Literature Database Entry

zhou2024optimizing

Ziqi Zhou, Youming Tao, Agon Memedi, Chunghan Lee, Seyhan Ucar, Onur Altintas and Falko Dressler, "Optimizing Task Migration Decisions in Vehicular Edge Computing Environments," Proceedings of 1st IEEE International Conference on Meta Computing (ICMC 2024), Qingdao, China, June 2024.

Abstract

Vehicular networks are undergoing rapid transformations that require efficient and reliable management of computational tasks. Edge computing is supposed to provide these resources with minimal delay. The concept of vehicular micro clouds (VMCs) offers such services through collection among the vehicles, creating a virtual edge server. Within the VMC framework, the efficient migration of computational tasks among vehicles stands as a foundational yet intricate challenge. In this paper, we propose a novel task migration mechanism that leverages both task scheduling and offloading techniques to achieve this objective. Our mechanism mainly consists of two algorithmic modules: (1) task scheduling that prioritizes the tasks according to the earliest deadline first (EDF) principle before offloading decisions and (2) task offloading that assigns each task to the most appropriate vehicle according to the completion time optimization. As a particular feature, we consider the dwell time of cars, i.e., the estimated time vehicles stay within the VMC. We conduct extensive simulations to evaluate the impact of various parameters on task performance, such as data rate, traffic density, computing capacities, and computational demands. Our research advances the state-of-the-art in intelligent transportation system (ITS) by revealing the operational benefits of task migration in the VMC.

Quick access

Authors' Version (PDF on this web site)
BibTeX

Contact

Ziqi Zhou
Youming Tao
Agon Memedi
Chunghan Lee
Seyhan Ucar
Onur Altintas
Falko Dressler

BibTeX reference

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org.

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at www.springerlink.com.

This page was automatically generated using BibDB and bib2web.