Title: Optimisation of task offloading scheduling strategy for vehicular edge computing networks

Authors: Tianqi Gao; Hongfeng Tao; Yuanzhi Ni

Addresses: School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China ' School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China ' School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China

Abstract: In the last decade, the service of intelligent transportation systems has benefited from the rapid development of advanced computing and communication technologies. However, it is difficult to satisfy the increasing user demand and strict service requirements with the current local or cloud computing paradigm only. In this paper, we propose an operator-based edge service network composed of multi-layer roadside units (RSUs) for various scenarios. An optimised task offloading scheduling strategy considering the service demand, computing delay and energy consumption, is designed for both stochastic and concurrent tasks. Furthermore, a genetic algorithm (GA) is proposed to solve the bandwidth allocation problem after the computation execution. Finally, the simulation results verify the service efficiency and operation effectiveness of the proposed strategy, in terms of the task execution delay, operation cost and attainment rate.

Keywords: edge computing; vehicular network; task offloading; resource allocation; roadside unit; optimisation.

DOI: 10.1504/IJSCIP.2024.138669

International Journal of System Control and Information Processing, 2024 Vol.4 No.2, pp.91 - 108

Received: 20 Feb 2023
Accepted: 18 Aug 2023
Published online: 23 May 2024 *

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