Topology-aware virtual network embedding based on closeness centrality

  • Authors:

  • Zihou Wang;Yanni Han;Tao Lin;Yuemei Xu;Song Ci;Hui Tang

  • Affiliations:

  • High Performance Network Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China 100190;High Performance Network Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China 100190;High Performance Network Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China 100190;High Performance Network Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China 100190;High Performance Network Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China 100190 and Department of Computer and Electronics Engineering, University of Nebraska-Linco ...;High Performance Network Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China 100190

  • Venue:
  • Frontiers of Computer Science: Selected Publications from Chinese Universities
  • Year:
  • 2013

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Abstract

Network virtualization aims to provide a way to overcome ossification of the Internet. However, making efficient use of substrate resources requires effective techniques for embedding virtual networks: mapping virtual nodes and virtual edges onto substrate networks. Previous research has presented several heuristic algorithms, which fail to consider that the attributes of the substrate topology and virtual networks affect the embedding process. In this paper, for the first time, we introduce complex network centrality analysis into the virtual network embedding, and propose virtual network embedding algorithms based on closeness centrality. Due to considering of the attributes of nodes and edges in the topology, our studies are more reasonable than existing work. In addition, with the guidance of topology quantitative evaluation, the proposed network embedding approach largely improves the network utilization efficiency and decreases the embedding complexity. We also investigate our algorithms on real network topologies (e.g., AT&T, DFN) and random network topologies. Experimental results demonstrate the usability and capability of the proposed approach.