基于mec的车载网络移动感知QoS提升与负载均衡:一种深度学习方法

Chih-Ho Hsu, Yao Chiang, Yi Zhang, Hung-Yu Wei
{"title":"基于mec的车载网络移动感知QoS提升与负载均衡:一种深度学习方法","authors":"Chih-Ho Hsu, Yao Chiang, Yi Zhang, Hung-Yu Wei","doi":"10.1109/VTC2021-Spring51267.2021.9448705","DOIUrl":null,"url":null,"abstract":"Recently, Multi-access Edge Computing (MEC) has become a promising enabler to support emerging applications in vehicular networks by offloading compute-intensive tasks from vehicles to proximate MEC servers. However, the high mobility of vehicles brings difficulties to provide reliable services in the MEC system due to potential outages of communication in the process of offloading. Also, load balancing of the MEC system is seldom considered in previous offloading schemes, which may increase the risk of system failure and reduce Quality of Service (QoS) of vehicles due to congestions. Currently, we still lack a low-complexity method to address these issues. In this paper, we aim to promote QoS of vehicular applications by taking vehicles' mobility and latency requirements into account while guaranteeing load balancing of the MEC system. Specifically, we first formulate the joint offloading decision and resource allocation problem as a Mixed Integer NonLinear Programming (MINLP) problem. Then, by taking advantage of both Deep Neural Network (DNN) and Particle Swarm Optimization (PSO), we propose a novel framework to effectively address the problem, where PSO accelerates the training by providing high quality labeled data to DNN. Finally, simulation results show that our proposed method outperforms traditional heuristic algorithms in terms of QoS and runtime.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Mobility-Aware QoS Promotion and Load Balancing in MEC-Based Vehicular Networks: A Deep Learning Approach\",\"authors\":\"Chih-Ho Hsu, Yao Chiang, Yi Zhang, Hung-Yu Wei\",\"doi\":\"10.1109/VTC2021-Spring51267.2021.9448705\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, Multi-access Edge Computing (MEC) has become a promising enabler to support emerging applications in vehicular networks by offloading compute-intensive tasks from vehicles to proximate MEC servers. However, the high mobility of vehicles brings difficulties to provide reliable services in the MEC system due to potential outages of communication in the process of offloading. Also, load balancing of the MEC system is seldom considered in previous offloading schemes, which may increase the risk of system failure and reduce Quality of Service (QoS) of vehicles due to congestions. Currently, we still lack a low-complexity method to address these issues. In this paper, we aim to promote QoS of vehicular applications by taking vehicles' mobility and latency requirements into account while guaranteeing load balancing of the MEC system. Specifically, we first formulate the joint offloading decision and resource allocation problem as a Mixed Integer NonLinear Programming (MINLP) problem. Then, by taking advantage of both Deep Neural Network (DNN) and Particle Swarm Optimization (PSO), we propose a novel framework to effectively address the problem, where PSO accelerates the training by providing high quality labeled data to DNN. Finally, simulation results show that our proposed method outperforms traditional heuristic algorithms in terms of QoS and runtime.\",\"PeriodicalId\":194840,\"journal\":{\"name\":\"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448705\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448705","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

最近,多访问边缘计算(MEC)通过将计算密集型任务从车辆卸载到邻近的MEC服务器,已经成为支持车载网络中新兴应用的一个有前途的推动者。然而,车辆的高机动性给MEC系统提供可靠的服务带来了困难,因为在卸载过程中可能会出现通信中断。此外,以往的卸载方案很少考虑MEC系统的负载均衡问题,这可能会增加系统故障的风险,并且会因拥堵而降低车辆的服务质量(QoS)。目前,我们仍然缺乏一种低复杂度的方法来解决这些问题。本文的目标是在保证MEC系统负载均衡的同时,考虑车辆的移动性和延迟需求,提高车载应用的QoS。具体来说,我们首先将联合卸载决策和资源分配问题表述为一个混合整数非线性规划(MINLP)问题。然后,通过利用深度神经网络(DNN)和粒子群优化(PSO),我们提出了一个新的框架来有效地解决这个问题,其中PSO通过向DNN提供高质量的标记数据来加速训练。最后,仿真结果表明,该方法在QoS和运行时间方面优于传统的启发式算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mobility-Aware QoS Promotion and Load Balancing in MEC-Based Vehicular Networks: A Deep Learning Approach
Recently, Multi-access Edge Computing (MEC) has become a promising enabler to support emerging applications in vehicular networks by offloading compute-intensive tasks from vehicles to proximate MEC servers. However, the high mobility of vehicles brings difficulties to provide reliable services in the MEC system due to potential outages of communication in the process of offloading. Also, load balancing of the MEC system is seldom considered in previous offloading schemes, which may increase the risk of system failure and reduce Quality of Service (QoS) of vehicles due to congestions. Currently, we still lack a low-complexity method to address these issues. In this paper, we aim to promote QoS of vehicular applications by taking vehicles' mobility and latency requirements into account while guaranteeing load balancing of the MEC system. Specifically, we first formulate the joint offloading decision and resource allocation problem as a Mixed Integer NonLinear Programming (MINLP) problem. Then, by taking advantage of both Deep Neural Network (DNN) and Particle Swarm Optimization (PSO), we propose a novel framework to effectively address the problem, where PSO accelerates the training by providing high quality labeled data to DNN. Finally, simulation results show that our proposed method outperforms traditional heuristic algorithms in terms of QoS and runtime.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信