用于吞吐量预测的机器学习和深度学习

Dongwon Lee, Joohyung Lee
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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。
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.
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