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Modeling client arrivals at access points in wireless campus-wide networks. Maria Papadopouli Assistant Professor Department of Computer Science University of North Carolina at Chapel Hill (UNC). - PowerPoint PPT Presentation
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Modeling client arrivals at access points in wireless campus-wide networks
Maria Papadopouli Assistant Professor Department of Computer ScienceUniversity of North Carolina at Chapel Hill (UNC)
This work was partially supported by the IBM Corporation under an IBM Faculty Award 2004
It was done while visiting the Institute of Computer Science, Foundation for Research and Technology-Hellas, Greece
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Coauthors And Collaborators
Haipeng ShenDepartment of Statistics & Operations ResearchUniversity of North Carolina at Chapel Hill (UNC)
Spanakis ManolisInstitute of Computer Science
Foundation for Research and Technology - Hellas
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Roadmap
Motivation & Research Objective Summary of main contributions Methodology Modeling the client arrival Clustering of access Points (APs) Future Work
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Motivation & Research Objective
Better admission control, load balancing, capacity planning mechanisms
More realistic access models for simulations & performance analysis studies
Evolution of wireless access
Model client arrivals at wireless APs
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Data Set
729-acre campus: 26,000 students, 3,000 faculty, 9,000 staff Diverse environment 14,712 unique MAC addresses 488 APs (Cisco 1200, 350, 340 Series) Syslog traces Tracing period: 29 September-25 November 2005
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Main Contributions
Novel methodology for modeling client arrivals at wireless APs
Model of client arrivals at APs as time-varying Poisson process
Use of SiZer visualization tool to understand the internal structures of traces
Clustering of visit arrivals based on building type
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SiZerMap of Visit Start Times (AP222)
increasing trend
decreasing trend
constant
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Visit Inter-arrival Times (17:30-18:30)
decreasing trend
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Visit Inter-arrival Times (Uniform Noise Added)
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Background on Poisson Process Stochastic point process
that counts the number of events in [0,t]
• Arrival rate • Renewal process with inter-arrival times independent exponential
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Analysis of Inter-arrival Times
Strong autocorrelation of inter-arrival times cannot model visit arrival as a renewal process with independent Weibull inter-arrival times
Simulation envelopesampling variability
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Time-varying Poisson Process
Arrival rate: function of time, λ(t)
Smooth variation of λ(t) is familiar in both theory and practice in a wide variety of contexts
(e.g. when driven by human behaviors)
Seems reasonable for client arrivals
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Construction of a Statistical Test Null hypothesis The arrival process is a time-varying Poisson process
with a slowly varying arrival rate
Break up the interval of a day into short blocks (i=1,..,24) Show that the null hypothesis cannot be rejected Define (i slot, j arrival)
• Under the null hypothesis Rij will be independent standard exponential variable
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Testing the Null Hypothesis
Show the exponentiality of Rij
Apply Kolmogorov-Smirnov test
Based on the maximum deviation between the empirical cumulative distribution & hypothesized theoretical CDF
Graphical tools
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Kolmogorov-Smirnov Test
The test statistic is 0.0188 p-value of 0.15 with 2143 observations
p-value is large
The null-hypothesis can not be rejected
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Exponentiality of Rij for [17:30, 18:30]
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Validation of Time-varying Poisson Models
Repeated the analysis and got similar results
We analyzed A few other hours at AP 222 (academic) Three other hotspot APs of other building
types (library, theater, residential)
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Clustering Based on Building Types & Client Arrivals
Aggregate Hourly Percentage of visitsO 25-th percentilex Median Std. Deviation
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Summary
Novel methodology for modeling the arrival of clients at APs
Time-Varying Poisson processes model well the client arrivals at APs
Validation of the models for different hours of day and different APs
Cluster of APs based on the building type and load of arrivals
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Future Work
Model flow arrivals & cluster them based on client profile, mobility & AP
Provide guidelines for load balancing, capacity planning & energy conservation
Enhance traffic forecasting using flow information Validate model with traces from other wireless
networks Contrast models from different wireless environments
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More Info
http://www.cs.unc.edu/~maria http://www.ics.forth.gr/mobile/ [email protected]
Thank You!