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Algorithms for Assigning Substrate Network Resources to Virtual Network Components

Zhu, Y., Ammar, M.·2006·Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications

Abstract

Recent proposals for network virtualization provide a promising way to overcome the Internet ossification. The key idea of network virtualization is to build a diversified Internet to support a variety of network services and architectures through a shared substrate. A major challenge in network virtualization is the assigning of substrate resources to virtual networks (VN) efficiently and on-demand. This paper focuses on two versions of the VN assignment problem: VN assignment without reconfiguration (VNA-I) and VN assignment with reconfiguration (VNAII). For the VNA-I problem, we develop a basic scheme as a building block for all other advanced algorithms. Subdividing heuristics and adaptive optimization strategies are then presented to further improve the performance. For the VNA-II problem, we develop a selective VN reconfiguration scheme that prioritizes the reconfiguration of the most critical VNs. Extensive simulation experiments demonstrate that the proposed algorithms can achieve good performance under a wide range of network conditions.

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Metadata

Authors
Zhu, Y., & Ammar, M.
Venue
Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications
Publisher
IEEE
Published
2006
Pages
1-12
OpenAlex
W2142547489
Cited by
779
References
0
FWCI
9.09
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Algorithms for Assigning Substrate Network Resources to Virtual Network Components. https://app.cochat.ai/papers/10.1109/infocom.2006.322
The paper
Zhu, Y., & Ammar, M. (2006). Algorithms for Assigning Substrate Network Resources to Virtual Network Components. Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications. https://doi.org/10.1109/infocom.2006.322

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.