Network Function Virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of the Internet of Things (IoT). Resource scheduling for the virtual network function service chain (VNF-SC) is the key issue of the NFV. Energy consumption is an important indicator for the IoT; we take the energy consumption into the objective and define a novel objective to satisfying different objectives of the decision-maker. Due to the complexity of VNF-SC deployment problem, through taking into consideration of the heterogeneity of nodes (each node only can provide some specific VNFs), and the limitation of resources in each node, a novel optimal model is constructed to define the problem of VNF-SC deployment problem. To solve the optimization model effectively, a weighted center opposition-based learning is introduced to brainstorm optimization to find the optimal solution (OBLBSO). To show the efficiency of the proposed algorithm, numerous of simulation experiments have been conducted. Experimental results indicate that OBLBSO can improve the accuracy of the solution than compared algorithm.

中文翻译：

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基于能效对立的学习和头脑风暴优化，用于物联网中的VNF-SC部署

网络功能虚拟化（NFV）可以根据请求提供资源，并可以提高网络的灵活性。它已成为物联网（IoT）的关键技术。虚拟网络功能服务链（VNF-SC）的资源调度是NFV的关键问题。能耗是物联网的重要指标；我们将能耗作为目标，并定义一个新颖的目标来满足决策者的不同目标。由于VNF-SC部署问题的复杂性，通过考虑节点的异构性（每个节点只能提供一些特定的VNF）以及每个节点中资源的限制，构建了一种新颖的最优模型来定义问题VNF-SC部署问题。为了有效地解决优化模型，将基于加权中心反对派的学习方法引入头脑风暴优化，以找到最佳解决方案（OBLBSO）。为了显示所提出算法的效率，已经进行了许多仿真实验。实验结果表明，与比较算法相比，OBLBSO可以提高解的精度。