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Discrete Dynamics in Nature and Society
Volume 2015, Article ID 316801, 8 pages
Research Article

A Hybrid Reliable Heuristic Mapping Method Based on Survivable Virtual Networks for Network Virtualization

1College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
2Faculty of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China

Received 19 October 2014; Revised 8 December 2014; Accepted 16 December 2014

Academic Editor: Muhammad Naveed Iqbal

Copyright © 2015 Qiang Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The reliable mapping of virtual networks is one of the hot issues in network virtualization researches. Unlike the traditional protection mechanisms based on redundancy and recovery mechanisms, we take the solution of the survivable virtual topology routing problem for reference to ensure that the rest of the mapped virtual networks keeps connected under a single node failure condition in the substrate network, which guarantees the completeness of the virtual network and continuity of services. In order to reduce the cost of the substrate network, a hybrid reliable heuristic mapping method based on survivable virtual networks (Hybrid-RHM-SVN) is proposed. In Hybrid-RHM-SVN, we formulate the reliable mapping problem as an integer linear program. Firstly, we calculate the primary-cut set of the virtual network subgraph where the failed node has been removed. Then, we use the ant colony optimization algorithm to achieve the approximate optimal mapping. The links in primary-cut set should select a substrate path that does not pass through the substrate node corresponding to the virtual node that has been removed first. The simulation results show that the acceptance rate of virtual networks, the average revenue of mapping, and the recovery rate of virtual networks are increased compared with the existing reliable mapping algorithms, respectively.