基于集成深度神经网络流量预测的动态网络切片迁移算法

针对5G网络切片(NS)场景下由于缺乏提前对物理网络资源进行感知而导致切片迁移滞后的问题，该文提出一种基于集成深度神经网络流量预测的动态切片调整和迁移算法(DSAM)。首先建立了基于计算、内存、带宽资源配置的网络总惩罚模型；其次，提出基于集成深度神经网络的流量预测算法预测未来网络流量情况，并根据流量类型的不同将其转换成对未来时刻物理网络的资源占用及切片的资源需求感知；最后，根据感知结果，以尽可能大地降低运营商惩罚为目标，通过动态切片调整和迁移策略将虚拟网络功能(VNF)和虚拟链路迁移到满足资源限制的物理节点和链路上。仿真结果表明，所提算法有效提高了切片迁移的效率和网络资源利用率。

Dynamic Network Slice Migration Algorithm Based on Ensemble Deep Neural Network Traffic Prediction

In order to solve the problem that slice migration lags behind by lacking awareness of physical network resources in 5G Network Slice (NS) scenarios, a Dynamic Slice Adjustment and Migration (DSAM) algorithm based on ensemble deep neural network traffic prediction is proposed. Firstly, a network total penalty model based on computing, memory and bandwidth resource allocation is established. Secondly, in order to predict the future traffic situation, a prediction algorithm based on ensemble deep neural network is proposed. Then the result of prediction are converted to perception of the physical network resource usage and resource requirements of slice in future according to the different types of traffic. Finally, in order to as large as possible to reduce operators punishment according to the result of perception, Virtual Network Functions (VNF) and virtual links are migrated to physical nodes and links that meet resource limits through dynamic slice adjustment and migration policies. The simulation results show that the proposed algorithm improves effectively the efficiency of slice migration and utilization of network resources.