Using meta-reinforcement learning for solving the Virtual Network Embedding Problem
Sahand Torkamani-Azar, Mohsen Jahanshahi, Alireza Hedayati · Engineering Applications of Artificial Intelligence · 2025
Introduction of Network Function Virtualization (NFV), a technique for virtualization of infrastructure on physical servers, requires careful, efficient management of substrate network resources. This has resulted in formulation of Virtual Network Embedding Problem (VNEP) for efficient deployment of virtual network functions in the substrate network. While the holder of a substrate network earns the highest revenue as the acceptance rate of customers’ embedding requests increases, they should optimize the costs for embedding the required resources. Techniques employed so far for solving VNEP often suffer from poor design, long runtime, and the need for large training sets and maintaining precision in large-scale environments. In this work, we introduce Fast Context Adaptation via Meta-Learning Virtual Network Embedding Problem (CAVIA-VNEP), a technique based on meta-reinforcement learning (MRL) as a fusion of meta-learning – capable of learning to learn, and reinforcement learning (RL). We train RL at a high computational speed with a small amount of data while using CAVIA for efficient training of neural networks . Simulation results demonstrate that CAVIA-VNEP outperforms three baseline, heuristic, and RL-based techniques in terms of long-term revenue ratio, long-term revenue-to-cost ratio, long-term acceptance ratio, and utilization of substrate nodes and links while facing challenges for reducing the average path length.