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Spatio-Temporal Modeling of Traffic Workload in a Campus WLAN

Spatio-Temporal Modeling of Traffic Workload in a Campus WLAN. Felix Hernandez-Campos 3 Merkouris Karaliopoulos 2 Maria Papadopouli 1,2,3 Haipeng Shen 2. 1 Foundation for Research & Technology-Hellas (FORTH) & University of Crete

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Spatio-Temporal Modeling of Traffic Workload in a Campus WLAN

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  1. Spatio-Temporal Modeling of Traffic Workload in a Campus WLAN Felix Hernandez-Campos3 Merkouris Karaliopoulos2 Maria Papadopouli1,2,3Haipeng Shen2 1 Foundation for Research & Technology-Hellas (FORTH) & University of Crete 2 University of North Carolina at Chapel Hill 3 Google 1IBM Faculty Award 2005, EU Marie Curie IRG, GSRT “Cooperation with non-EU countries” grants

  2. Motivation • Growing demand for wireless access • Mechanisms for better than best-effort service provision need to be deployed Examples: capacity planning, monitoring, AP selection, load balancing • Evaluate these mechanisms via simulations & analytically • Models for network & user activity are fundamental requirements

  3. User B Wireless infrastructure disconnection Internet Router Wired Network AP3 Switch Wireless Network User A AP 1 AP 2

  4. 1 2 3 0 Wireless infrastructure Internet disconnection Router Wired Network Switch AP3 Wireless Network User A AP 1 AP 2 roaming roaming User B Session Associations Flows Packets

  5. Modeling Traffic Demand Multi-level spatio-temporal nature • Different spatial scales Entire infrastructure, AP-level, client-level • Time granularities Packet-level, flow-level, session-level

  6. Modelling objectives Distinguish two important dimensions on wireless network modelling • User demand (access & traffic) • Topology (network, infrastructure, radio propagation) Find concepts that are well-behaved, robust to network dependencies & scalable

  7. Internet disconnection Wired Network Router Switch AP3 Wireless Network User A AP 1 AP 2 Events User B Session 1 2 3 0 Association Flow Arrivals t1 t2 t3 t4 t5 t6 t7 time

  8. Our Models • Session • Arrival process • Starting AP • Flow within a session • Arrival process • Number of flows • Size • Systems-wide & AP-level Captures interaction between clients & network Above packet level for traffic analysis & closed-loop traffic generation

  9. Wireless Infrastructure • 488 APs, 26,000 students, 3,000 faculty, 9,000 staff over 729-acre campus • SNMP data collected every 5 minutes • Packet-header traces: • 8-day period April 13th ‘05 – April 20th ‘05 • 175GB • captured on the link between UNC & the rest of the Internet using a high-precision monitoring card

  10. Time Series on Session Arrivals

  11. Session Arrivals Time-varying Poisson Process

  12. AP Preference Distribution

  13. Number of Flows Per Session

  14. Stationarity of the Distribution of Number of Flows within Session

  15. Flow Inter-Arrivals within Session

  16. Flow Size Model

  17. Model Validation Methodology • Produced synthetic data based on • Our models on session and flows-per-session Session arrivals: Time-Varying Poisson Flow interarrival in session: Lognormal • Compound model (session, flows-per-session) Session arrivals: Time-Varying Poisson Flows interarrival in session: Weibull • Flat model • No session concept • Flows: renewal process

  18. Model Validation Methodology Simulations -- Synthetic data vs. original trace Metrics: Variables not explicitly addressed by our models • Aggregate flow arrival count process • Aggregate flow interarrival time-series (1st & 2nd order statistics)  Systems-wide & AP-based  Different tracing periods (in 2005 & 2006)

  19. Simulations • Produce synthetic data based on aforementioned models • Synthesize sessions & flows for a 3-day period in simulations • Consider flows generated during the third day (due to heavy-tailed session duration)

  20. Validation Number of Aggregate Flow Arrivals

  21. Validation Coefficient of Variation

  22. Validation: Autocorrelation

  23. Aggregate Flow Inter-arrivals 99.9th percentile

  24. Related Work in Modeling Traffic in Wired Networks • Flow-level in several protocols (mainly TCP) • Session-level FTP, web traffic Session borders are heuristically defined by intervals of inactivity

  25. Related work in Modeling Wireless Demand Flow-level modelling by Meng et al. [mobicom04] • No session concept • Flow interarrivals follow Weibull • Modelling flows to specific APs over one-hour intervals Does not scale well

  26. Conclusions Firstsystem-wide, multi-level parametric modelling of wireless demand • Enables superimposition of models for demand on a given topology • Focuses on the right level of detail • Masks network-related dependencies that may not be relevant to a range of systems • Makes the wireless networks amenable to statistical analysis & modeling

  27. Future Work • Explore the spatial distribution of flows & sessions at various scales of spatial aggregation Examples: building, building type, groups of buildings • Model the client dynamics

  28. UNC/FORTH Web Archive • Online repository of wireless measurement data models tools • Packet header, SNMP, SYSLOG, signal quality • http://www.cs.unc.edu/Research/mobile/datatraces.htm Login/ password access after free registration • Joint effort of Mobile Computing Groups @ UNC & FORTH

  29. WitMeMo’06 2nd International Workshop on Wireless Traffic Measurements and Modeling August 5th, 2006 Boston http://www.witmemo.org

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