5G网络切片场景中基于预测的虚拟网络功能动态部署算法

针对无线虚拟化网络在时间域上业务请求的动态变化和信息反馈时延导致虚拟资源分配的不合理，该文提出一种基于长短时记忆(LSTM)网络的流量感知算法，该算法通过服务功能链(SFC)的历史队列信息来预测未来负载状态。基于预测的结果，联合考虑虚拟网络功能(VNF)的调度问题和相应的计算资源分配问题，提出一种基于最大最小蚁群算法(MMACA)的虚拟网络功能动态部署方法，在满足未来队列不溢出的最低资源需求的前提下，采用按需分配的方式最大化计算资源利用率。仿真结果表明，该文提出的基于LSTM神经网络预测模型能够获得很好的预测效果，实现了网络的在线监测；基于MMACA的VNF部署方法有效降低了比特丢失率的同时也降低了整体VNF调度产生的平均端到端时延。

Virtual Network Function Dynamic Deployment Algorithm Based on Prediction for 5G Network Slicing

In order to solve the unreasonable virtual resource allocation caused by the dynamic change of service request and delay of information feedback in wireless virtualized network, a traffic-aware algorithm which exploits historical Service Function Chaining (SFC) queue information to predict future load state based on Long Short-Term Memory (LSTM) network is proposed. With the prediction results, the Virtual Network Function (VNF) deployment and the corresponding computing resource allocation problems are studied, and a VNFs’ deployment method based on Maximum and Minimum Ant Colony Algorithm (MMACA) is developed. On the premise of satisfying the minimum resource demand for future queue non-overflow, the on-demand allocation method is used to maximize the computing resource utilization. Simulation results show that the prediction model based on LSTM neural network in this paper obtains good prediction results and realizes online monitoring of the network. The Maximum and Minimum Ant Colony Algorithm based VNF deployment method reduces effectively the bit loss rate and the average end-to-end delay caused by overall VNFs’ scheduling at the same time.