Virtual Network Function Placement Based on Differentiated Weight Graph Convolutional Neural Network and Maximal Weight Matching

The intelligent service function chains (SFCs) provisioning is of great significance for agile deployments of 5G vertical applications. However, the heterogeneities of entities in substrate network (SNet) and SFCs hinder deep learning (DL) models to fully integrate the information of SNet and SFCs. Furthermore, the potential infeasibility of output policies and difficulty in training data acquisition also pose challenges to DL methods. To overcome the above limitations, we propose a Differentiated Weight Graph Convolutional Neural Network (DWGCN) model, which configures different weights for different kinds of entities, to predict the optimal virtual network function (VNF) placements. Moreover, the model is integrated with maximal weight matching to enhance the feasibility of VNF placement policies. A transfer learning method is further introduced to reduce the required training data with knowledge transfer. Experimental results demonstrate the effectiveness of the proposed methods in SFC mapping cost, high time efficiency, and knowledge transferability.