Virtual network mapping method and device based on graph convolution network

Abstract

The invention provides a virtual network mapping method and device based on a graph convolution network, and relates to the technical field of virtual networks, and the method comprises the steps: obtaining a mapping request of a virtual network, and determining a feature matrix of a physical node based on the mapping request and the attribute information of the physical network; inputting the feature matrix of the physical node into a target graph convolutional network to obtain a mapping probability of the physical node; sequentially performing virtual network mapping on all virtual nodes inthe virtual network according to a resource demand sequence based on the mapping probability of the physical nodes; and finally, if the node mapping and the link mapping in the virtual network mapping succeed, determining that the virtual network mapping succeeds. According to the method, the high-order space structure information of the physical nodes can be extracted by utilizing the target graph convolutional network, so that the virtual network mapping efficiency and the resource utilization rate of the physical network are improved.

Description

technical field [0001] The present invention relates to the field of virtual network technology, in particular to a virtual network mapping method and device based on graph convolutional network. Background technique [0002] Existing deep learning algorithms use the traditional deep learning models CNN and RNN to model the structure of the physical network, and extract several pieces of information about physical nodes, such as degrees and resource sizes, as local representations of physical nodes. The data used in these traditional deep learning models are all data in Euclidean space. The method of extracting several topological node information to represent the overall structure of the physical network will undoubtedly lose a lot of information for non-Euclidean data such as graphs. Therefore, the existing virtual network mapping method has the technical problems of low efficiency of virtual network mapping and low utilization of physical network resources. Contents of ...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a virtual network mapping method and device based on graph convolutional network, so as to alleviate the technical problems of low efficiency of virtual network mapping and low resource utilization of physical network existing in the prior art

Images