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Short-term Traffic Forecasting in a Campus-Wide Wireless Network

Short-term Traffic Forecasting in a Campus-Wide Wireless Network. Maria Papadopouli Assistant Professor Department of Computer Science University of North Carolina at Chapel Hill (UNC). This work was partially supported by the IBM Corporation under an IBM Faculty Award 2004

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Short-term Traffic Forecasting in a Campus-Wide Wireless Network

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  1. Short-term Traffic Forecasting in a Campus-Wide Wireless Network Maria Papadopouli Assistant Professor Department of Computer Science University of North Carolina at Chapel Hill (UNC) This work was partially supported by the IBM Corporation under an IBM Faculty Award 2004 This work was done while visiting the Institute of Computer Science, Foundation for Research and Technology-Hellas, Greece IEEE PIMRC 2005

  2. Haipeng Shen Department of Statistics & Operations Research University of North Carolina at Chapel Hill (UNC) USA Coauthors & Collaborators Elias Raftopoulos and Manolis Ploumidis Institute of Computer Science Foundation for Research & Technology-Hellas Greece Felix-Hernandez Campos Department of Computer Science University of North Carolina at Chapel Hill (UNC) IEEE PIMRC 2005

  3. Roadmap • Motivation & Research Objectives • Data Acquisition • Forecasting Methodology • Performance of Prediction Algorithms • Contributions • Future Work IEEE PIMRC 2005

  4. Motivation & Research Objectives Motivation • Wireless traffic models for performance analysis & simulations • Better load-balancing, admission control, capacity planning, client support • Access Points (APs) can use the expected traffic estimation to decide whether to accept a new association Research Objectives • Analysis of the traffic load at each AP • Design & evaluation of short-term forecasting algorithms for APs Use ofreal-measurements from large wireless testbeds IEEE PIMRC 2005

  5. Data Acquisition • 729-acre campus with 26,000 students, 3,000 faculty, 9,000 staff • Diverse environment • 14,712 unique MAC addresses • 488 APs (Cisco 1200, 350, 340 Series) • SNMP polling every AP every 5minutes using a non-blocking library calls • Tracing period 63 days Data cleaning follows … IEEE PIMRC 2005

  6. Hourly Traffic Load (a hotspot AP) IEEE PIMRC 2005

  7. Traffic Load Modeling & Forecasting • Time series extraction, cleaning, treatment of missing values, processing of unexpectedly values Hourly traffic load of AP i at tth hour Xi(t) • Power spectrum analysis & partial autocorrelation analysis • Data normalization & traffic load modeling • Forecasting using the aforementioned models General methodology : IEEE PIMRC 2005

  8. Hourly Traffic Load • Diurnal patterns • Weekly periodicities • 10 out of the 19 hotspots have clear diurnal pattern IEEE PIMRC 2005

  9. Normalizing Hourly Traffic Load X (t) IEEE PIMRC 2005

  10. Simple Prediction Algorithms • Prediction based on the historical hourly mean of the traffic load at each AP • e.g., traffic load during (3pm,4pm] at each day of the history • Prediction based on historical mean hour-of-day traffic load at each AP • e.g., traffic load during (3pm,4pm] at each Tuesday of the history • Based on recent traffic load at each AP • e.g., traffic load during the previous three hours IEEE PIMRC 2005

  11. Prediction Using Historical Means & Recent Traffic Xi(k) : actual (hourly) traffic for AP I at k-th hour mi(h) : historical hourly mean for AP i at hour h mi(h,l) : historical hourly mean for AP i at hour h of day l Historical mean hour-of-day Recent history Historical mean hour IEEE PIMRC 2005

  12. Prediction Based on Historical Mean Hour (P1) , Hour-of-Day (P2) Recent Traffic (P3) IEEE PIMRC 2005

  13. Normalize the Transformed Time-Series IEEE PIMRC 2005

  14. Normalized Time-Series Forecasting (NAMSA) • Transform traffic load to make data more normally distributed • Normalize the transform data if mean & variability are time-varying • Develop standard time-series models (eg AR(p)) • Employ model selection procedures (eg AIC) for optimality • Perform multiple-step ahead forecasting using fitted model • Back-transform the forecast to the original value IEEE PIMRC 2005

  15. Median Prediction Error Ratio IEEE PIMRC 2005

  16. Contributions • Methodology for performing wireless measurements & forecasting algorithms • Short-term forecasting algorithms based on recent history, periodicities • Recent history has larger impact than the hourly and hour-of-day periodicities • Large variability hard prediction task IEEE PIMRC 2005

  17. Future Work • More rigorous preprocessing of the time-series e.g., impute entries with unexpectedly low values (compared to the historical means with some estimates) • Use of flow-based information (e.g., start of the flow, type of application) in forecasting • Long-term forecasting for capacity planning • Comparative analysis of diverse wireless testbeds • UNC/FORTH Repository wireless measurements & models repository IEEE PIMRC 2005

  18. More Info • http://www.cs.unc.edu/~maria • http://www.ics.forth.gr/mobile/ • maria@cs.unc.edu Thank You! IEEE PIMRC 2005

  19. Mean Prediction Error RatiosHistorical Mean Hour, Hour-of-Day, Recent Traffic IEEE PIMRC 2005

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