Details

Autor(en) / Beteiligte

• Xuan, Junlei
• Yang, Huifang
• Zhao, Xuelin
• Ma, Xingpo
• Yang, Xiaokai
• Xue, Xingsi
• Xingsi Xue

Titel

Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment

Ist Teil von

• Security and communication networks, 2021-10, Vol.2021, p.1-11

Ort / Verlag

London: Hindawi

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Network function virtualization (NFV) has the potential to lead to significant reductions in capital expenditure and can improve the flexibility of the network. Virtual network function (VNF) deployment problem will be one of key problems that need to be addressed in NFV. To solve the problem of routing and VNF deployment, an optimization model, which minimizes the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF, is established. In this optimization model, the dependency among the different VNFs is considered. In order to solve the service chain mapping problem of high dynamic virtual network, a new virtual network function service chain mapping algorithm PDQN-VNFSC was proposed by combining prediction algorithm and DQN (Deep Q-Network). Firstly, the real-time mapping of virtual network service chains is modeled into a partial observable Markov decision process. Then, the real-time mapping process of virtual network service chain is optimized by using global and long-term benefits. Finally, the service chain of virtual network function is mapped through the learning decision framework of offline learning and online deployment. The simulation results show that, compared with the existing algorithms, the proposed algorithm has a lower the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF.