1 / 22

Modeling client arrivals at access points in wireless campus-wide networks

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).

lotta
Download Presentation

Modeling client arrivals at access points in wireless campus-wide networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Modeling client arrivals at access points in wirelesscampus-wide networks 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 It was done while visiting the Institute of Computer Science, Foundation for Research and Technology-Hellas, Greece

  2. Coauthors And Collaborators Haipeng Shen Department of Statistics & Operations Research University of North Carolina at Chapel Hill (UNC) Spanakis Manolis Institute of Computer Science Foundation for Research and Technology - Hellas IEEE Lanman'05

  3. Roadmap • Motivation & Research Objective • Summary of main contributions • Methodology • Modeling the client arrival • Clustering of access Points (APs) • Future Work IEEE Lanman'05

  4. 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 IEEE Lanman'05

  5. IEEE Lanman'05

  6. 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 IEEE Lanman'05

  7. 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 buildingtype IEEE Lanman'05

  8. SiZerMap of Visit Start Times (AP222) increasing trend decreasing trend constant IEEE Lanman'05

  9. Visit Inter-arrival Times (17:30-18:30) decreasing trend IEEE Lanman'05

  10. Visit Inter-arrival Times(Uniform Noise Added) IEEE Lanman'05

  11. Stochastic point process that counts the number of events in [0,t] Background on Poisson Process • Arrival rate l • Renewal process with inter-arrival timesindependent exponential IEEE Lanman'05

  12. Analysis of Inter-arrival Times Simulation envelope sampling variability  Strong autocorrelation of inter-arrival times  cannot model visit arrival as a renewal process with independent Weibull inter-arrival times IEEE Lanman'05

  13. Time-varying Poisson Process • Arrival rate: function of time, λ(t) Smoothvariation ofλ(t) is familiar in both theory and practice in awide variety of contexts (e.g. when driven by human behaviors)  Seems reasonable for client arrivals IEEE Lanman'05

  14. Construction of a StatisticalTest • 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 IEEE Lanman'05

  15. 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 IEEE Lanman'05

  16. 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 IEEE Lanman'05

  17. Exponentiality of Rij for [17:30, 18:30] IEEE Lanman'05

  18. 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) IEEE Lanman'05

  19. O 25-th percentile x Median  Std. Deviation Clustering Based on Building Types & Client Arrivals Aggregate Hourly Percentage of visits IEEE Lanman'05

  20. 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 IEEE Lanman'05

  21. 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 IEEE Lanman'05

  22. More Info • http://www.cs.unc.edu/~maria • http://www.ics.forth.gr/mobile/ • maria@cs.unc.edu Thank You! IEEE Lanman'05

More Related