Virtual Network Embedding Algorithm via Diffusion Wavelet

The great success of the Internet has promoted the development of digital industries and increased the demand for communication bandwidth. For example, ultrahigh-definition videos and vehicle networks require fast bandwidth speed and increase network connection density, respectively. High-bandwidth and high-density parallel communication drive the rapid development of network virtualization and 5G/6G technology. In a network virtualization environment, this new demand also brings new link resource allocation difficulties in existing substrate networks. To solve this far-reaching problem, this paper proposes a virtual network embedding algorithm via diffusion wavelet (VNE_DW), which is an unsupervised structure learning algorithm. Through the diffusion wavelet, the topology structure of nodes, connection density, and link volume among the nodes are comprehensively evaluated. Nodes that facilitate the link mapping success rate are preferentially selected. Experimental results demonstrate that the mapping success rate and revenue-cost ratio of VNE_DW outperform other state-of-the-art algorithms with high bandwidth and density.