Application of Virtual Network Embedding Algorithm Based on Variational Graph Autoencoder and K-means Clustering
Mapping virtual networks to physical networks is an important task in network functional virtualization.In order to effectively allocate virtual network requests,it is necessary to embed the virtual network into the physical network topology.However,due to the complexity of virtual networks and the limitations of physical networks,this task has become very challenging.In view of this,the study improves the existing Virtual Network Embedding(VNE)model by incorporating Variational Graph Auto Encoders(VGAE),and a new virtual network embedding algorithm model is proposed.The embedded features of the virtual network is extracted through an encoder.And then used the K-means clustering algorithm,the obtained embedded features is classfied,ultimately a suitable embedding allocation method is obtained.The experimental results show that the new model performs the best and has the best stability compared to other embedding algorithms of the same type.Its average embedding request acceptance rate is 60％,and the long-term average CPU resource utilization rate is as high as 97％.In summary,the new virtual network embedding algorithm proposed in the study performs well in resource utilization and embedding quality,and can effectively cope with complex network environments and large-scale virtual network requests.
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