IEEE PIMRC 2005 1

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

NetworkNetwork

Maria PapadopouliMaria Papadopouli Assistant ProfessorAssistant Professor

Department of Computer ScienceDepartment of Computer ScienceUniversity of North Carolina at Chapel Hill (UNC)University of North Carolina at Chapel Hill (UNC)

This work was partially supported by the IBM Corporation under an IBM Faculty Award 2004This work was done while visiting the Institute of Computer Science, Foundation for Research and Technology-Hellas, Greece

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Coauthors & CollaboratorsCoauthors & Collaborators

Felix-Hernandez CamposFelix-Hernandez CamposDepartment of Computer ScienceDepartment of Computer Science

University of North Carolina at Chapel Hill University of North Carolina at Chapel Hill (UNC)(UNC)

Haipeng ShenDepartment of Statistics & Operations ResearchUniversity of North Carolina at Chapel Hill (UNC) USA

Elias Raftopoulos and Manolis Ploumidis Institute of Computer ScienceFoundation for Research & Technology-Hellas Greece

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RoadmapRoadmap

Motivation & Research ObjectivesMotivation & Research Objectives Data AcquisitionData Acquisition Forecasting Methodology Forecasting Methodology Performance of Prediction Performance of Prediction

AlgorithmsAlgorithms ContributionsContributions Future WorkFuture Work

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Motivation & Research Motivation & Research ObjectivesObjectives

MotivationMotivation Wireless traffic models for performance analysis & Wireless traffic models for performance analysis &

simulationssimulations Better load-balancing, admission control, capacity Better load-balancing, admission control, capacity

planning, client supportplanning, client support Access Points (APs) can use the expected traffic estimation Access Points (APs) can use the expected traffic estimation

to decide whether to accept a new associationto decide whether to accept a new association

Research ObjectivesResearch Objectives Analysis of the traffic load at each APAnalysis of the traffic load at each AP Design & evaluation of short-term forecasting algorithms Design & evaluation of short-term forecasting algorithms

for APs for APs Use ofUse of real-measurements from large wireless testbedsreal-measurements from large wireless testbeds

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Data AcquisitionData Acquisition

729-acre campus with 26,000 students, 3,000 729-acre campus with 26,000 students, 3,000 faculty, 9,000 staff faculty, 9,000 staff

Diverse environmentDiverse environment 14,712 unique MAC addresses14,712 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 SNMP polling every AP every 5minutes using a

non-blocking library calls non-blocking library calls Tracing period 63 daysTracing period 63 days Data cleaning follows …

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Hourly Traffic Load (a Hourly Traffic Load (a hotspot AP)hotspot AP)

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Traffic Load Modeling & Traffic Load Modeling & ForecastingForecasting

Time series extraction, cleaning, treatment of missing Time series extraction, cleaning, treatment of missing values, processing of unexpectedly valuesvalues, processing of unexpectedly values

Hourly traffic load of AP i at tHourly traffic load of AP i at tthth hour X hour Xii(t)(t) Power spectrum analysis & partial autocorrelation Power spectrum analysis & partial autocorrelation

analysisanalysis Data normalization & traffic load modelingData normalization & traffic load modeling Forecasting using the aforementioned modelsForecasting using the aforementioned models

General methodology :

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Hourly Traffic Load Hourly Traffic Load

Diurnal patterns Weekly periodicities 10 out of the 19

hotspots have clear diurnal pattern

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Normalizing Hourly Traffic Normalizing Hourly Traffic LoadLoad

)()( 4/1 tXtY X (t)

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SimpleSimple Prediction Algorithms Prediction Algorithms

Prediction based on the Prediction based on the historical hourly mean of the traffic load at each AP of the traffic load at each AP e.g., traffic load during (3pm,4pm] at each day of e.g., traffic load during (3pm,4pm] at each day of

the history the history Prediction based on Prediction based on historical mean hour-of-

day traffic load at each AP traffic load at each AP e.g., traffic load during (3pm,4pm] at each Tuesday e.g., traffic load during (3pm,4pm] at each Tuesday

of the historyof the history Based on Based on recent traffic load at each AP at each AP

e.g., traffic load during the previous three hourse.g., traffic load during the previous three hours

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Prediction Using Historical Means Prediction Using Historical Means & Recent Traffic& Recent Traffic

1

3 )(),()()/1(),()3(t

wtkiiii hcdhbkXwadhZP

Xi(k) : actual (hourly) traffic for AP I at k-th houri(h) : historical hourly mean for AP i at hour hi(h,l) : historical hourly mean for AP i at hour h of day l

Recent history

Historical mean hour-of-day

Historical mean hour

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Prediction Based on Prediction Based on Historical Mean Hour (P1) , Hour-of-Historical Mean Hour (P1) , Hour-of-

Day (P2) Recent Traffic (P3)Day (P2) Recent Traffic (P3)

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Normalize the Transformed Normalize the Transformed Time-SeriesTime-Series

)(

)()()(

th

thtYte

)(

)()()(

th

thtYte

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Normalized Time-Series Forecasting Normalized Time-Series Forecasting (NAMSA)(NAMSA)

Transform traffic load to make data more Transform traffic load to make data more normally distributednormally distributed

Normalize the transform data if mean & Normalize the transform data if mean & variability are time-varyingvariability are time-varying

Develop standard time-series models (eg Develop standard time-series models (eg AR(pAR(p)) )) Employ Employ model selection procedures (eg AIC)

for optimality Perform Perform multiple-step ahead forecastingmultiple-step ahead forecasting

using fitted modelusing fitted model Back-transform the forecast to the original valueBack-transform the forecast to the original value

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Median Prediction Error Median Prediction Error RatioRatio

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ContributionsContributions

Methodology for performing wireless Methodology for performing wireless measurements & forecasting algorithmsmeasurements & forecasting algorithms

Short-term forecasting algorithms based Short-term forecasting algorithms based on recent history, periodicitieson recent history, periodicities

Recent history has larger impact than Recent history has larger impact than the hourly and hour-of-day periodicitiesthe hourly and hour-of-day periodicities

Large variability hard prediction Large variability hard prediction tasktask

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Future WorkFuture Work

More rigorous More rigorous preprocessing of the time-seriespreprocessing of the time-series

e.g., impute entries with unexpectedly low values e.g., impute entries with unexpectedly low values (compared to the historical means with some (compared to the historical means with some estimates)estimates)

Use of Use of flow-basedflow-based information (e.g., start of the information (e.g., start of the flow, type of application) in forecastingflow, type of application) in forecasting

Long-term forecastingLong-term forecasting for capacity planning for capacity planning Comparative analysis of Comparative analysis of diverse wireless testbedsdiverse wireless testbeds UNC/FORTH RepositoryUNC/FORTH Repository wireless measurements wireless measurements

& models repository & models repository

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More InfoMore Info

http://www.cs.unc.edu/~mariahttp://www.cs.unc.edu/~maria

http://www.ics.forth.gr/mobile/http://www.ics.forth.gr/mobile/

maria@cs.unc.edumaria@cs.unc.edu

Thank You!Thank You!

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Mean Prediction Error RatiosMean Prediction Error RatiosHistorical Mean Hour, Hour-of-Day, Historical Mean Hour, Hour-of-Day,

Recent TrafficRecent Traffic