VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

Wang, Chao; Batth, Ranbir Singh; Zhang, Peiying; Aujla, Gagangeet Singh; Duan, Youxiang; Ren, Lihua

Abstract

The rapid development and deployment of network services has brought a series of challenges to researchers. On the one hand, the needs of Internet end users/applications reflect the characteristics of travel alienation, and they pursue different perspectives of service quality. On the other hand, with the explosive growth of information in the era of big data, a lot of private information is stored in the network. End users/applications naturally start to pay attention to network security. In order to solve the requirements of differentiated quality of service (QoS) and security, this paper proposes a virtual network embedding (VNE) algorithm based on deep reinforcement learning (DRL), aiming at the CPU, bandwidth, delay and security attributes of substrate network. DRL agent is trained in the network environment constructed by the above attributes. The purpose is to deduce the mapping probability of each substrate node and map the virtual node according to this probability. Finally, the breadth first strategy (BFS) is used to map the virtual links. In the experimental stage, the algorithm based on DRL is compared with other representative algorithms in three aspects: long term average revenue, long term revenue consumption ratio and acceptance rate. The results show that the algorithm proposed in this paper has achieved good experimental results, which proves that the algorithm can be effectively applied to solve the end user/application differentiated QoS and security requirements.

Citation

Wang, C., Batth, R. S., Zhang, P., Aujla, G. S., Duan, Y., & Ren, L. (2021). VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning. Computing, 103(6), 1061-1083. https://doi.org/10.1007/s00607-020-00883-w

• Journal Article Type: Article
• Acceptance Date: Dec 1, 2020
• Online Publication Date: Jan 21, 2021
• Publication Date: 2021-06
• Deposit Date: Apr 23, 2021
• Publicly Available Date: Jan 21, 2022
• Journal: Computing
• Print ISSN: 0010-485X
• Electronic ISSN: 1436-5057
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 103
• Issue: 6
• Pages: 1061-1083
• DOI: https://doi.org/10.1007/s00607-020-00883-w
• Public URL: https://durham-repository.worktribe.com/output/1249517