Real-Time Streaming Traffic Prediction Using Deep Learning Models Based on Recurrent Neural Network


KIPS Transactions on Computer and Communication Systems, Vol. 12, No. 2, pp. 53-60, Feb. 2023
https://doi.org/10.3745/KTCCS.2023.12.2.53,   PDF Download:
Keywords: Real-Time Streaming Service, traffic prediction, Recurrent Neural Network, deep-learning
Abstract

Recently, the demand and traffic volume for various multimedia contents are rapidly increasing through real-time streaming platforms. In this paper, we predict real-time streaming traffic to improve the quality of service (QoS). Statistical models have been used to predict network traffic. However, since real-time streaming traffic changes dynamically, we used recurrent neural network-based deep learning models rather than a statistical model. Therefore, after the collection and preprocessing for real-time streaming data, we exploit vanilla RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU models to predict real-time streaming traffic. In evaluation, the training time and accuracy of each model are measured and compared.


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Cite this article
[IEEE Style]
J. Kim and D. An, "Real-Time Streaming Traffic Prediction Using Deep Learning Models Based on Recurrent Neural Network," KIPS Transactions on Computer and Communication Systems, vol. 12, no. 2, pp. 53-60, 2023. DOI: https://doi.org/10.3745/KTCCS.2023.12.2.53.

[ACM Style]
Jinho Kim and Donghyeok An. 2023. Real-Time Streaming Traffic Prediction Using Deep Learning Models Based on Recurrent Neural Network. KIPS Transactions on Computer and Communication Systems, 12, 2, (2023), 53-60. DOI: https://doi.org/10.3745/KTCCS.2023.12.2.53.