A Virtual Network Embedding Algorithm Based on RBF Neural Network

2017 ◽  
Author(s):  
Hui Zhang ◽  
Xiangwei Zheng ◽  
Jie Tian ◽  
Qingshui Xue
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Virtual Network ◽  
Virtual Network Embedding ◽  
Network Embedding

scholarly journals Energy-Efficient Virtual Network Embedding Algorithm Based on Hopfield Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mengyang He ◽  
Lei Zhuang ◽  
Sijin Yang ◽  
Jianhui Zhang ◽  
Huiping Meng
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Virtual Network ◽  
Embedding Problem ◽  
Virtual Network Embedding ◽  
Network Embedding ◽  
Unconstrained Problem ◽  
Candidate Set

To solve the energy-efficient virtual network embedding problem, this study proposes an embedding algorithm based on Hopfield neural network. An energy-efficient virtual network embedding model was established. Wavelet diffusion was performed to take the structural feature value into consideration and provide a candidate set for virtual network embedding. In addition, the Hopfield network was used in the candidate set to solve the virtual network energy-efficient embedding problem. The augmented Lagrangian multiplier method was used to transform the energy-efficient virtual network embedding constraint problem into an unconstrained problem. The resulting unconstrained problem was used as the energy function of the Hopfield network, and the network weight was iteratively trained. The energy-efficient virtual network embedding scheme was obtained when the energy function was balanced. To prove the effectiveness of the proposed algorithm, we designed two experimental environments, namely, a medium-sized scenario and a small-sized scenario. Simulation results show that the proposed algorithm achieved a superior performance and effectively decreased the energy consumption relative to the other methods in both scenarios. Furthermore, the proposed algorithm reduced the number of open nodes and open links leading to a reduction in the overall power consumption of the virtual network embedding process, while ensuring the average acceptance ratio and the average ratio of the revenue and cost.

Latency-Aware Virtual Network Embedding using Clusters for Green Fog Computing

2020 ◽  
Author(s):  
Bartosz Kopras ◽  
Filip Idzikowski ◽  
Wei-Che Chen ◽  
Te-Jen Wang ◽  
Chun-Ting Chou ◽  
...  
Keyword(s):  
Fog Computing ◽  
Virtual Network ◽  
Virtual Network Embedding ◽  
Network Embedding
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