Name
Abstract | ||
|---|---|---|
The performance of virtual network embedding determines the effectiveness and efficiency of a virtualized network, making it a critical part of the network virtualization technology. However, most existing algorithms fail to provide automatic embedding solutions in an acceptable running time. In this paper, we combine reinforcement learning with a novel neural network structure and propose a new virtual network embedding algorithm. The proposed algorithm can learn to embed virtual networks automatically. Extensive simulation results show that our algorithm achieves the best performance on most metrics compared with the existing typical and stateof-the-art solutions. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/HPCC/SmartCity/DSS.2019.00095 | 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) |
Keywords | Field | DocType |
Virtual Network Embedding,Network Virtualization,Reinforcement Learning,Graph Convolutional Network | Embedding,Computer science,Virtual network embedding,Artificial neural network,Network virtualization,Distributed computing,Reinforcement learning | Conference |
ISBN | Citations | PageRank |
978-1-7281-2059-1 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongxia Yan | 1 | 0 | 0.34 |
| Jingguo Ge | 2 | 7 | 3.15 |
| Yulei Wu | 3 | 480 | 51.95 |
| Hongbo Zheng | 4 | 4 | 1.07 |
| Liangxiong Li | 5 | 20 | 1.00 |
| Tong Li | 6 | 1 | 1.38 |