Fully Distributed Task Offloading in Vehicular Edge Computing

Abstract

In vehicular edge computing (VEC), the deployment of road side units (RSUs) along roads enables vehicles to offload computation-intensive tasks for efficient data processing. However, VEC poses unique challenges, including resource constraints on vehicles and RSUs, high vehicle mobility, and the large-scale nature of the infrastructure. Existing solutions, whether centralized or distributed, often suffer from longer decision-making times or task response times, making them unsuitable for vehicular scenarios. To address these challenges, this paper proposes a Fully Distributed Task Offloading (FDTO) decision-making scheme, which enables vehicles to iteratively adjust their offloading decisions based on resource utilization information obtained from neighboring RSUs. FDTO employs two different algorithms for decision adjustments: a greedy-based algorithm and a convex optimization-based algorithm. Theoretical analysis proves the convergence of the proposed algorithms to a global optimum through iterations. To evaluate the performance of FDTO, extensive simulations are conducted and the results demonstrate that the proposed algorithms offer near-optimal performance with a short decision-making time, reducing the average task response time by 50%-65% compared to existing algorithms.