Estimating and Forecasting Network Traffic Performance Based on Statistical Patterns Observed in SNMP Data

  • Kejia Hu
  • Alex Sim
  • Demetris Antoniades
  • Constantine Dovrolis
Conference paper

DOI: 10.1007/978-3-642-39712-7_46

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7988)
Cite this paper as:
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

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.

Keywords

Time series Seasonal Adjustment Network Traffic Forecast STL X12-ARIMA 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kejia Hu
    • 1
    • 2
  • Alex Sim
    • 1
  • Demetris Antoniades
    • 3
  • Constantine Dovrolis
    • 3
  1. 1.Lawrence Berkeley National LaboratoryUSA
  2. 2.University of California at DavisUSA
  3. 3.Georgia Institute of TechnologyUSA

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