Observing the headway in vehicular industry, new applications are developed demanding more resources. For instance, real-time vehicular applications require fast processing of the vast amount of generated data by vehicles in order to maintain service availability and reachability while driving. Fog devices are capable of bringing cloud intelligence near the edge, making them a suitable candidate to process vehicular requests. However, their location, processing power, and technology used to host and update services affect their availability and performance while considering the mobility patterns of vehicles. In this paper, we overcome the aforementioned limitations by taking advantage of the evolvement of On-Board Units, Kubeadm Clustering, Docker Containerization, and micro-services technologies. In this context, we propose an efficient resource and context aware approach for deploying containerized micro-services on on-demand fogs called Vehicular-OBUs-As-On-Demand-Fogs. Our proposed scheme embeds (1) a Kubeadm based approach for clustering OBUs and enabling on-demand micro-services deployment with the least costs and time using Docker containerization technology, (2) a hybrid multi-layered networking architecture to maintain reachability between the requesting user and available vehicular fog cluster, and (3) a vehicular multi-objective container placement model for producing efficient vehicles selection and services distribution. An Evolutionary Memetic Algorithm is elaborated to solve our vehicular container placement problem. Experiments and simulations demonstrate the relevance and efficiency of our approach compared to other recent techniques in the literature.

中文翻译：

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车载按需雾：容器化微服务的资源和上下文感知部署

随着汽车工业的发展，需要更多资源的新应用也在不断发展。例如，实时车辆应用要求车辆快速处理大量生成的数据，以便在驾驶时保持服务可用性和可达性。雾设备能够将云智能带到边缘，使其成为处理车辆请求的合适人选。但是，它们的位置，处理能力以及用于托管和更新服务的技术会影响其可用性和性能，同时还要考虑车辆的出行方式。在本文中，我们通过利用车载单元，Kubeadm群集，Docker容器化和微服务技术的发展来克服上述限制。在这种情况下，我们提出了一种有效的资源和上下文感知方法，用于在称为Fehicular-OBUs-As-On-Demand-Fogs的按需雾中部署容器化微服务。我们提出的方案嵌入（1）一种基于Kubeadm的方法，用于对OBU进行群集，并使用Docker容器化技术以最少的成本和时间实现按需微服务部署；（2）混合多层网络体系结构，以保持请求之间的可访问性用户和可用的车辆雾簇，以及（3）用于产生有效的车辆选择和服务分配的车辆多目标集装箱放置模型。阐述了一种进化模因算法来解决我们的车辆集装箱放置问题。