Technical, Economic and Environmental Evaluation of Vehicular and Edge Computing

Jury

• M. Marcelo DIAS DE AMORIM, CNRS, LIP6, Sorbonne Université, Reporter
• M. Riadh DHAOU, Institut National Polytechnique de Toulouse, Reporter
• Mme Kinda KHAWAM, Université Paris-Saclay, Examiner
• M. Claudio Enrico PALAZZI, Università degli Studi di Padova, Examiner
• M. Laurent ROULLET, Nokia Bell Labs France – Paris-Saclay, Invited member

Keywords

Cloud véhiculaire,cloud computing,réseaux véhiculaires,edge computing,investissement au edge,5G

Abstract

This thesis investigates the technical, economic, and environmental feasibility of computing architectures supporting delay-sensitive applications such as Augmented Reality (AR) and Autonomous Driving (AD). While Cloud Computing (CC) is the prevalent paradigm today, it cannot offer sufficiently low latency. This limitation is addressed by Edge Computing (EC), which deploys computational capability at the edge of the access network. However, EC entails high infrastructure costs and raises environmental concerns due to the short lifecycle of electronic devices (around four years) and increased energy consumption. Meanwhile, the number of connected vehicles is steadily increasing. These vehicles already carry onboard computing and communication resources that can be opportunistically exploited not only for driving-related tasks but also for offloading computation from external devices such as smartphones, laptops, or wearable health devices. These resources can be managed under the Vehicular Cloud Computing (VCC) paradigm. In this thesis, we first analyze the economic feasibility of EC deployment through a game-theoretic model, showing how multi-tenant cooperation can mitigate high deployment costs. Then, we evaluate under which conditions VCC can replace EC, that is, whether offloading tasks to vehicles can provide performance comparable to EC. Results are obtained via high fidelity network and mobility simulations in an urban mobile network scenario. We find that VCC can achieve ultra low latency, around 10 ms, even when vehicles are sparsely distributed. A comparative cost analysis shows that replacing EC with VCC can reduce infrastructure expenditure by about 10 percent over five years. Finally, we propose a VCC management scheme that optimizes energy consumption, carbon emissions, and computes a fair allocation of revenues generated by end user tasks. The scheme is based on mathematical programming and coalitional game theory. Through Monte Carlo simulations, we show that the energy consumption attributable to VCC is below 0.1 percent of total vehicle consumption in realistic scenarios, and that vehicle owners receive substantial incentives to participate in task execution. Overall, this thesis demonstrates the feasibility of next generation mobile network architectures such as Edge Computing and Vehicular Cloud Computing for supporting ultra low latency applications.