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LIAO Jianxin, QING Sude, WANG Jingyu, ZHU Xiaomin, WANG Jing. Hybrid Virtual Network Embedding with Time-oriented Scheduling Policy[J]. Chinese Journal of Electronics, 2013, 22(4): 789-794.
Citation: LIAO Jianxin, QING Sude, WANG Jingyu, ZHU Xiaomin, WANG Jing. Hybrid Virtual Network Embedding with Time-oriented Scheduling Policy[J]. Chinese Journal of Electronics, 2013, 22(4): 789-794.
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Hybrid Virtual Network Embedding with Time-oriented Scheduling Policy

  • 1.

    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;

  • 2.

    Eastcom-BUPT Information Technology Company, Limited, Beijing 100191, China

Funds: This work is supported by the National Basic Research Program of China (973 Program) (No.2013CB329102), the National Natural Science Foundation of China (No.61271019, No.61101119, No.61121001, No.61072057, No.60902051), and the Program for Changjiang Scholars and Innovative Research Team in University (No.IRT1049).
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  • Received Date: July 31, 2011
  • Revised Date: December 31, 2012
  • Published Date: September 24, 2013
    Graphical Abstract
    • Abstract
  • Network virtualization provides a powerful tool to allow multiple networks, each customized to a specific purpose, to run on a shared substrate. However, a big challenge is how to map multiple virtual networks onto specific nodes and links in the shared substrate network, known as virtual network embedding problem. Previous works in virtual network embedding can be decomposed to two classes: two-stage virtual network embedding and onestage virtual network embedding. In this paper, by pruning the topology of virtual network using k-core decomposition, a hybrid virtual network embedding algorithm, with consideration of location constraints, is proposed to leverage the respective advantage of the two kinds of algorithm simultaneously in the mapping process. In addition, a time-oriented scheduling policy is introduced to improve the mapping performance. We conduct extensive simulations and the results show that the proposed algorithm has better performance in the long-term run.
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    • References (18)
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