1. What are the key technologies and use cases in the integration of 5G and MEC systems for Vehicle-to-Cloud communication?
The integration of 5G and Multi-Access Edge Computing (MEC) systems for Vehicle-to-Cloud (V2C) communication involves key technologies such as 5G Service-Based Architecture (SBA), Multi-Access Edge Computing (MEC), High Definition (Local) Maps, and Object Pose Estimation. These technologies enable low-latency, high-data-speed communication, and provide a stable connection between user equipments (UEs) and edge servers. Use cases heavily relying on environment detection, such as sensor fusion applications based on collective perception, benefit from these technologies. For example, the High Definition (Local) Maps use case utilizes MEC systems to process environmental data transmitted by vehicles, resulting in continuously updated local maps running on edge servers. Object Pose Estimation enables 3D modeling of objects based on camera data, further enhancing the capabilities of V2C communication. The integration of these technologies and use cases in the V2C communication domain opens up new opportunities for efficient and reliable communication between vehicles and cloud-based services.
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2. Which simulation tools and frameworks are closely related to the Cloud-in-the-Loop (CiL) framework?
The tools and frameworks closely related to the Cloud-in-the-Loop (CiL) framework include dSPACE's V2Cloud Hardware-in-the-Loop simulator, Dell's Hardware-in-the-Loop Autonomous driving simulation system, the X-in-the-Loop Test Methods for Cloud-based Vehicle Functions, the CarTest V2X Simulation Framework, OPNET, OMNeT, SimuLTE, Simu5G, Telco Cloud Simulator, and iCanCloud. These tools and frameworks provide various capabilities for testing and evaluating real cloud deployments and applications, supporting detailed benchmarking of relevant hardware-software systems. However, the CiL framework requires capable hardware and does not currently support 5G functionalities. Despite its limitations, the CiL framework enables testing and evaluation of real cloud deployments and applications, aiding in the design, dimensioning, and performance analysis of tested environments.
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3. What is the Cloud-in-the-Loop (CiL) simulation framework?
The Cloud-in-the-Loop (CiL) simulation framework is a research-based solution that has been developed over several years. It provides the foundation for the achievements presented in the work, allowing for the orchestration of a real, distributed cloud-based environment and the testing of cloud-native applications within it. The framework utilizes a simulator that models the behavior of user devices, enabling the examination of V2C use cases. It is configured to work with a versatile, multi-modal traffic simulation software called SUMO, which models large-scale road networks and simulates detailed traffic models. The framework consists of three main components: an automotive traffic simulator, a CiL Orchestrator, and a distributed cloud environment. The Orchestrator, developed in Java, manages the distributed cloud-based environment and orchestrates service-providing applications in the cloud. The distributed cloud environment is realized using a Kubernetes platform, enabling the deployment of cloud-native applications that implement automotive use cases. The framework allows for the examination of real applications, fine-grained benchmarking, and data collection, leading to improvements in the system and its supporting applications.
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4. How does SUMO contribute to traffic simulation?
SUMO provides an opportunity to implement and investigate any given traffic situation. The simulation traffic map used for presenting the framework's capabilities, which provides an excellent basis for testing edge systems, has already been implemented during previous tests. The map is based on Hungary, Budapest XI. district's urban environment around Infopark. The resources (servers) of the integrated edge cloud environment are located virtually on this map. To achieve this, so-called latency zones are defined for the two edge servers of the cluster, determining which resource serves the vehicle moving in the given position. These zones represent the limit within which their associated edge server can still fulfill the given latency requirements with acceptable reliability. The shape of these zones is affected by countless factors, such as the location of the base stations or the network structure. However, the zones created in the current simulation environment implement only one possible layout among many, but it is ideal from the point of view of testing edge cloud systems. The simulation data generated by SUMO is processed by CiL-Orchestrator, which then controls the pods and services running on the Kubernetes cluster and the client applications running on the application server. According to the vehicles' relative position to the zones in the simulation, the CiL-O manages the backend applications' relocation, and sets the server and client applications' network configuration to ensure proper service access. During the simulations, cars follow predefined routes designed to model traffic in an urban environment.
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