An efficient task offloading scheme in vehicular edge computing

Salman Raza,Wei Liu,Manzoor Ahmed,Muhammad Ayzed Mirza,Shangguang Wang,Qibo Sun,Muhammad Rizwan Anwar

doi:10.1186/s13677-020-00175-w

Abstract

Vehicular edge computing (VEC) is a promising paradigm to offload resource-intensive tasks at the network edge. Owing to time-sensitive and computation-intensive vehicular applications and high mobility scenarios, cost-efficient task offloading in the vehicular environment is still a challenging problem. In this paper, we study the partial task offloading problem in vehicular edge computing in an urban scenario. Where the vehicle computes some part of a task locally, and offload the remaining task to a nearby vehicle and to VEC server subject to the maximum tolerable delay and vehicle’s stay time. To make it cost-efficient, including the cost of the required communication and computing resources, we consider to fully exploit the vehicular available resources. We estimate the transmission rates for the vehicle to vehicle and vehicle to infrastructure communication based on practical assumptions. Moreover, we present a mobility-aware partial task offloading algorithm, taking into account the task allocation ratio among the three parts given by the communication environment conditions. Simulation results validate the efficient performance of the proposed scheme that not only enhances the exploitation of vehicular computation resources but also minimizes the overall system cost in comparison to baseline schemes.

Highlights

As an enabling technology for the Internet of Vehicles, Vehicular edge computing (VEC) provides possible solutions to share the computation capabilities between vehicles
As we have considered partial task offloading, part of the computation is handled locally, while the remaining task is offloaded to nearby vehicles and the VEC server
We evaluate the influence of different parameters and vehicular environments on our mobility-aware partial task offloading scheme by comparing it with different strategies

Summary

Introduction

As an enabling technology for the Internet of Vehicles, Vehicular edge computing (VEC) provides possible solutions to share the computation capabilities between vehicles. We propose a mobility-aware partial task offloading algorithm in the VEC scenario This allows each vehicle to select its nearby vehicles based on the best available resources with minimum cost. (26d) indicates that the task for the V2V part should be transmitted completely before the vehicle Vn runs out of the communication range of Vi. Mobility-Aware partial (MAP) task offloading algorithm In the V2V network, the global information of vehicles may not be available or cost too much. The algorithm to choose the qualified nearby vehicle is as follow: Ratio estimation for partial task offloading The time to transmit the portion of a task of vehicle Vn must satisfy the constraint of the stay time of a selected vehicle. By exploiting the Eqs. (6) and (16), we can Algorithm 1: Choosing Qualified Nearby Vehicle

OUTPUT

Results and discussions

Conclusion