Novel Lagrange Multipliers-Driven Adaptive Offloading for Vehicular Edge Computing

Abstract

Vehicular Edge Computing (VEC) is a transportation-specific version of Mobile Edge Computing (MEC) designed for vehicular scenarios. Task offloading allows vehicles to send computational tasks to nearby Roadside Units (RSUs) in order to reduce the computation cost for the overall system. However, the state-of-the-art solutions have not fully addressed the challenge of large-scale task result feedback with low delay, due to the extremely flexible network structure and complex traffic data. In this paper, we explore the joint task offloading and resource allocation problem with result feedback cost in the VEC. In particular, this study develops a VEC computing offloading scheme, namely, a Lagrange multipliers-based adaptive computing offloading with prediction model, considering multiple RSUs and vehicles within their coverage areas. First, the VEC network architecture employs GAN to establish a prediction model, utilizing the powerful predictive capabilities of GAN to forecast the maximum distance of future trajectories, thereby reducing the decision space for task offloading. Subsequently, we propose a real-time adaptive model and adjust the parameters in different scenarios to accommodate the dynamic characteristic of the VEC network. Finally, we apply Lagrange Multiplier-based Non-Uniform Genetic Algorithm (LM-NUGA) to make task offloading decision. Effectively, this algorithm provides reliable and efficient computing services. The results from simulation indicate that our proposed scheme efficiently reduces the computation cost for the whole VEC system. This paves the way for a new generation of disruptive and reliable offloading schemes.