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The rapid development and deployment of network services has brought a seriesof challenges to researchers. On the one hand, the needs of Internet endusers/applications reflect the characteristics of travel alienation, and theypursue different perspectives of service quality. On the other hand, with theexplosive growth of information in the era of big data, a lot of privateinformation is stored in the network. End users/applications naturally start topay attention to network security. In order to solve the requirements ofdifferentiated quality of service (QoS) and security, this paper proposes avirtual network embedding (VNE) algorithm based on deep reinforcement learning(DRL), aiming at the CPU, bandwidth, delay and security attributes of substratenetwork. DRL agent is trained in the network environment constructed by theabove attributes. The purpose is to deduce the mapping probability of eachsubstrate node and map the virtual node according to this probability. Finally,the breadth first strategy (BFS) is used to map the virtual links. In theexperimental stage, the algorithm based on DRL is compared with otherrepresentative algorithms in three aspects: long term average revenue, longterm revenue consumption ratio and acceptance rate. The results show that thealgorithm proposed in this paper has achieved good experimental results, whichproves that the algorithm can be effectively applied to solve the enduser/application differentiated QoS and security requirements.

VNE Solution for Network Differentiated QoS and Security Requirements: From the Perspective of Deep Reinforcement Learning