Browse by author
Lookup NU author(s): Dr Nigel Thomas
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Enhanced wireless communication improves the connectivity of vehicular networks in which vehicles are utilized as infrastructures for communication and computation. Thus, a new concept “Vehicular Edge Computing (VEC)” is formed. As VEC utilizes a collaborative multitude of near-user edge resources (i.e. vehicles) in the Internet of Vehicles, the capability of these joint resources becomes heterogeneous especially in their movements. Therefore, one critical problem is how to efficiently schedule each task under such mobile environments. For the reason, we propose a hybrid dynamic scheduling scheme (HDSS) that has the ability to optimize the task scheduling dynamically based on the changeable system environments. HDSS provides a decision function (DF) to select a better-performed scheduling algorithm from two provided candidates: the queue-based dynamic scheduling (QDS) algorithm and the time-based dynamic scheduling (TDS). QDS coincides with the Join-the-Shortest Queue scheme, which decides the scheduling by sorting out a server with the shortest queue-length; nevertheless, TDS is novel scheme that is designed to implement task allocation by estimating the waiting time of each server in order to select a server with the fastest response. Finally, this research generates formal models of each scheduling algorithm and the hybrid scheduling scheme to conduct performance evaluation with a fluid flow approximation technique. The analysis results in a superior performance of HDSS in the unstable VEC environments.
Author(s): Chen X, Thomas N, Zhan T, Ding J
Publication type: Article
Publication status: Published
Journal: IEEE Access
Year: 2019
Volume: 7
Pages: 11708-11709
Online publication date: 12/08/2019
Acceptance date: 12/08/2019
Date deposited: 14/08/2019
ISSN (print): 2169-3536
ISSN (electronic): 2169-3536
Publisher: IEEE
URL: https://doi.org/10.1109/ACCESS.2019.2934890
DOI: 10.1109/ACCESS.2019.2934890
Altmetrics provided by Altmetric
