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一种面向分片感知的虚拟网络重构算法。

An algorithm for fragment-aware virtual network reconfiguration.

机构信息

Information and Navigation college, Air Force Engineering University, Xi'an, Shaanxi, China.

出版信息

PLoS One. 2018 Nov 21;13(11):e0207705. doi: 10.1371/journal.pone.0207705. eCollection 2018.

DOI:10.1371/journal.pone.0207705
PMID:30462702
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6248979/
Abstract

In view of the fact that the current online virtual network embedding algorithms do not consider the fragment resources generated in the embedding process deeply enough, resulting in the problem that the acceptance ratio and the revenue to cost ratio are both low, a mathematical model for virtual network reconfiguration is constructed and a heuristic algorithm for fragment-aware virtual network reconfiguration (FA-VNR) is proposed. The FA-VNR algorithm selects the set of virtual nodes to be migrated according to the fragment degrees of the physical nodes, and selects the best virtual node migration scheme according to the reduction of the fragment degrees of the physical nodes as well as the reduction of the embedding cost of the embedded virtual networks. Extensive simulation results show that the proposed FA-VNR algorithm not only can obviously improve the acceptance ratio and the revenue to cost ratio of the current online virtual network embedding algorithm, but also has better optimization effect than the existing virtual network reconfiguration algorithm.

摘要

鉴于当前在线虚拟网络嵌入算法对嵌入过程中生成的碎片资源考虑不够深入,导致接受率和收支比都较低的问题,构建了一个虚拟网络重构的数学模型,并提出了一种感知碎片的虚拟网络重构启发式算法(FA-VNR)。FA-VNR 算法根据物理节点的碎片程度选择要迁移的虚拟节点集,并根据物理节点碎片程度的减少以及嵌入虚拟网络的嵌入成本的减少选择最佳的虚拟节点迁移方案。大量的仿真结果表明,所提出的 FA-VNR 算法不仅可以明显提高当前在线虚拟网络嵌入算法的接受率和收支比,而且比现有的虚拟网络重构算法具有更好的优化效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/b6b135bc9bbc/pone.0207705.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/f1d5e28a4fd0/pone.0207705.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/4ee139b0ffae/pone.0207705.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/51816e176bc5/pone.0207705.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/3411f4cc3e82/pone.0207705.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/c2fd536d3e92/pone.0207705.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/f9b76dbc12aa/pone.0207705.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/b6b135bc9bbc/pone.0207705.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/f1d5e28a4fd0/pone.0207705.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/4ee139b0ffae/pone.0207705.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/51816e176bc5/pone.0207705.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/3411f4cc3e82/pone.0207705.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/c2fd536d3e92/pone.0207705.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/f9b76dbc12aa/pone.0207705.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de99/6248979/b6b135bc9bbc/pone.0207705.g007.jpg

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