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

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.

Enhancing 5G Network Slicing: Slice Isolation Via Actor-Critic Reinforcement Learning with Optimal Graph Features

“…PyTorch is used for implementing and training the RL models. We compared the performance of SIRL against nine existing RL models, namely CDRL [13], RDAM [14], VNEQS [16], MLRL [17], DRLVNE [18], GCNNRL [19], A3CGCN [20], DeepViNE [21], PNVNE [22], and A2CRL [23]. Since we focus on the features and their ability to model the environment, we considered the same reward function for all the simulated models to remove the impact of the rewards on the model's performance.…”

“…Jiang and Zhang [16] proposed an RL model that considers each virtual node's security level in solving the VNE problem. We call this model VNEQS, short for VNE for Quality of Service and Security.…”

Reinforcement Learning-Based Slice Isolation Against DDoS Attacks in Beyond 5G Networks

“…Jiang and Zhang [20] proposed a RL model that considers both quality of service and the security level of each VN in the process of solving the VNE problem. We call this model VNEQS, short for Virtual Network Embedding for Quality of service and Security.…”

“…Then, we have compared the performance of SIRL against nine of the existing RL models, which are CDRL [17], RDAM [18], VNEQS [20], MLRL [21], DRLVNE [22], GCNNRL [23], A3CGCN [24], DeepViNE [25], PNVNE [26], and A2CRL [27]. Since our focus is on the features and their ability to model the environment, we have considered the same reward function for all the simulated models to remove the impact of the rewards on the model's performance.…”