An Efficient Partial Task Offloading and Resource Allocation Scheme for Vehicular Edge Computing in a Dynamic Environment

In conventional vehicular edge computing (VEC), vehicles at edge nodes often face issues such as congestion and overhead, particularly when numerous vehicles offload their tasks to a single edge node. This scenario results in heightened processing delays and increased energy consumption. Additionally, the unpredictability of the task offloading process at the edge node presents a major challenge for vehicles in determining their offloading strategies within a dynamic environment. In this paper, we propose a jointed approach for task offloading and resource allocation aimed at minimizing the overall latency and energy consumption of all vehicles. This is accomplished through task profiling, channel allocation, and resource distribution for both vehicles and roadside units (RSUs). We introduce a framework for partial task offloading and resource allocation based on a decentralized learning algorithm known as the Distributed Partial Task Offloading and Resource Allocation (DPTORA) scheme. This approach provides flexibility in task processing, allowing each vehicle to choose whether to execute its task locally, partially offload it, or distribute it among multiple RSUs using vehicle-to-infrastructure (V2I) connections in a dense network. We develop algorithms based on the Generalist Pursuit Learning Algorithm (GPLA) and the Distributed Partial Task Offloading (DPTO) scheme to effectively address the optimization problem. Additionally, we provide a sub-optimal solution with low computational complexity. Extensive simulations validate the effectiveness of our proposed scheme in decreasing time latency and energy consumption while facilitating partial task offloading and resource allocation through a decentralized learning Approach in dynamic VEC networks.

I. Introduction

With the rapid advancement of the Internet of Vehicles (IoV) has given rise to various new applications, including virtual reality (VR), augmented reality (AR), smart traffic management, autonomous driving, and real-time navigation, all of which have a high demand for computational task offloading and resource allocation in dynamic vehicular networks [1]. Consequently, efficiently processing tasks within vehicles poses significant challenges due to their limited computing and storage capabilities. Traditional cloud-based centralized computing approaches result in increased transmission latency and energy consumption due to the long distances involved between vehicles and cloud servers. Vehicular edge computing (VEC) presents a promising solution to effectively tackle this issue. In a VEC system, tasks generated by vehicles are offloaded to edge servers equipped with roadside units (RSUs) to minimize offloading latency and energy consumption. Edge-based solutions offer computing and storage resources at the network’s edge, greatly decreasing the distance to the servers compared to traditional cloud servers [2], [3].