Performance of multivariable traffic model that allows estimating Throughput mean values
AbstractThe present paper is aimed at developing a multi-variable traffic model of a Wi-Fi data network that allows estimating throughput mean values. In order to construct the model, data corresponding to an 8-host wireless adhoc network were collected using a software package called WireShark; the network was specially designed for modeling purposes. Subsequently, the most convenient multi-variable models were estimated according to the traffic features extracted from the collected data. Results were the evaluated using a software package called STATA, leading to the establishment of significant explanatory variables for the model and its performance levels. For our Wi-Fi network, results show that the analyzed traffic exhibits selfsimilarity features. Additionally, model coefficients and their corresponding significance levels are shown in various Tables. Finally, an explanatory multivariable model consisting of four variables was produced on the basis of ordinary least-squares methodologies (with a per-cent error of 22.16). The findings suggest that the multi-variable traffic model produced in this study allows a reliable analysis of throughput mean values; however, the model is limited when predicting traffic values for data outside the selected estimation set.
C. Hernández, O. Salcedo, A. Escobar. “An ARIMA model for forecasting Wi-Fi data networks traffic values”. Revista Ingeniería e Investigación. Vol. 29. 2009. pp. 65-69.
M. Alzate. “Modelos de tráfico en análisis y control de redes de Comunicaciones”. Colombia Ingeniería. Vol. 9. 2004. pp. 63-87.
M. Tiep, G. Bidisha, W. Simon. “Multivariate short-term traffic flow forecasting using Bayesian vector autoregressive moving average model”. Transportation Research Board. Vol. 12. 2012. pp. 16.
M. Papadopouli, H. Shen, E. Raftopuulos, M. Ploumidis, F. Hernández. Short-term traffic forecasting in a campus-wide girdles network. in IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications. Berlin, Germany. 2005. pp. 1446-1452
D. Mark, R. McDonald, R. Antoon. Aspectos Básicos de Networking. Ed. Cisco Press. Madrid, España. 2008. pp. 606.
P. Fierens. Introducción a las Redes Wi-Fi. Instituto Tecnológico de Buenos Aires. Boletín electrónico: Comisión Interamericana de Telecomunicaciones. No. 14. Agosto. 2005.
N. Torres, L. Pedraza, C. Hernández. “Redes neuronales y predicción de tráfico”. Tecnura. Vol. 15. 2011. pp. 90-97.
J. Sa Silva, R. Ruivo, T. Camilo. IP in wireless sensor networks Issues and lessons learnt. In Proceedings of the 3rd International Conference on Communication Systems Software and Middleware and Workshops. Bangalore, India. 2008. pp. 496-502.
A. Feria. Modelo OSI. Ed. El Cid Editor. Santa Fe, Argentina. 2009. pp. 14.
G. Box, G. Jenkins, G. Reinsel. “Forecasting and Control”. Time Series Analysis. 3rd ed. Ed. PrenticeHall. Englewood Cliffs. San Francisco, USA. pp. 75. 1994.
Y. Chia, J. Shyu. “Applying multivariate time series models to technological product sales forecasting”. International Journal of Technology Management. Vol. 27. 2004. pp. 306-319.
M. Ariño, P. Franses. “Forecasting the levels of vector autoregressive log-transformed time series”. International Journal of Forecasting. Vol. 16. 1999. pp. 111-116.
J. Carroll, C. Hernández. “Comparación del modelo FARIMA y SFARIMA para obtener la mejor estimación del tráfico en una red Wi-Fi”. Tecnura. Vol. 16. 2012. pp. 84-90.
A. Dainotti, A. Pescapé, P. Rossi, F. Palmieri, G. Ventre. “Internet traffic modeling by means of Hidden Markov Models”. Computer Networks. Vol. 52. 2008. pp. 2645-2662.
K. Balaji. Forecasting models and adaptive quantized bandwidth provisioning for nonstationary network traffic. PhD. Dissertation. University of Missouri. Kansas City, United States. 2006. pp. 173.
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