A branch-and-price algorithm for the single-path virtual network embedding problem

Networks ◽  
2017 ◽  
Vol 71 (3) ◽  
pp. 188-208 ◽  
Author(s):  
Leonardo F.S. Moura ◽  
Luciano P. Gaspary ◽  
Luciana S. Buriol
Keyword(s):  
Virtual Network ◽  
Embedding Problem ◽  
Virtual Network Embedding ◽  
Network Embedding ◽  
Branch And Price ◽  
Price Algorithm ◽  
Single Path

A branch-and-price framework for optimal virtual network embedding

2016 ◽  
Vol 94 ◽  
pp. 318-326 ◽  
Author(s):  
Yang Wang ◽  
Qian Hu ◽  
Xiaojun Cao
Keyword(s):  
Virtual Network ◽  
Virtual Network Embedding ◽  
Network Embedding ◽  
Branch And Price

A Dynamic Resource Provisioning VNE Algorithm Based on Graph Theory

2018 ◽  
pp. 73-97
Author(s):  
Isha Pathak ◽  
Deo Prakash Vidyarthi ◽  
Atul Tripathi
Keyword(s):  
Graph Theory ◽  
Network Virtualization ◽  
Resource Provisioning ◽  
Virtual Network ◽  
Embedding Problem ◽  
Theory Approach ◽  
Virtual Network Embedding ◽  
Network Access ◽  
Network Embedding ◽  
Performance Study

Business has transformed drastically over the years and cloud computing has emerged as an upcoming platform to provide all types of services, especially in the domain of digital business. Virtualization in cloud is a core activity, done at various levels, to support multiple services. Network virtualization is a significant aspect that liberates the users for seamless network access. Virtual network embedding is a process in which the demand of virtual nodes and virtual links are fulfilled by physical/substrate nodes and links while optimizing certain characteristic parameters. This chapter addresses the virtual network embedding problem to optimize parameters such as running time, residual physical network, and embedding cost using graph theory approach. It also minimizes the exhaustion of substrate network resources and still using its resources efficiently. In this chapter, a concept of graph theory has been applied for the virtual network embedding problem. The proposed model has been simulated for its performance study, and results reveal the efficacy of the method.

A topology-cognitive algorithm framework for virtual network embedding problem

2014 ◽  
Vol 11 (4) ◽  
pp. 73-84 ◽  
Author(s):  
Huang Tao ◽  
Liu Jiang ◽  
Chen Jianya ◽  
Liu Yunjie
Keyword(s):  
Virtual Network ◽  
Embedding Problem ◽  
Virtual Network Embedding ◽  
Network Embedding ◽  
Algorithm Framework

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.

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