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Wireless Networks
Erschienen in: Wireless Networks 8/2020

22.07.2020

Network traffic prediction based on INGARCH model

verfasst von: Meejoung Kim

Erschienen in: Wireless Networks | Ausgabe 8/2020

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Abstract

In this paper, we introduce the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) as a network traffic prediction model. As the INGARCH is known as a non-linear analytical model that could capture the characteristics of network traffic such as Poisson packet arrival and long-range dependence property, INGARCH seems to be an adequate model for network traffic prediction. Based on the investigation for the traffic arrival process in various network topologies including IoT and VANET, we could confirm that assuming the Poisson process as packet arrival works for some networks and environments of networks. The prediction model is generated by estimating parameters of the INGARCH process and predicting the Poisson parameters of future-steps ahead process using the conditional maximum likelihood estimation method and prediction procedure, respectively. Its performance is compared with those of three different models; autoregressive integrated moving average, GARCH, and long short-term memory recurrent neural network. Anonymized passive traffic traces provided by the Center for Applied Internet Data Analysis are used in the experiment. Numerical results show that the proposed model predicts better than the three models in terms of measurements used in prediction models. Based on the study, we can conclude the followings: INGARCH can capture the characteristics of network traffic better than other statistic models, it is more tractable than neural networks (NNs) overcoming the black-box nature of NNs, and the performances of some statistical models are comparable or even superior to those of NNs, especially when the data is insufficient to apply deep NNs.
Vorheriger Artikel Gradient-based adaptive modeling for IoT data transmission reduction
Nächster Artikel QoS aware cross layer paradigm for urban development applications in IoT

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Literatur
1.
Zhan, M. F., & Elbiaze, H. (2009). Analysis and prediction of real network traffic. Journal of Networks, 4(9), 855–865.
2.
3.
Amiri, M., & Mohammad-Khanli, L. (2017). Survey on prediction models of applications for resources provisioning in Cloud. Journal of Network and Computer Applications, 82, 93–113.CrossRef
4.
Chakraborty, D., Ashir, A., Suganuma, T., Keeni, G. M., Roy, T. K., & Shiratori, N. (2004). Self-similar and fractal nature of Internet traffic. International Journal of Network Management, 12(2), 119–129.CrossRef
5.
Karagiannis, T., Molle, M., & Faloutsos, M. (2004). Long-range dependence ten years of Internet traffic modeling. IEEE Internet Computing, 8(5), 57–64.CrossRef
6.
Cao, J., Cleveland, W. S., Lin, D., & Sun, D. X. (2001). On the nonstationarity of Internet traffic. In Proceedings of ACM SIGMETRICS (pp. 102–112).
7.
Hoßfeld, T., Metzger, F., & Heegaard, P. E. (2018). Traffic modeling for aggregated periodic IoT data. In Proceedings of ICIN (pp. 1–8).
8.
Koukoutsidis, I. & Siris, V. A. (2007). Modeling approximations for an IEEE 802.11 WLAN Under Poisson MAC-Level Arrivals. In Proceedings of networking (pp. 439–449).
9.
: Oh, Y. & Hwang, G. (2018). Spatial modelling and analysis of WLAN with Poisson point process. In Proceedings of QTNA (pp. 145–159).
10.
Guo, J., Zhang, Y., Chen, X., & Wang, Y. (2017). Spatial stochastic vehicle traffic modeling for VANETs. IEEE Transactions on Intelligent Transportation Systems, 99, 1–10.
11.
Heath, R. W., Kountouris, M., & Bai, T. (2013). Modeling heterogeneous network interference using Poisson point processes. IEEE Transactions on Signal Processing, 61(16), 4114–4126.MathSciNetMATHCrossRef
12.
Gulati, K., Evans, B. L., Andrews, J. G., & Tinsley, K. R. (2010). Statistics of co-channel interference in a field of Poisson and Poisson–Poisson clustered interferers. IEEE Transactions on Signal Processing, 58(12), 6207–6222.MathSciNetMATHCrossRef
13.
Suarez, C. A. H., Parra, O. J. S., & Díaz, A. E. (2009). An ARIMA model for forecasting Wi-Fi data network traffic values. Revista Ingenieria E Investigacion, 29(2), 65–69.
14.
Laner, M., Svoboda, P., & Rupp, M. (2013). Parsimonious fitting of long-range dependent network traffic using ARMA models. IEEE Communications Letters, 17(12), 2368–2371.CrossRef
15.
Xu, S., & Zeng, B. (2014). Network traffic prediction model based on auto-regressive moving average. Journal of Networks, 9(3), 653–659.CrossRef
16.
Zhou, D., Chen, S., & Dong, S. (2012). Network traffic prediction based on ARFIMA model. International Journal of Computer Science Issues, 9(6, 3), 106–111.
17.
Jiang, D., & Hu, G. (2009). GARCH model-based large-scale IP traffic matrix estimation. IEEE Communications Letters, 13(1), 52–54.MathSciNetCrossRef
18.
Ntlangu, B., & Baghai-Wadji, A. (2017). Modelling network traffic using time series analysis: A Review. In Proceedings of BDIoT (pp. 209–215).
19.
Kim, S. (2011). Forecasting Internet traffic by using seasonal GARCH models. Journal of Communications and Networks, 13(6), 621–624.CrossRef
20.
Chen, C., Hu, J., Meng, Q., & Zhang, Y. (2011). Short-time traffic flow prediction with ARIMA-GARCH Model. In Proceedings of IEEE IV (pp. 607–612).
21.
Zeng, D., Xu, J., Gu, J., Liu, L., & Xu, G. (2008) Short term traffic flow prediction using hybrid ARIMA and ANN models. In Proceedings of PEITS (pp. 621–625).
22.
Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11, 2664–2675.CrossRef
23.
Babu, C. N., & Reddy, B. E. (2015). Performance comparison of four new ARIMA-ANN prediction models on Internet traffic data. Journal of Telecommunications and Information Technology, 1, 67–75.
24.
Li, K. -L., Zhai, C. -J., & Xu, J. -M. (2017). Short-term traffic flow prediction using a methodology based on ARIMA and RBF-ANN. In Proceedings of CAC (pp. 2804–2807).
25.
Katris, C., & Daskalaki, S. (2015). Comparing forecasting approaches for Internet traffic. Expert Systems with Applications, 42, 1872–8183.MATHCrossRef
26.
Chabaa, S., Zeroual, A., & Antari, J. (2010). Identification and prediction of internet traffic using artificial neural networks. Journal of Intelligent Learning Systems and Applications, 2, 147–155.CrossRef
27.
Luo, Y. (2015). Network traffic prediction based on LMD and neural network. In Proceedings of ICMMITA (pp. 371–374).
28.
Zhuo, Q., Li, Q., Yan, H., & Qi, Y. (2017). Long short-term memory neural network for network traffic prediction. In Proceedings of ISKE (pp. 1–6).
29.
Li, R., Zhao, Z., Zheng, J., Mei, C., Cai, Y., & Zhang, H. (2017). The learning and prediction of application-level traffic data in cellular networks. IEEE Transactions on Wireless Communications, 16(6), 3899–3912.CrossRef
30.
Qiu, C., Zhang, Y., Feng, Z., Zhang, P., & Cui, S. (2018). Spatio-temporal wireless traffic prediction with recurrent neural network. IEEE Wireless Communications letters, 7(4), 554–557.CrossRef
31.
Dalgkitsis, A., Louta, M., & Karetsos, G. T. (2018). Traffic forecasting in cellular networks using the LSTM RNN. In Proceedings of PCI (pp. 28–33).
32.
Alawe, I., Ksentini, A., Hadjadj-Aoul, Y., & Bertin, P. (2018). Improving traffic forecasting for 5G core network scalability: A machine learning approach. IEEE Network, 32(6), 42–49.CrossRef
33.
Mozo, A., Ordozgoiti, B., & Gómez-Canaval, S. (2018). Forecasting short-term data center network traffic load with convolutional neural networks. PLoS ONE, 13(2), e0191939.CrossRef
34.
Nie, L., Wang, X., Wan, L., Yu, S., Song, H., & Jiang, D. (2018). Network traffic prediction based on deep belief network and spatiotemporal compressive sensing in wireless mesh backbone networks. Wireless Communications and Mobile Computing, Article ID 1260860, 10 pages.
35.
Kim, M. (2019). Supervised learn-based DDoS attacks detection: Tuning hyperparameters. ETRI Journal, 41(5), 560–573.CrossRef
36.
Chen, Z., Wen, J., & Geng, Y. (2016). Predicting future traffic using hidden Markov models. In Proceedings of IEEE ICNP (pp. 1–6).
37.
Maheshwari, S., Mahapatra, S., Kumar, C. S., & Vasu, K. (2013). A joint parametric prediction model for wireless internet traffic using Hidden Markov Model. Wireless Networks, 19(6), 1171–1185.CrossRef
38.
Li, R., Zhao, Z., Zhou, X., Palicot, J., & Zhang, H. (2014). The prediction analysis of cellular radio access network traffic: From entropy theory to networking practice. IEEE Communications Magazine, 52(6), 234–240.CrossRef
39.
Bayati, A., Nguyen, K., & Cheriet, M. (2018). Multiple-step-ahead traffic prediction in high-speed networks. IEEE Communications Letters, 22(12), 244–2450.CrossRef
40.
Tran, Q. T., Li, H., & Trinh, Q. K. (2019). Cellular network traffic prediction using exponential smoothing methods. Journal of Information and Communications Technology, 18(1), 1–18.CrossRef
41.
Iqbal, M. F., Zahid, M., Habib, D., & John, L. K. (2019). Efficient prediction of network traffic for real-time applications. Journal of Computer Networks and Communications, Article ID 4067135, 11 pages.
42.
43.
Doukhan, P., Oppenheim, G., & Taqqu, M. (Eds.). (2003). Theory and applications of long-range dependence. Basel: Birkhäuser.MATH
44.
Maheu, J. (2007). Can GARCH models capture long-range dependence? Studies in Nonlinear Dynamics & Econometrics, 9(4), 1–41.MathSciNetMATH
45.
Sun, W. Rachev, S. Z., & Fabozzi, F. (2008). Long-range dependence, fractal processes, and intra daily data. In D. Seese, C. Weinhardt, and F. Schlottmann (Eds.), Handbook on Information Technology in Finance (pp. 543–585).
46.
Wang, X., Smith-Miles, K., & Hyndman, R. J. (2009). Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series. Neurocomputing, 72(10), 2581–2594.CrossRef
47.
Lemke, C., & Gabrys, B. (2010). Meta-learning for time series forecasting and forecast combination. Neurocomputing, 73(10), 2006–2016.CrossRef
48.
Widodo, A. & Budi, I. (2013). Model selection using dimensionality reduction of time series characteristics. In Proceedings of ISF (pp. 1–8).
49.
Kück, M., Crone, S., & Freitag, M. (2016). Meta-learning with neural networks and landmarking for forecasting model selection—An empirical evaluation of different feature sets applied to industry data. In Proceedings of IJCNN (pp. 1499–1506).
50.
Talagala, T. S., Hyndman, R. J., & Athanasopoulos, G. (2018). Meta-learning how to forecast time series. Melbourne: Monash University.
51.
Barak, S., Nasiri, M., & Rostamzadeh, M. (2019). Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms (pp. 1–30). e-print arXiv:​1908.​08489v1.
52.
Afolabi, D., Guan, S.-U., Man, K. L., Wong, P. W. H., & Zhao, X. (2017). Hierarchical meta-learning in time series forecasting for improved interference-less machine learning. Symmetry, 9, 283.CrossRef
53.
Quoreshi, A. M. M. S. (2014). A long-memory integer-valued time series model, INARFIMA, for financial application. Quantitative Finance, 14(12), 2225–2235.MathSciNetMATHCrossRef
54.
Ferland, R., Latour, A., & Oraichi, D. (2006). Integer-valued GARCH process. Journal of Time Series Analysis, 27(6), 923–942.MathSciNetMATHCrossRef
55.
56.
Cui, Y., & Wu, R. (2016). On conditional maximum likelihood estimation for INGARCH(p, q) models. Statistics and Probability Letters, 118, 1–7.MathSciNetMATHCrossRef
57.
Bollerslev, T., Chou, R. Y., & Kroner, K. F. (1992). ARCH modeling in finance: A review of the theory and empirical evidence. Journal of Econometrics, 52, 5–59.MATHCrossRef
58.
59.
Metadaten
Titel
Network traffic prediction based on INGARCH model
verfasst von
Meejoung Kim
Publikationsdatum
22.07.2020
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 8/2020
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-020-02431-y

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