1. Accurate & scalable models for wireless traffic workload Assistant Professor Department of Computer Science, University of Crete & Institute of Computer Science, Foundation for Research & Technology-Hellas (FORTH) 1 IBM Faculty Award, EU Marie Curie IRG, GSRT “Cooperation with non-EU countries” grants Maria Papadopouli Joint research with: F. Hernandez-Campos, M. Karaliopoulos, H. Shen, E. Raftopoulos COST-ACTION: TMA meeting @ Samos’08

2. Wireless landscape Growing demand for wireless access Mechanisms for better than best-effort service provision Performance analysis of these mechanisms  Typically using simplistic traffic models Empirically-based measurements impel modeling efforts to produce more realistic models  Enable more meaningful performance analysis studies

3. Wireless infrastructure Wired Network Wireless Network Router Internet User A User B AP 1 AP 2 AP3 Switch roaming disconnection 1230 Flows Associations Packets

4. Dimensions in modeling wireless access Intended user demand User mobility patterns –Arrival at APs –Roaming across APs –Duration the user is connected to an infrastructure Link conditions Network topology

5. 1230 Session Wired Network Wireless Network Router Internet User A User B AP 1 AP 2 AP3 Switch disconnection Flow time Events Arrivals t1 t2t3t7t6t5t4

6. Our parameters and models ParameterModelProbability Density Function Related Papers Association, session duration BiPareto EW' 06 Session arrival Time-varying Poisson N: # of sessions between t1 and t2 WICON '06 LANMAN'05 AP of first association/session Lognormal WICON '06 Flow interarrival/session Lognormal Same as above WICON '06 Flow number/session BiPareto WICON '06 Flow size BiPareto Same as above WICON '06 Client roaming between APs Markov-chain INFOCOM'04

7. Wireless infrastructure & acquisition 26,000 students, 3,000 faculty, 9,000 staff in over 729-acre campus 488 APs (April 2005), 741 APs (April 2006) SNMP data collected every 5 minutes Several months of SNMP & SYSLOG data from all APs Packet-header traces: –Two weeks (in April 2005 & April 2006) –Captured on the link between UNC & rest of Internet via a high- precision monitoring card

8. Modeling process 1.Selection of models (e.g., various distributions) 2.Fitting parameters using empirical traces 3.Evaluation and comparison of models Visual inspection e.g., CCDFs & QQ plots models vs. empirical data Statistical-based criteria e.g., QQ/simulation envelops, statistical tests Systems-based criteria 4.Validation of models 5.Generalization of models determines spatial & temporal scale

9. Modeling in various spatio-temporal scales Sufficient spatial detailScalableAmenable to analysis Hourly period @ AP    Network-wide    Objective Scales  Tradeoff with respect to accuracy, scalability, reusability & tractablity

10. Synthetic trace generation

11. Simulation/Emulation testbed TCP flows UDP Wired clients: senders Wireless clients: receivers

12. Simulation & emulation testbeds Wired Network Wireless Network Router Internet User A AP 1 AP 2 AP3 Switch User B Scenario of wireless access User CUser D Various traffic conditions Assign traffic demand Scenario: User A generates a flow of size X @ T 1 User B generates a flow of size Y @ T 2 ▪

13. Main results  Accurate and scalable models of wireless demand  Same distributions/models persist: over two different periods (2005 and 2006) over two different campus-wide infrastructures over heavy & normal traffic conditions @ AP using statistical- & systems-based metrics  Empirical traces used as “ground truth” for the comparison with synthetics traces based on various models

14. Main results (con’t) Accuracy: our models perform very close to the empirical traces popular models deviate substantially from the empirical traces Scalability: same distributions at various spatial & temporal scales group of APs per bldg addresses scalability-accuracy tradeoffs Application mix of AP traffic mostly web: very accurate models both web & p2p : models are ok mostly p2p: larger deviations from empirical data

15. In progress … Improve modeling of non-web traffic Client profiling Impact of underlying network conditions on application and usage patterns Evaluate the performance of AP or channel selection, load balancing & admission control protocols under real-life traffic conditions –Mesh testbed –Heterogeneous wireless networks

16. UNC/FORTH web archive  Online repository of models, tools, and traces – Packet header, SNMP, SYSLOG, synthetic traces, … http://netserver.ics.forth.gr/datatraces/  Free login/ password to access it  Simulation & emulation testbeds that replay synthetic traces for various traffic conditions Mobile Computing Group @ University of Crete/FORTH http://www.ics.forth.gr/mobile/  maria@csd.uoc.gr

17. Hourly aggregate throughput EMPIRICAL BIPARETO-LOGNORMAL Fixed flow sizes & empirical flow arrivals (aggregate traffic as in EMPIRICAL) Pareto flow sizes, empirical flow arrivals FLOWSIZE—FLOWARRIVAL BIPARETO-LOGNORMAL-AP Impact of flow size

18. Scalability vs. Accuracy: flow interarrivals EMPIRICAL BDLG(DAY) BDLGTYPE(DAY) NETWORK(TRACE)

19. Scalability vs Accuracy: Number of flow arrivals in an hour EMPIRICAL BDLG(DAY) NETWORK(TRACE) BDLGTYPE(TRACE)

20. Per-flow throughput EMPIRICAL BIPARETO-LOGNORMAL-AP BIPARETO-LOGNORMAL Fixed flow sizes & empirical flow arrivals FLOWSIZE—FLOWARRIVAL Pareto flow sizes & uniform flow arrivals in tracing period Pareto flow sizes due to large % of small size flows

21. Histogram of flow sizes

22. Aggregate hourly downloaded traffic

23. UDP traffic scenario  Wireless hotspot AP  Wireless clients downloading  Wired traffic transmit at 25Kbps  Total aggregate traffic sent in CBR and in empirical is the same Empirical: 1.4 Kbps Bipareto-Lognormal-AP: 2.4 Kbps Bipareto-Lognormal: 2.6 Kbps Large differences in the distributions

24. Impact of application mix on per-flow throughput AP with 85% web traffic AP with 50% web & 40% p2p traffic AP with 80% p2p traffic TCP-based scenario

25. Goodput

26. Per-flow delay

27. Jitter per flow

28. 50% web & 40% p2p 85% web 80% p2p Impact of application mix of AP traffic

29. Session-level flow related variation Mean in-session flow interarrival f In-session flow interarrival can be modeled with same distribution for all building types but with different parameters

30. Session-level flow size variation Mean flow size f (bytes)

31. Flow size vs. flow-interarrival on hourly throughput Flow interarrivals has slightly higher impact avg flow interarrivals fixed original flow size avg flow size fixed original flow interarrival TCP scenario empirical Flow size - Flow interarrival

32. Flow size vs. flow-interarrival on per-flow throughput original flow size avg flow interarrivals fixed Flow size has higher impact avg flow size fixed original flow interarrivals original trace Flow size - Flow interarrival

33. Per flow statistics for hours that have produced the same aggregate download traffic

34. Our models persist for traffic generated during busy periods Empirical trace: one hour of a hotspot AP with heavy workload conditions

35. Number of flows per session Simplicity at the cost of higher loss of information

36. Number of Flows Per Session