Title: Feature-aware task offloading and scheduling mechanism in vehicle edge computing environment

Authors: Shunli Zhang

Addresses: Jinzhong University, Yuci District, Jinzhong City, Shanxi Province, China

Abstract: With the rapid development and application of driverless technology, the number and location of vehicles, the channel and bandwidth of wireless network are time-varying, which leads to the increase of offloading delay and energy consumption of existing algorithms. To solve this problem, the vehicle terminal task offloading decision problem is modelled as a Markov decision process, and a task offloading algorithm based on DDQN is proposed. In order to guide agents to quickly select optimal strategies, this paper proposes an offloading mechanism based on task feature. In order to solve the problem that the processing delay of some edge server tasks exceeds the upper limit of their delay, a task scheduling mechanism based on buffer delay is proposed. Simulation results show that the proposed method has greater performance advantages in reducing delay and energy consumption compared with existing algorithms.

Keywords: mobile edge computing; internet of vehicles; task offloading; DDQN; task feature.

DOI: 10.1504/IJVICS.2024.142101

International Journal of Vehicle Information and Communication Systems, 2024 Vol.9 No.4, pp.415 - 433

Received: 23 Jul 2023
Accepted: 22 Apr 2024
Published online: 07 Oct 2024 *

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