Vehicular-OBUs-As-On-Demand-Fogs: Resource and Context Aware Deployment of Containerized Micro-Services

• Published in: IEEE/ACM transactions on networking Vol. 28; no. 2; pp. 778 - 790
• Main Authors: Sami, Hani, Mourad, Azzam, El-Hajj, Wassim
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automobiles; Availability; Cloud computing; Clustering; Computer architecture; Computer simulation; container; Containers; Context; Delays; Demand; Docker; Driver behavior; Evolutionary algorithms; Fog; Industrial development; Kubeadm; memetic algorithm; micro-services; Multilayers; on-demand fog placement; orchestration; Performance evaluation; Placement; Task analysis; Upgrading; Vehicles; vehicular clustering; vehicular edge computing; vehicular fog computing; Vehicular on-boarding units (OBUs)
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2020.2973800

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.