Survivable virtual network embedding (SVNE) is one of major challenges of network virtualization. In order to improve the utilization rate of the substrate network (SN) resources with virtual network (VN) topology connectivity guarantee under link failure in SN, we first establishes an Integer Linear Programming (ILP) model for that under SN supports path splitting. Then we designs a novel survivable VN topology protection method based on particle swarm optimization (VNE-PSO), which redefines the parameters and related operations of particles with the embedding overhead as the fitness function. Simulation results show that the solution significantly improves the long-term average revenue of the SN, the acceptance rate of VN requests, and reduces the embedding time compared with the existing research results.
Guangyuan LIU
Shijiazhuang Tiedao University,Tsinghua University
Daokun CHEN
Institute of Software Chinese Academy of Sciences
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Guangyuan LIU, Daokun CHEN, "Survivable Virtual Network Topology Protection Method Based on Particle Swarm Optimization" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 6, pp. 1414-1418, June 2020, doi: 10.1587/transinf.2019EDL8113.
Abstract: Survivable virtual network embedding (SVNE) is one of major challenges of network virtualization. In order to improve the utilization rate of the substrate network (SN) resources with virtual network (VN) topology connectivity guarantee under link failure in SN, we first establishes an Integer Linear Programming (ILP) model for that under SN supports path splitting. Then we designs a novel survivable VN topology protection method based on particle swarm optimization (VNE-PSO), which redefines the parameters and related operations of particles with the embedding overhead as the fitness function. Simulation results show that the solution significantly improves the long-term average revenue of the SN, the acceptance rate of VN requests, and reduces the embedding time compared with the existing research results.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8113/_p
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@ARTICLE{e103-d_6_1414,
author={Guangyuan LIU, Daokun CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Survivable Virtual Network Topology Protection Method Based on Particle Swarm Optimization},
year={2020},
volume={E103-D},
number={6},
pages={1414-1418},
abstract={Survivable virtual network embedding (SVNE) is one of major challenges of network virtualization. In order to improve the utilization rate of the substrate network (SN) resources with virtual network (VN) topology connectivity guarantee under link failure in SN, we first establishes an Integer Linear Programming (ILP) model for that under SN supports path splitting. Then we designs a novel survivable VN topology protection method based on particle swarm optimization (VNE-PSO), which redefines the parameters and related operations of particles with the embedding overhead as the fitness function. Simulation results show that the solution significantly improves the long-term average revenue of the SN, the acceptance rate of VN requests, and reduces the embedding time compared with the existing research results.},
keywords={},
doi={10.1587/transinf.2019EDL8113},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - Survivable Virtual Network Topology Protection Method Based on Particle Swarm Optimization
T2 - IEICE TRANSACTIONS on Information
SP - 1414
EP - 1418
AU - Guangyuan LIU
AU - Daokun CHEN
PY - 2020
DO - 10.1587/transinf.2019EDL8113
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2020
AB - Survivable virtual network embedding (SVNE) is one of major challenges of network virtualization. In order to improve the utilization rate of the substrate network (SN) resources with virtual network (VN) topology connectivity guarantee under link failure in SN, we first establishes an Integer Linear Programming (ILP) model for that under SN supports path splitting. Then we designs a novel survivable VN topology protection method based on particle swarm optimization (VNE-PSO), which redefines the parameters and related operations of particles with the embedding overhead as the fitness function. Simulation results show that the solution significantly improves the long-term average revenue of the SN, the acceptance rate of VN requests, and reduces the embedding time compared with the existing research results.
ER -