A Reliable Learning Based Task Offloading Framework for Vehicular Edge Computing

Balawal Shabir, A. Malik, A. U. Rahman, M. A. Khan, Z. Anwar
{"title":"A Reliable Learning Based Task Offloading Framework for Vehicular Edge Computing","authors":"Balawal Shabir, A. Malik, A. U. Rahman, M. A. Khan, Z. Anwar","doi":"10.1109/ICoDT255437.2022.9787462","DOIUrl":null,"url":null,"abstract":"Vehicular fog computing is an evolving solution for the delay sensitive computations at the vehicular edge. Due to the rapidly changing environment, effective resource utilisation becomes quite challenging. Centralised solution are proposed to improve the resource utilisation efficiency but with the added cost of central management and lower efficiency of the resource sharing environment. Distributed task offloading solutions are presented to address the issue; however, it results in an uneven workload distribution without considering the reliability of the communication between the nodes. In this work, we propose a fully distributed task offloading framework that minimises the residence time of the system under the task failure constraints. This overall improves the straggler effect by guaranteeing the task offloading delay at the vehicular edge by replicating the tasks at different vehicular destinations. The proposed work only keeps the tasks with the fastest response time and tasks with the slower response times are removed from the execution queues improving the task resource utilisation efficiency of the resource sharing environment.","PeriodicalId":291030,"journal":{"name":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"218 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICoDT255437.2022.9787462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Vehicular fog computing is an evolving solution for the delay sensitive computations at the vehicular edge. Due to the rapidly changing environment, effective resource utilisation becomes quite challenging. Centralised solution are proposed to improve the resource utilisation efficiency but with the added cost of central management and lower efficiency of the resource sharing environment. Distributed task offloading solutions are presented to address the issue; however, it results in an uneven workload distribution without considering the reliability of the communication between the nodes. In this work, we propose a fully distributed task offloading framework that minimises the residence time of the system under the task failure constraints. This overall improves the straggler effect by guaranteeing the task offloading delay at the vehicular edge by replicating the tasks at different vehicular destinations. The proposed work only keeps the tasks with the fastest response time and tasks with the slower response times are removed from the execution queues improving the task resource utilisation efficiency of the resource sharing environment.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种可靠的基于学习的车辆边缘计算任务卸载框架
车辆雾计算是针对车辆边缘延迟敏感计算的一种进化解决方案。由于环境的快速变化,有效地利用资源变得非常具有挑战性。提出了提高资源利用效率的集中式解决方案,但增加了集中管理的成本,降低了资源共享环境的效率。针对这一问题,提出了分布式任务卸载方案;但是,如果不考虑节点间通信的可靠性,则会导致工作负载分布不均匀。在这项工作中,我们提出了一个完全分布式的任务卸载框架,该框架可以最大限度地减少系统在任务失效约束下的停留时间。通过在不同的车辆目的地复制任务,保证了车辆边缘的任务卸载延迟,从而总体上改善了离散效应。该方法只保留响应时间最快的任务,将响应时间较慢的任务从执行队列中移除,提高了资源共享环境的任务资源利用效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Segmentation of Images Using Deep Learning: A Survey Semantic Keywords Extraction from Paper Abstract in the Domain of Educational Big Data to support Topic Clustering Automatically Categorizing Software Technologies A Theoretical CNN Compression Framework for Resource-Restricted Environments Automatic Detection and classification of Scoliosis from Spine X-rays using Transfer Learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1