Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing - Julong LAN; Di ZHU; Dan LI (Tongxin xuebao)
Objectives:With the emergence and development of new network structures such as polymorphic network,the demand for network resource capacity is becoming more and more diverse.It is very difficult to adjust the distribution of virtual network functions (VNFs) carried on network slices for on-demand,dynamic and efficient resource capacity matching.This paper uses a data-driven VNF resource capacity prediction method to explore the pre-deployment of VNFs carried on network slices. Methods: Using the data-driven idea, a VNF resource capacity prediction method based on spatiotemporal feature extraction was adopted to pre-deploy VNFs for the upcoming slicing demand through resource capacity prediction. First, the time series of data stream used for prediction is subjected to two-stage weighting processing,and then the processed time series and its dependent spatial topology information are input into the network model for spatiotemporal feature extraction. For the extraction of spatial features, by given an adjacency matrix and a feature matrix,graph convolutional network is used to reorganize the spatial distribution features of time series in the Fourier domain. For the extraction of temporal features, the temporal dependencies of the input data are perceived through the information transfer between the units via gated recurrent units.Then,based on the mapping relationship between the data flow sequence and the number of VNF instances, the feedforward neural network performs data dimension transformation and finally outputs the VNF resource demand prediction results. Results: From the experimental results, the prediction performance of this method is mainly reflected in the following aspects:1.The prediction accuracy of this method is stable.The primary reason is that the method adopts a reasonable spatiotemporal feature extraction structure, which can effectively deal with both the spatial structure with non-Euclidean features and the time series features with contextual dependencies.The secondary reason is that the weighted preprocessing process of the input data stream sequence in this method effectively avoids the characteristic mutation caused by the burst flow in the network,and can truly reflect the changing trend of the data stream and slicing capacity requirements, thus obtaining stable prediction effect. 2. The method has the ability of spatiotemporal prediction. It shows that after a large amount of data training, the spatial feature extraction layer of the method can quickly calculate the topology relationship and data flow distribution of the network, and the temporal feature extraction layer can assist in predicting the sudden changes may occur in the data flow according to the potential correlation between data flows between nodes. The two feature extraction layers work in coordination to obtain accurate spatiotemporal prediction results.3.The method has the ability to convert data flow prediction results. It shows that this method can combine the mapping relationship between the fluctuation trend of data flow and the change trend of the number of VNF instances, and efficiently realizes the conversion of data flow prediction and network slice capacity prediction through the transformation of data dimension. Conclusions: With the vigorous development of artificial intelligence technology, polymorphic network has given computing, storage and transmission capabilities to virtual nodes on network slices,and has been able to realize the dual improvement of network resource utilization and user experience through the adaptive flow of business demand data on the basis of autonomously sensing data throughput and autonomously predicting the resource requirements of VNFs on nodes. With the help of machine learning algorithms, the VNF resource capacity demand prediction method VNFPre proposed for polymorphic network scenarios,it can judge the future VNF resource capacity demand of network slices, and provide a priori information for the placement and mapping of VNFs carried by network slices.