Case study : cloud computing model for vehicular data processing

Abstract

This thesis work presents an exploratory case study on a cloud computing model tailored for vehicular data processing, examining the integration of cloud-native technologies and microservices to enhance the system reliability, scalability, and real-time processing capabilities of vehicular computing systems. The study begins with an investigation on the current landscape of cloud computing, cloud-native technologies, edge computing, and their applications for enabling software-defined vehicles in vehicular environments, which forms the theoretical foundation for the proposed system. The case context is University of Oulu’s research unit, and the target of analysis is the cloud stack platform used for research on software-defined vehicles. The previously deployed system no longer served its purpose, and this exploratory case study includes both the development and the evaluation of a replacement system, the Kuura cloud stack. The case encompasses the development of Kuura along with comparisons to its predecessors, the SMAD stack and Eclipse Kuksa cloud stack. Much like its predecessors, Kuura utilizes containerized services on the OKD Kubernetes platform to achieve horizontal scalability and maintainability, but the system complexity is reduced by minimizing the number of required software components. The system evaluation, based on collected data from test drives using a real vehicle, demonstrates the system’s capability to handle both physical and virtual driving sessions simultaneously. The results emphasize the potential of the proposed model to enhance vehicular data processing and serve as a blueprint for other data-intensive applications in the Internet of Things (IoT) landscape.