Virtual Network Embedding Algorithm via Diffusion Wavelet

Zhuang, Lei; Tian, Shuaikui; He, Mengyang; Wang, Guoqing; Liu, Wentan; Ma, Ling

doi:10.1109/access.2019.2940971

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…Network virtualization technologies have evolved from virtual LANS (VLANs), VPNS, active programming networks, and overlay networks to multiple network theories, which enable multiple and heterogeneous physical networks to provide flexible and scalable cloud computing services. Based on the concept of network virtualization and virtualization technologies, traffic loads and communication operations can be easily controlled and managed to better support on-demand parallel processing systems for big data applications, online gaming, high definition television (HDTV), and on-demand bandwidth, such as MapReduce [18,19]. Two virtual nodes from the same virtual network request can be mapped to two different physical nodes, while two virtual nodes from different virtual network requests can be mapped to the same physical node, and the main resources of the virtual node and virtual link request are CPU and bandwidth, respectively.…”

Section: Network Virtualization Technologymentioning

confidence: 99%

Research on Intelligent Mapping Algorithm of Secure Virtual Network under Cloud Computing

Niu

Liang

Zhang

2022

Wireless Communications and Mobile Computing

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In order to improve the mapping efficiency of network intelligent mapping algorithm, this paper proposes a secure virtual network intelligent mapping algorithm under cloud computing. Firstly, the network virtualization technology is analyzed, and a network virtualization example diagram is proposed. Then the energy consumption model and network load model of virtual network mapping problem are constructed to analyze the mapping reliability. Finally, the virtual network mapping strategy is optimized by cloud computing technology to realize the research on intelligent mapping algorithm of secure virtual network under cloud computing. Experimental results show that the percentage of nodes used by the proposed algorithm is much lower than that of other mapping algorithms compared, and the energy consumption is about 8100RMB/h at 60,000 unit time point, which is much lower than that of other methods, indicating that the proposed algorithm has high mapping efficiency.

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Section: Network Virtualization Technologymentioning

confidence: 99%

Research on Intelligent Mapping Algorithm of Secure Virtual Network under Cloud Computing

Niu

Liang

Zhang

2022

Wireless Communications and Mobile Computing

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“…This solution has a high calculation complexity, and it is not suitable for large-scale network environments. Zhuang et al [11] proposed a VNE algorithm on the basis of the diffusion wavelets. With the diffusion wavelets, the algorithm comprehensively evaluates the topological structure of the nodes, the connection density, and the number of links between the nodes.…”

Section: Introductionmentioning

confidence: 99%

Energy‐Efficient Virtual Network Embedding Algorithm Based on Hopfield Neural Network

Zhuang

Yang

et al. 2021

Wireless Communications and Mobile Computing

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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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Virtual Network Embedding Algorithm via Diffusion Wavelet

Cited by 2 publications

References 35 publications

Research on Intelligent Mapping Algorithm of Secure Virtual Network under Cloud Computing

Research on Intelligent Mapping Algorithm of Secure Virtual Network under Cloud Computing

Energy‐Efficient Virtual Network Embedding Algorithm Based on Hopfield Neural Network

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