A Channel-aware FL Approach for Virtual Machine Placement in 6G Edge Intelligent Ecosystems

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Internet of Things Pub Date : 2023-02-17 DOI:10.1145/3584705
Benedetta Picano, R. Fantacci
{"title":"A Channel-aware FL Approach for Virtual Machine Placement in 6G Edge Intelligent Ecosystems","authors":"Benedetta Picano, R. Fantacci","doi":"10.1145/3584705","DOIUrl":null,"url":null,"abstract":"This article deals with an artificial intelligence (AI) framework to support Internet-of-everything (IoE) applications over sixth-generation wireless (6G) networks. An integrated IoE-Edge Intelligence ecosystem is designed to effectively face the problems of Virtual Machines (VMs) placement based on their popularity, computation offloading optimization, and system reliability improvement predicting compute nodes faults. The main objective of the article is to increase performance in terms of minimization of worst end-to-end (e2e) delay, percentage of requests in outage, and the enhancement of reliability. The article focuses on the following main issues: (i) proposal of a channel-aware federated learning (FL) approach to forecast the popularity of the VMs required by IoE devices; (ii) use of an AI-based channel conditions forecasting module at the benefits of the FL process; (iii) development of a suitable VMs placement on the basis of their popularity and of an efficient tasks allocation technique based on a modified version of the auction theory (AT) and a proper matching game; (iv) enhancement of the system reliability by an echo-state-network (ESN), located on each computation node and running in the background to predict failures and anticipate tasks migration. Numerical results validate the effectiveness of the proposed strategy for IoE applications over 6G networks.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Internet of Things","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3584705","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

This article deals with an artificial intelligence (AI) framework to support Internet-of-everything (IoE) applications over sixth-generation wireless (6G) networks. An integrated IoE-Edge Intelligence ecosystem is designed to effectively face the problems of Virtual Machines (VMs) placement based on their popularity, computation offloading optimization, and system reliability improvement predicting compute nodes faults. The main objective of the article is to increase performance in terms of minimization of worst end-to-end (e2e) delay, percentage of requests in outage, and the enhancement of reliability. The article focuses on the following main issues: (i) proposal of a channel-aware federated learning (FL) approach to forecast the popularity of the VMs required by IoE devices; (ii) use of an AI-based channel conditions forecasting module at the benefits of the FL process; (iii) development of a suitable VMs placement on the basis of their popularity and of an efficient tasks allocation technique based on a modified version of the auction theory (AT) and a proper matching game; (iv) enhancement of the system reliability by an echo-state-network (ESN), located on each computation node and running in the background to predict failures and anticipate tasks migration. Numerical results validate the effectiveness of the proposed strategy for IoE applications over 6G networks.
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
6G边缘智能生态系统中虚拟机放置的通道感知FL方法
本文讨论了通过第六代无线(6G)网络支持万物互联(IoE)应用程序的人工智能(AI)框架。基于虚拟机的普及程度、优化计算负载、提高系统可靠性、预测计算节点故障,设计集成的IoE-Edge智能生态系统,有效应对虚拟机的布局问题。本文的主要目标是通过最小化最差端到端(e2e)延迟、中断请求的百分比和增强可靠性来提高性能。本文重点关注以下主要问题:(i)提出一种通道感知联邦学习(FL)方法来预测物联网设备所需虚拟机的普及程度;(ii)利用FL过程的优势,使用基于人工智能的通道状况预测模块;(iii)根据虚拟机的受欢迎程度,发展合适的虚拟机位置,并根据改良的拍卖理论和适当的配对游戏,发展有效的任务分配技术;(iv)通过回声状态网络(ESN)提高系统可靠性,回声状态网络位于每个计算节点上,并在后台运行,以预测故障和预测任务迁移。数值结果验证了该策略在6G网络上物联网应用的有效性。
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
CiteScore
5.20
自引率
3.70%
发文量
0
期刊最新文献
FLAShadow: A Flash-based Shadow Stack for Low-end Embedded Systems CoSense: Deep Learning Augmented Sensing for Coexistence with Networking in Millimeter-Wave Picocells CASPER: Context-Aware IoT Anomaly Detection System for Industrial Robotic Arms Collaborative Video Caching in the Edge Network using Deep Reinforcement Learning ARIoTEDef: Adversarially Robust IoT Early Defense System Based on Self-Evolution against Multi-step Attacks
×
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