environments, Virtual Network Embedding (VNE) confronts three fundamental challenges: resources heterogeneity, services dynamicity and policies diversity. Traditional exact algorithms, (meta)heuristic methods and learning-based approaches are respectively con strained by computational complexity, limited generalization capability and data dependency, rendering them inadequate for addressing these challenges effectively. Although Large Language Models (LLMs) demonstrate remarkable capabilities in semantic understanding and reasoning, their direct application to VNE still suffers from difficulties in cross-modal coordination, insufficient solution reliability and the inability to quantify business intents. To address these limitations, we propose FLEXVNE, an LLM-driven flexible VNE framework that incorporates three key innovations: (i) a coordinated feature encoder that fuses network graph structure with natural language descriptions to precisely characterize heterogeneous resources; (ii) a VNE head equipped with dynamic masking and hierarchical pol icy networks, embedding hard constraints directly into the decision process to ensure feasible and reliable embedding solutions; (iii) an intent-driven policy controller that transforms ambiguous natural language requirements into optimizable objective weights, guiding policy adaptation to diverse business preference. We conduct comprehensive evaluations of FLEXVNE on a simulation platform. Experimental results demonstrate that across topologies of varying scales and constraint intensities, FLEXVNE significantly outperforms all baseline schemes across all evaluation metrics. Furthermore, FLEXVNE successfully accommodates various user-defined intents, offering a promising paradigm for building intelligent and adaptive cloud-network resource orchestration systems.
Funding Information
  • National Key Research and Development Program of China (2023YFB2903902)

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