Leased Line Traffic Prediction Using a Recurrent Deep Neural Network Model


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 10, pp. 391-398, Oct. 2021
https://doi.org/10.3745/KTSDE.2021.10.10.391,   PDF Download:
Keywords: Leased Line, Traffic Modeling, time series analysis, Deep Learning, RNN, LSTM
Abstract

Since the leased line is a structure that exclusively uses two connected areas for data transmission, a stable quality level and security are ensured, and despite the rapid increase in the number of switched lines, it is a line method that is continuously used a lot in companies. However, because the cost is relatively high, one of the important roles of the network operator in the enterprise is to maintain the optimal state by properly arranging and utilizing the resources of the network leased line. In other words, in order to properly support business service requirements, it is essential to properly manage bandwidth resources of leased lines from the viewpoint of data transmission, and properly predicting and managing leased line usage becomes a key factor. Therefore, in this study, various prediction models were applied and performance was evaluated based on the actual usage rate data of leased lines used in corporate networks. In general, the performance of each prediction was measured and compared by applying the smoothing model and ARIMA model, which are widely used as statistical methods, and the representative models of deep learning based on artificial neural networks, which are being studied a lot these days. In addition, based on the experimental results, we proposed the items to be considered in order for each model to achieve good performance for prediction from the viewpoint of effective operation of leased line resources.


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Cite this article
[IEEE Style]
I. Lee and M. Song, "Leased Line Traffic Prediction Using a Recurrent Deep Neural Network Model," KIPS Transactions on Software and Data Engineering, vol. 10, no. 10, pp. 391-398, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.10.391.

[ACM Style]
In-Gyu Lee and Mi-Hwa Song. 2021. Leased Line Traffic Prediction Using a Recurrent Deep Neural Network Model. KIPS Transactions on Software and Data Engineering, 10, 10, (2021), 391-398. DOI: https://doi.org/10.3745/KTSDE.2021.10.10.391.