Abstract
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.
Highlights
Network virtualization is a promising network architecture that can effectively solve network impasse problems [1]
This architecture is a prerequisite for network slicing, which provides an opportunity for Internet service providers (ISPs) to integrate their devices with standardized high-capacity components [2]
We analyze the following five aspects to improve the overall performance of the Virtual network embedding (VNE) using structural features of the nodes
Summary
Network virtualization is a promising network architecture that can effectively solve network impasse problems [1]. The existing VNE algorithm deploys virtual nodes through topology and resource attributes without considering structural characteristics, link volume, and connection density among the nodes. Our paper considers structural features, communication capability, and connection density among nodes and focuses on optimizing the scheme of node mapping to facilitate the success rate of link mapping. To analyze structural features, such as connection density, link volume, and closeness among nodes, a virtual network embedding algorithm via diffusion wavelet (VNE_DW) is proposed, which is inspired by spectral theory that treats the diffusion wavelet as the main idea [14], [15]. The algorithm focuses on improving the success rate of link mapping by optimizing the mapping scheme of nodes and obtaining affluent revenue.
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