6G边缘智能生态系统中虚拟机放置的通道感知FL方法

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Benedetta Picano, R. Fantacci
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引用次数: 0

摘要

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。
A Channel-aware FL Approach for Virtual Machine Placement in 6G Edge Intelligent Ecosystems
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.
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CiteScore
5.20
自引率
3.70%
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