Abstract
With scientific data growing to unprecedented volumes and the needs to share such massive amounts of data by increasing numbers of geographically distributed collaborators, the best possible network performance is required for efficient data access. Estimating the network traffic performance for a given time window with a probabilistic tolerance enables better data routing and transfers that is particularly important for large scientific data movements, which can be found in almost every scientific domain. In this paper, we develop a network performance estimation model based on statistical time series approach, to improve the efficiency of network resource utilization and data transfer scheduling and management over networks. Seasonal adjustment procedures are developed for identification of the cycling period and patterns, seasonal adjustment and diagnostics. Compared to the traditional time series models, we show a better forecast performance in our seasonal adjustment model with narrow confidence intervals.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alarcon-Aquino, V., Barria, J.A.: Multiresolution FIR Neural-Network-Based Learning Algorithm Applied to Network Traffic Prediction. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews 36(2), 208–220 (2006)
Box, G., Jenkins, G.: Time series analysis: Forecasting and control. Wiley Series in Probability and Statistics (1970)
Cleveland, W.S.: STL: A Seasonal-Trend Decomposition Procedure Based on Loess. Journal of Official Statistics 6(1), 3–73 (1990)
Durbin, J., Koopman, S.J.: Time Series Analysis by State Space Methods. Oxford University Press (2001)
Feng, H., Shu, Y.: Study on Network Traffic Prediction Techniques. In: Proceedings of International Conference on Wireless Communications, Networking and Mobile Computing, vol. 2, pp. 1041–1044 (2005)
Findley, D.F., Hood, C.H.: Seasonal Adjustment Procedures. In: Experiences and Perspectives, Istituto Nazionale di Statistica, Rome, pp. 231–251 (2000)
Findley, D.F.: Some Recent Developments and Directions in Seasonal Adjustment. Journal of Official Statistics 21(2) (2005)
Findley, Monsell, Bell, Otto, Chen: New Capabilities and Methods of the X-12-ARIMA Seasonal Adjustment Program. Preprint version of JBES (1998)
Findley, M., Shulman, P.: Sliding Spans Diagnostics for Seasonal and Related Adjustments. Journal of the American Statistical Association 85, 345–355 (1990)
Granger, C.W.J.: Seasonality: Causation, Interpretation, and Implications. In: Zellner, A. (ed.) NBER Book Seasonal Analysis of Economic Time Series, pp. 33–56 (1979)
Harvey, A.C.: Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press (1989)
Higginson, J.: F-test for the Presence of Moving Seasonality. Statistics Canada, Seasonal Adjustment Methods (1975)
Holt, C.C.: Forecasting trends and seasonals by exponentially weighted moving averages. International Journal of Forecasting 20(1), 5–10 (1957)
Hood, C.H., Monsell, B.C.: Getting Started with X-12-ARIMA Input Files on Your PC (2002), http://www.census.gov/ts/papers/gettingstartedx12.pdf
ESnet, http://stats.es.net
Hyndman, R.J., Akram, M.D., Archibald, B.: The admissible parameter space for exponential smoothing models. Annals of Statistical Mathematics 60(2), 407–426 (2008)
Lothian, J., Morry, M.: A Test of Quality Control Statistics for the X-11-ARIMA Sea-sonal Adjustment Program. Research Paper. Seasonal Adjustment and Time Series Staff. Statistics Canada (1978)
Qian, Y., Xia, J., Fu, K., Zhang, R.: Network Traffic Forecasting by Support Vector Machines Based on Empirical Mode Decomposition Denoising. In: 2012 2nd International Conference on Consumer Electronics, Communications and Networks, CECNet (2012)
Seasonal Adjustment Diagnostics and Supporting Document A (Checklists). Census Bureau, https://www.census.gov/ts/papers/G18-0_v1.1_Seasonal_Adjustment.pdf
Winters, P.R.: Forecasting sales by exponentially weighted moving averages. Management Science 6, 324–342 (1960)
X-12-ARIMA Seasonal Adjustment Program, http://www.census.gov/srd/www/x12a/
X-12-ARIMA Version 0.3 Reference Manual (2006), http://www.census.gov/ts/x12a/v03/x12adocV03.pdf
Zhao, H., Ansari, N.: Wavelet Transform-based Network Traffic Prediction: A Fast On-line Approach. Journal of Computing and Information Technology 20(1), 15–25 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hu, K., Sim, A., Antoniades, D., Dovrolis, C. (2013). Estimating and Forecasting Network Traffic Performance Based on Statistical Patterns Observed in SNMP Data. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-39712-7_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39711-0
Online ISBN: 978-3-642-39712-7
eBook Packages: Computer ScienceComputer Science (R0)