1. IEEE PIMRC 2005 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

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

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

4. 4IEEE PIMRC 2005 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 of real-measurements from large wireless testbeds Use of real-measurements from large wireless testbeds

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

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

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

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

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

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

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

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

13. 13IEEE PIMRC 2005 Normalize the Transformed Time-Series

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

15. 15IEEE PIMRC 2005 Median Prediction Error Ratio

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

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

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

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