用于吞吐量预测的机器学习和深度学习

Dongwon Lee, Joohyung Lee
{"title":"用于吞吐量预测的机器学习和深度学习","authors":"Dongwon Lee, Joohyung Lee","doi":"10.1109/ICUFN49451.2021.9528756","DOIUrl":null,"url":null,"abstract":"Wireless communication contains many fluctuations than wired networks. In this paper, we present several machine learning and deep learning models to predict future network throughput, which is crucial for reducing latency in online streaming services. This paper explains the main components of the throughput prediction system. The throughput prediction model includes data input, data training, and prediction computation parts. This model accepts network throughput for the training data of the model and forecasts future data. We also present the advantages and limitations of utilizing AI models for throughput prediction. Finally, we believe that this study highlights the impact of deep learning techniques for throughput prediction.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Machine Learning and Deep Learning for Throughput Prediction\",\"authors\":\"Dongwon Lee, Joohyung Lee\",\"doi\":\"10.1109/ICUFN49451.2021.9528756\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wireless communication contains many fluctuations than wired networks. In this paper, we present several machine learning and deep learning models to predict future network throughput, which is crucial for reducing latency in online streaming services. This paper explains the main components of the throughput prediction system. The throughput prediction model includes data input, data training, and prediction computation parts. This model accepts network throughput for the training data of the model and forecasts future data. We also present the advantages and limitations of utilizing AI models for throughput prediction. Finally, we believe that this study highlights the impact of deep learning techniques for throughput prediction.\",\"PeriodicalId\":318542,\"journal\":{\"name\":\"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICUFN49451.2021.9528756\",\"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 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICUFN49451.2021.9528756","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

无线通信比有线网络包含许多波动。在本文中,我们提出了几个机器学习和深度学习模型来预测未来的网络吞吐量,这对于减少在线流媒体服务的延迟至关重要。本文介绍了吞吐量预测系统的主要组成部分。吞吐量预测模型包括数据输入、数据训练和预测计算三个部分。该模型接受网络吞吐量作为模型的训练数据,并对未来数据进行预测。我们还介绍了利用人工智能模型进行吞吐量预测的优点和局限性。最后,我们认为这项研究强调了深度学习技术对吞吐量预测的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Machine Learning and Deep Learning for Throughput Prediction
Wireless communication contains many fluctuations than wired networks. In this paper, we present several machine learning and deep learning models to predict future network throughput, which is crucial for reducing latency in online streaming services. This paper explains the main components of the throughput prediction system. The throughput prediction model includes data input, data training, and prediction computation parts. This model accepts network throughput for the training data of the model and forecasts future data. We also present the advantages and limitations of utilizing AI models for throughput prediction. Finally, we believe that this study highlights the impact of deep learning techniques for throughput prediction.
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
期刊最新文献
Augmented Reality Musical Service Part 1 for Non-face-to-face Watching by Multiple Audiences Performance Analysis of Cell-Free mmWave Massive MIMO with Low-Resolution DAC Quantization Efficient Task Offloading for MEC-Enabled Vehicular Networks: A Non-Cooperative Game Theoretic Approach High Efficiency & Low Area DC-DC Buck Converter with the Digital Feedback Loop for the Wireless Applications Interesting Projects To Strenghthen DSP Teaching
×
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