A Task Dependency-based Deduplicated Task Offloading Mechanism in Vehicular Edge Computing
Zhenyi Shao, Zhuofan Liao, Xiaoyong Tang · 2024
The increasing demand for in-vehicle applications has raised the complexity and computational load, while the in-vehicle tasks exhibit a sensitivity to latency. Previous research has proposed utilizing the idle computational resources of roadside vehicles to alleviate this contradiction. However, the high mobility of vehicles leads to communication interruptions, and the time-varying nature of vehicle density makes resource allocation challenging. In this work, we leverage the dependencies between vehicular computing tasks and design a deduplication offloading mechanism for stable reduction of latency. This mechanism consists of two stages, named the Multi-hop Clustering Deduplication Offloading (MCDO) mechanism. Firstly, a Multi-hop Two Layer Clustering (MTLC) algorithm is designed to divide vehicles based on task dependencies, speed, and position information. Then, a Deduplication Layered Offloading (DLO) algorithm is proposed to identify and remove duplicated tasks within each cluster while maintaining their inter-dependencies. Simulation results demonstrate that MCDO effectively divides vehicle clusters and offloads tasks efficiently under various road conditions. Compared to existing approaches, MCDO significantly enhances system performance, achieving a minimum improvement of 15.1% in terms of latency.