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Entropy 2018, 20(5), 315; https://doi.org/10.3390/e20050315

Virtual Network Embedding Based on Graph Entropy

1
School of Information, Yunnan University of Finance and Economics, Kunming 650221, China
2
School of Continuing Education, Yunnan University of Finance and Economics, Kunming 650221, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 25 January 2018 / Revised: 8 April 2018 / Accepted: 21 April 2018 / Published: 25 April 2018
(This article belongs to the Special Issue Graph and Network Entropies)
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Abstract

For embedding virtual networks into a large scale substrate network, a massive amount of time is needed to search the resource space even if the scale of the virtual network is small. The complexity of searching the candidate resource will be reduced if candidates in substrate network can be located in a group of particularly matched areas, in which the resource distribution and communication structure of the substrate network exhibit a maximal similarity with the objective virtual network. This work proposes to discover the optimally suitable resource in a substrate network corresponding to the objective virtual network through comparison of their graph entropies. Aiming for this, the substrate network is divided into substructures referring to the importance of nodes in it, and the entropies of these substructures are calculated. The virtual network will be embedded preferentially into the substructure with the closest entropy if the substrate resource satisfies the demand of the virtual network. The experimental results validate that the efficiency of virtual network embedding can be improved through our proposal. Simultaneously, the quality of embedding has been guaranteed without significant degradation. View Full-Text
Keywords: graph entropy; virtual network embedding; probability; information measure graph entropy; virtual network embedding; probability; information measure
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Zhang, J.; Zhao, C.; Wu, H.; Lin, M.; Duan, R. Virtual Network Embedding Based on Graph Entropy. Entropy 2018, 20, 315.

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