A novel reinforcement learning algorithm for virtual network embedding

作者：Yao, Haipeng^*; Chen, Xu; Li, Maozhen; Zhang, Peiying; Wang, Luyao

来源：Neurocomputing, 2018, 284: 1-9.

DOI：10.1016/j.neucom.2018.01.025

• Virtual network embedding
• Reinforcement learning
• Policy network
• Policy gradient

Network virtualization enables the share of a physical network among multiple virtual networks. Virtual network embedding determines the effectiveness of utilization of network resources. Traditional heuristic mapping algorithms follow static procedures, thus cannot be optimized automatically, leading to suboptimal ranking and embedding decisions. To solve this problem, we introduce a reinforcement learning method to virtual network embedding. In this paper, we design and implement a policy network based on reinforcement learning to make node mapping decisions. We use policy gradient to achieve optimization automatically by training the policy network with the historical data based on virtual network requests. To the best of our knowledge, this work is the first to utilize historical requests data to optimize network embedding automatically. The performance of the proposed embedding algorithm is evaluated in comparison with two other algorithms which use artificial rules based on node ranking. Simulation results show that our reinforcement learning is able to learn from historical requests and outperforms the other two embedding algorithms.

• 出版日期2018-4-5
• 单位北京工业大学; 网络与交换技术国家重点实验室; 北京邮电大学