Reinforcement learning-based virtual network embedding: A comprehensive survey

Abstract

Virtual network embedding plays a vital role in network virtualization, as it determines the deployment and connection of virtual networks to the physical network in the 5G and beyond. An efficient virtual network embedding algorithm is essential to ensure that virtual networks are embedded in a way that meets the performance, security, and resource requirements of the virtual networks and their users. The integration of reinforcement learning with virtual network embedding can lead to more intelligent and efficient network management, which can enhance the performance of large-scale networked systems. Reinforcement learning has the potential to improve and overcome some limitations of traditional algorithms, such as the need for prior knowledge of network conditions and the difficulty in dealing with non-linear and dynamic network environments. Therefore, we conducted this survey to provide a comprehensive overview and examine potential future directions for the optimal reinforcement learning-based virtual network embedding solutions. However, applying reinforcement learning directly to virtual network embedding is a challenging task that requires further research and study. Additionally, it encourages researchers to examine the potential of reinforcement learning in virtual network embedding, identify the challenges for its application, and cover various factors related to the reinforcement learning application in virtual network embedding, including motivations, performance metrics, and challenges.

Keywords

• Virtual network embedding
• Deep reinforcement learning
• Graph neural network
• Artificial intelligence