With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation.

中文翻译：

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适用于智能交通大规模物联网的VNF链放置。

随着物联网（IoT）的出现，智能交通已经随着时间的流逝而发展，以提高交通安全性和效率以及减少拥堵和环境污染。但是，有一些具有挑战性的问题需要解决，以便可以充分发挥其潜力。智能交通的主要挑战在于，作为物联网基础设施中边缘节点的主要类型的车辆和行人正在不断发展。因此，大型网络的拓扑结构随时间而迅速变化，并且服务链可能需要频繁地重建。现有的虚拟网络功能（VNF）链放置方法大多擅长于静态网络拓扑，并且网络的任何发展都需要全局计算，这会导致计算效率低下和资源浪费。通过将网络拓扑映射到图，我们提出了一种新颖的VNF放置方法，称为BVCP（边界VNF链放置），以通过将图精心划分为多个子图并充分利用边界管理程序来解决此问题。实验结果表明，BVCP在VNF链放置方面优于最新方法，在智能交通的大规模IoT中非常高效。