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

Wang, Chao and Batth, Ranbir Singh and Zhang, Peiying and Aujla, Gagangeet Singh and Duan, Youxiang and Ren, Lihua (2021) 'VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning.', Computing., 103 (6). pp. 1061-1083.


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.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Publisher statement:This is a post-peer-review, pre-copyedit version of an article published in Computing. The final authenticated version is available online at:
Date accepted:01 December 2020
Date deposited:23 April 2021
Date of first online publication:21 January 2021
Date first made open access:21 January 2022

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