论文标题
基于深入强化学习的网络资源分配策略
Network Resource Allocation Strategy Based on Deep Reinforcement Learning
论文作者
论文摘要
传统的互联网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）技术作为未来网络体系结构，它支持的虚拟网络嵌入（VNE）算法在解决资源分配问题方面具有巨大潜力。结合有效的机器学习（ML）算法，构建了接近基板网络环境的神经网络模型，以训练增强剂学习代理。本文提出了一种基于深钢筋学习（DRL）（TS-DRL-VNE）的两阶段VNE算法，即有关现有启发式算法的映射结果易于收敛到局部最佳解决方案的问题。对于基于ML的现有VNE算法通常忽略了基板网络表示和训练模式的重要性，提出了基于完整属性矩阵（FAM-DRL-VNE）的DRL VNE算法的重要性。考虑到现有VNE算法通常忽略虚拟网络请求之间的基本资源变化的问题，提出了基于矩阵扰动理论（MPT-DRL-VNE）的DRL VNE算法。实验结果表明，上述算法优于其他算法。
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.
