Browse > Article
http://dx.doi.org/10.3745/KTSDE.2021.10.10.391

Leased Line Traffic Prediction Using a Recurrent Deep Neural Network Model  

Lee, In-Gyu (세명대학교 정보통신학부)
Song, Mi-Hwa (세명대학교 정보통신학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.10, 2021 , pp. 391-398 More about this Journal
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.
Keywords
Leased Line; Traffic Modeling; Time Series Analysis; Deep Learning; RNN; LSTM;
Citations & Related Records
연도 인용수 순위
  • Reference
1 I. Aijaz and P. Agarwal, "A study on time series forecasting using hybridization of time series models and neural networks," Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) Vol.13, No.5, pp.827-832, 2020.
2 I. G. Lee and M. H. Song, "A comparative study of statistical techniques and machine learning models for efficient leased line resource usage prediction," Proceedings of the KIPS, Vol.28., No.1, pp.474-476, 2021.
3 L. G. Roberts and B. D. Wessler, "Computer network development to achieve resource sharing," Proceedings of the May 5-7, 1970, Spring Joint Computer Conference, 1970.
4 H. M. Sigurdsson, S. E. Thorsteinsson, and T. K. Stidsen. "Cost optimization methods in the design of next generation networks," IEEE Communications Magazine, Vol.42, No.9, pp.118-122, 2004.
5 Statistical Office, "Business Basic Statistical Survey Report," Each Year (2020).
6 M. Joshi and T. H. Hadi, "A review of network traffic analysis and prediction techniques," arXiv preprint arXiv: 1507.05722, 2015.
7 S. J. Jung, D. J. Kim, Y. H. Know, and C. G. Kim, "A fitness verification of time series models for network traffic predictions," The Journal of Korea Information and Communications Society, Vol.29, No.2B, pp.217-227, 2004.
8 S. H. Ji, H. Hasanova, K. S. Shim, and M. S. Kim, "Prediction of traffic usage using machine learning algorithm for efficient network management," Proceedings of Symposium of the Korean Institute of communications and Information Sciences, pp.824-825, 2018.
9 W. Stallings, "SNMP and SNMPv2: the infrastructure for network management," IEEE Communications Magazine, Vol.36, No.3, pp.37-43, 1998.   DOI
10 K. McCloghrie and M. T. Rose, "RFC1213: Management information base for network management of TCP/IP-based internets: MIB-II," 1991.
11 M. T. Rose and K. McCloghrie, "RFC1155: Structure and identification of management information for TCP/IP-based internets," 1990.
12 R. G. Brown and R. F. Meyer, "The fundamental theorem of exponential smoothing," Operations Research, Vol.9, No.5, pp.673-685, 1961.   DOI
13 W. Yoo and A. Sim. "Time-series forecast modeling on high-bandwidth network measurements," Journal of Grid Computing, Vol.14, No.3, pp.463-476, 2016.   DOI
14 R. J. Hyndman and G. Athanasopoulos, "Forecasting: Principles and practice," OTexts, 2018. [Internet], Available from: https://otexts.com/fpp2
15 Colah's Blog, Understanding LSTM Networks [Internet], Available from: http://colah.github.io/posts/2015-08-Understanding-LSTMs/(2015)
16 R. Kumar, P. Kumar, and Y. Kumar, "Time series data prediction using iot and machine learning technique," Procedia Computer Science, Vol.167, pp.373-381, 2020.   DOI
17 A. Sherstinsky, "Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network," Physica D: Nonlinear Phenomena, Vol.404, pp.132306, 2020.   DOI
18 S. Hochreiter, and J. Schmidhuber, "Long short-term memory," Neural Computation, Vol.9, No.8, pp.1735-1780, 1997.   DOI
19 F. A. Gers, J. Schmidhuber, and F. Cummins, "Learning to forget: Continual prediction with LSTM," Neural Computation, Vol.12, No.10, pp.2451-2471, 2000.   DOI
20 B. Lim and S. Zohren, "Time-series forecasting with deep learning: A survey," Philosophical Transactions of the Royal Society A, Vol.379, No.2194, pp.20200209, 2021.   DOI
21 J. Zhao, et al., "Do rnn and lstm have long memory?," International Conference on Machine Learning, PMLR, 2020.
22 H. W. Taek, A. S. Jin, and C. J. Wook, "Forecasting technique of line utilization based on SNMP MIB-II using time series analysis," KIPS Journal, Vol.6, No.9, pp.2470-2478, 1999. DOI: 10.3745/KIPSTE.1999.6.9.2470.   DOI
23 J. D. Case, M. Fedor, M. L., Schoffstall, and J. Davin, "RFC1157: Simple network management protocol (snmp)," 1990.
24 G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, "Time series analysis: Forecasting and control," John Wiley & Sons, 2015.