Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation

TL;DR

This paper proposes a multi-agent reinforcement learning approach for task offloading and resource allocation in a digital twin vehicular edge computing network, improving real-time processing of multiple vehicle tasks.

Contribution

It introduces a novel multi-task digital twin VEC network and develops a reinforcement learning method for optimized offloading and resource allocation.

Findings

The proposed method outperforms benchmark algorithms.

Effective in real-time multi-task vehicular environments.

Enhances computational efficiency and resource utilization.

Abstract

With the increasing demand for multiple applications on internet of vehicles. It requires vehicles to carry out multiple computing tasks in real time. However, due to the insufficient computing capability of vehicles themselves, offloading tasks to vehicular edge computing (VEC) servers and allocating computing resources to tasks becomes a challenge. In this paper, a multi task digital twin (DT) VEC network is established. By using DT to develop offloading strategies and resource allocation strategies for multiple tasks of each vehicle in a single slot, an optimization problem is constructed. To solve it, we propose a multi-agent reinforcement learning method on the task offloading and resource allocation. Numerous experiments demonstrate that our method is effective compared to other benchmark algorithms.