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A heuristic survivable virtual network mapping algorithm

  • Xiangwei Zheng
  • Jie Tian
  • Xiancui Xiao
  • Xinchun Cui
  • Xiaomei Yu
Foundations

Abstract

Network virtualization is a promising solution to attack Internet ossification. Virtual network mapping (or embedding) problem is the core of it and is proved to be NP-hard. In this paper, virtual network mapping problem with survivability is formulated and solved with a heuristic algorithm. Firstly, network link resources are divided into primary flow resources and secondary flow resources. The former are used under normal network operation, whereas the latter are used as backup resources once the networks fail. Secondly, we introduce a novel metric named global resource capacity (GRC) which is recently proposed for measuring node mapping capacity to improve network load balance. At last, a heuristic survivable virtual network embedding algorithm (GRC-SVNE) is proposed. In node mapping phase, we calculate the mapping capacity of all nodes and then some nodes are selected as candidate nodes for virtual network embedding and the goal is to improve mapping successful ratio. After that, link mapping is performed with Dijkstra algorithm. Simulation results show that GRC-SVNE outperforms the traditional greedy algorithm (GREEDY), randomized algorithm (R-ViNE) as well as deterministic algorithm (D-ViNE) and demonstrates desirable results in terms of acceptance ratio, network load balance and network revenue.

Keywords

Heuristic algorithm Network failure Survivability Load balance 

Notes

Acknowledgements

This study was funded by the National Natural Science Foundation of China (61373149, 61672329) and Shandong Provincial Natural Science Foundation for Young Scholars of China (Grant No. ZR2017QF008).

Compliance with ethical standards

Conflict of interest

There is no conflict of interest.

Human participants or animals

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinanPeople’s Republic of China
  2. 2.Shandong Provincial Key Laboratory for Distributed Computer Software Novel TechnologyJinanPeople’s Republic of China
  3. 3.School of Computer Science and TechnologyQufu Normal UniversityRizhaoPeople’s Republic of China

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