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.

中文翻译：

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基于Hopfield神经网络的高效节能虚拟网络嵌入算法。

为解决节能虚拟网络的嵌入问题，本研究提出了一种基于Hopfield神经网络的嵌入算法。建立了高效节能的虚拟网络嵌入模型。进行小波扩散是为了考虑结构特征值，并为虚拟网络嵌入提供候选集。另外，在候选集中使用了Hopfield网络来解决虚拟网络的节能嵌入问题。利用增强拉格朗日乘子法将高能效虚拟网络嵌入约束问题转化为无约束问题。由此产生的无约束问题被用作Hopfield网络的能量函数，并且对网络权重进行了迭代训练。当能量函数达到平衡时，获得了一种高效节能的虚拟网络嵌入方案。为了证明该算法的有效性，我们设计了两个实验环境，即中型场景和小型场景。仿真结果表明，在两种情况下，相对于其他方法，该算法均具有较好的性能，并有效降低了能耗。此外，提出的算法减少了开放节点和开放链路的数量，从而减少了虚拟网络嵌入过程的总体功耗，同时确保了平均接受率以及收益和成本的平均比率。中型方案和小型方案。仿真结果表明，在两种情况下，相对于其他方法，该算法均具有较好的性能，并有效降低了能耗。此外，提出的算法减少了开放节点和开放链路的数量，从而减少了虚拟网络嵌入过程的总体功耗，同时确保了平均接受率以及收益和成本的平均比率。中型方案和小型方案。仿真结果表明，在两种情况下，相对于其他方法，该算法均具有较好的性能，并有效降低了能耗。此外，提出的算法减少了开放节点和开放链路的数量，从而减少了虚拟网络嵌入过程的总体功耗，同时确保了平均接受率以及收益和成本的平均比率。