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

Abstract

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.

A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network

“…Since DHNN suffers from limited storage capacity, enhancing the storage capacity of DHNN is the real focus of our proposed HEA. Here the arrangements of Ideal strings are shown below: (1) , S (2) , S (3) , S (4) , S (5) ] overall, these multi-objective functions have been written mathematically in Equations ( 17)- (20):…”

“…The mechanism continues unless any one of the phases failed to satisfy the objective functions. Moreover, the Pareto optimality can be considered when five (5) ideal solution strings fail to generate in a simulation. In our total simulation (10 120) NN  , the proposed model successfully achieved five ideal solution strings.…”

An edge-based approach for virtual network embedding based on the graph edit distance

“…In general, the resource allocation problem is known as the virtual network embedding (VNE) problem [4], which can be posed as a graph-theoretic problem: both VNR, describing a workload, and SN, describing the physical infrastructure, can be modeled as graphs. To better explain the VNE problem, Fig 1 shows a physical network of numerous nodes and edges receiving a VNR with four virtual nodes and four virtual links.…”