The traditional Internet has encountered a bottleneck in allocating network resources for emerging technology needs. Network virtualization (NV) technology as a future network architecture, the virtual network embedding (VNE) algorithm it supports shows great potential in solving resource allocation problems. Combined with the efficient machine learning (ML) algorithm, a neural network model close to the substrate network environment is constructed to train the reinforcement learning agent. This paper proposes a two-stage VNE algorithm based on deep reinforcement learning (DRL) (TS-DRL-VNE) for the problem that the mapping result of existing heuristic algorithm is easy to converge to the local optimal solution. For the problem that the existing VNE algorithm based on ML often ignores the importance of substrate network representation and training mode, a DRL VNE algorithm based on full attribute matrix (FAM-DRL-VNE) is proposed. In view of the problem that the existing VNE algorithm often ignores the underlying resource changes between virtual network requests, a DRL VNE algorithm based on matrix perturbation theory (MPT-DRL-VNE) is proposed. Experimental results show that the above algorithm is superior to other algorithms.

中文翻译：

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基于深度强化学习的网络资源分配策略

传统Internet在为新兴技术需求分配网络资源时遇到了瓶颈。网络虚拟化（NV）技术作为未来的网络体系结构，它支持的虚拟网络嵌入（VNE）算法显示出解决资源分配问题的巨大潜力。结合有效的机器学习（ML）算法，构建了一个与基材网络环境接近的神经网络模型来训练强化学习代理。针对现有启发式算法的映射结果易于收敛到局部最优解的问题，提出了一种基于深度强化学习（DRL）的两阶段VNE算法。针对现有基于ML的VNE算法经常忽略衬底网络表示和训练模式的重要性的问题，提出了一种基于全属性矩阵的DRL VNE算法（FAM-DRL-VNE）。针对现有的VNE算法经常忽略虚拟网络请求之间的底层资源变化的问题，提出了一种基于矩阵扰动理论的DRL VNE算法（MPT-DRL-VNE）。实验结果表明，该算法优于其他算法。