English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The issue of virtual network (VN) embedding constitutes an important aspect of network virtualization, which is considered to be one of the most crucial techniques to overcome the Internet ossification problem. The main purpose of VN embedding is to efficiently utilize the limited physical network resources to offer the supporting of virtual nodes and virtual links from the VNs. Due to the fact that the VN embedding problem is proved to be NP-hard, previous works have put forward some of heuristic algorithms to solve this VN embedding problem. However, most of the existing research works only consider the local resources of nodes, ignoring the topological attributes of its neighborhood nodes, and lead to lower resource utilization of the substrate network. To address this issue, we proposed an approach of VN embedding algorithm called VNE-DCC, which based on the node degree and the clustering coefficient information, we adopted the technique of node importance metric to rank the substrate nodes aim to select the node with the most embedding potential for every virtual node in each VN requests, and exploited the breadth-first-search algorithm to embed the virtual nodes aiming at reducing the resource utilization of substrate links so as to increase the acceptance ratio of VN requests and increase the revenues of operational providers. Extensive simulations have shown that the efficiency of our algorithm is better than the other state-of-the-art algorithms in terms of Revenue/Cost ratio and acceptance ratio.

Virtual Network Embedding Based on the Degree and Clustering Coefficient Information