Joint Offloading and Resource Allocation in Vehicular Edge Computing and Networks

TL;DR

This paper proposes a joint offloading and resource allocation algorithm for Vehicular Edge Computing to optimize task processing delay and system utility, addressing resource limitations and improving vehicular service performance.

Contribution

It introduces a novel joint optimization framework for offloading and resource allocation in VEC, with a low-complexity algorithm outperforming existing solutions.

Findings

JOSC algorithm achieves lower delay than benchmarks.

System utility is significantly improved with joint optimization.

Algorithm demonstrates robustness across various network conditions.

Abstract

The emergence of computation intensive on-vehicle applications poses a significant challenge to provide the required computation capacity and maintain high performance. Vehicular Edge Computing (VEC) is a new computing paradigm with a high potential to improve vehicular services by offloading computation-intensive tasks to the VEC servers. Nevertheless, as the computation resource of each VEC server is limited, offloading may not be efficient if all vehicles select the same VEC server to offload their tasks. To address this problem, in this paper, we propose offloading with resource allocation. We incorporate the communication and computation to derive the task processing delay. We formulate the problem as a system utility maximization problem, and then develop a low-complexity algorithm to jointly optimize offloading decision and resource allocation. Numerical results demonstrate the superior performance of our Joint Optimization of Selection and Computation (JOSC) algorithm compared to state of the art solutions.