Modeling the W ireless Traffic Workload

1. Modeling the Wireless Traffic Workload Maria Papadopouli Assistant Professor Department of Computer Science, University of Crete & Institute of Computer Science, Foundation for Research & Technology-Hellas (FORTH) Joint research with: F. Hernandez-Campos, M. Karaliopoulos, H. Shen, E. Raftopoulos IBM Faculty Award, EU Marie Curie IRG, GSRT “Cooperation with non-EU countries” grants

2. Research Projects @ UoC/FORTH • Measurements on large-scale wireless networks • Delays, packet losses, traffic characterization, impact of caching • Measurement-based modelling of wireless networks • Mechanisms for improving wireless access & spectrum utilization • AP selection and caching mechanisms • Evaluating user experience running streaming applications over wireless • Location-sensing • Mobile p2p computing • Impact of caching in mobile social networking • Design & evaluation of mobile applications

3. Empirical measurements • Can be beneficial in revealing • deficienciesof awireless technology • different phenomena of the wireless access & workload • Impel modelling efforts to produce more realisticmodels & synthetic traces based on these models • Enable meaningful performance analysis studies using such empirical and synthetic traces  Highlight the ability of empirical-based models to capture the characteristics of the user-workload and provide a flexible framework for using them in performance analysis

4. Modelling and trace generation • The definition of realism must be considered in the context of its usage • eg requirements for capacity planning vs. queue management • Our motivation: Capacity planning, admission control, AP selection algorithms • Modelling objectives: Accuracy, scalability, re-usability, tractability (easy to interpret)

5. Roadmap • Background • Proposed models • Modelling methodology • Model evaluation & validation • Scalability vs. accuracy tradeoffs • Conclusions • On-going research

6. Related work • Rich literature in traffic characterization in wired networks • Willinger, Taqqu, Leland, Park on self-similarity of Ethernet LAN traffic • Crovela, Barford on Web traffic • Feldmann, Paxson on TCP • Paxson, Floyd on WAN • Jeffay, Hernandez-Campos, Smith on HTTP    • Traffic generators for wired traffic • Hernandez-Campos, Vahdat, Barford, Ammar, Pescape, … • P2P traffic • Saroiu, Sen, Gummadi, He, Leibowitz, … • On-line games • Pescape, Zander, Lang, Chen, … • Modelling of wireless traffic • Meng et al.

7. 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 Associations Flows Packets

8. Dimensions in modeling wireless access • Intended user demand • User mobility patterns • Arrival at APs • Roaming across APs • Link conditions • Network topology

9. Main approaches for traffic generation • Packet-level replay • An exact reproduction of a collected trace in terms of packet arrival times, size, source, destination, content type  Reflects specific traffic conditions • Suffers from arbitrary delays e.g., interrupts, service mechanisms, scheduling processes  difficult to incorporate feedback-loop characteristics • Source-level generation  Allows the underlying network, protocol, & application layer to specify & control the packet arrival process • Simplest example: infinite source model

10. Our approach  Inspired by the source-level (or network independent) modelling Assumptions: • Client arrivals at an infrastructure (initiated by humans) at a large extent are not affected by the underlying network technology • Very low % of packet loss at the network layer  flow arrivals & sizes approximate intended user traffic demand

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

12. Traffic Demand Parameters • Session • arrival process • starting AP • Flow within session • arrival process • number of flows • size (in bytes) Captures interaction between clients & network Above packet-level analysis

13. 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 monthsof SNMP & SYSLOG data from all APs • Packet-headertraces: • Two weeks (in April 2005 and April 2006) • Captured on the link between UNC & rest of Internet via a high-precision monitoring card

14. Related modeling approaches • Flow-level modeling by Meng [mobicom ‘04] • No session concept • Weibull for flow interarrivals • Lognormalfor flow sizes • AP-level over hourly intervals • Hierarchical modeling by Papadopouli [wicon ‘06] Time-varying Poisson processfor session arrivals • BiPareto for in-session flow numbers & flow sizes • Lognormal for in-session flow interarrivals Sessions capture the non-stationarity of traffic workload

15. Modeling methodology • Selection of models (e.g., various distributions) • Fitting parameters using empirical traces • Evaluationand comparison of models • Visual inspection e.g., CCDFs & QQ plots of models vs. empirical data • Statistical-based criteria e.g., QQ/simulation envelopes, Kullback-Lieblerdivergence • Systems-based criteria e.g., throughput, delay, jitter, queue size • Validationof models • Generalization of models

16. Synthetic trace generation

17. Synthetic traces based on empirical ones original data from the real-life infrastructure Produced by this process: Generate session arrivals within each session: generate number offlows for each flow: generate flow arrivals & sizes based on specific models • Session arrivals: using hourly, building-specific empiricaltraces • Flow-related data: usingempirical traces of different spatial scales

18. Model validation Use empirical data from different • tracing periods April 2005 & 2006 • spatial scales AP-level < building-level < building-type-level < network-wide • traffic conditions @ AP • campus-wide wireless infrastructures UNC, Dartmouth • Do the same distributions persist across these traces? Compare their performance (empirical traces: “ground truth”) YES!

19. Model evaluation Create synthetic data based on models Analysis with metrics not explicitlyaddressed by the models Statistical-based aggregate flow arrival count process aggregate flow interarrival (1st & 2nd order statistics) System-based: performance of an IEEE802.11 LAN traffic load and queuesize in various time scales per-flow & hourly aggregate throughput per-flow delay and jitter  Compare their performance (empirical traces: “ground truth”)

20. Modeling in Various Spatio-temporal Scales Objective Scales  Tradeoff with respect to accuracy, scalability & reusability

21. Scalability vs. Accuracy: Flow Interarrivals Spatial /Temporal Scales EMPIRICAL BDLG(DAY) BDLGTYPE(DAY) NETWORK(TRACE)

22. Scalability vs. Accuracy: Number of Flow Arrivals in an Hour BDLGTYPE(TRACE) BDLG(DAY) EMPIRICAL NETWORK(TRACE)

23. Model evaluation • Create synthetic data based on models • Analysis with metrics not explicitlyaddressed by the models • Statistical-based • aggregate flow arrival count process • aggregate flow interarrival (1st & 2nd order statistics) • System-based: performance of an IEEE802.11 LAN • traffic load and queuesize in various time scales • per-flow & hourly aggregate throughput • per-flow delay and jitter  Compare their performance (empirical traces: “ground truth”)  Dominant parameters ? Impact of application mix?

24. User D User C User B Simulation/Emulation Testbed Internet Router Wired Network AP3 Switch Wireless Network User A AP 1 AP 2 Assign traffic demand Scenario of wireless access Scenario: User A generates a flow of size X @ T1 User B generates a flow of size Y @ T2 ▪ ▪ Various traffic conditions

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

26. Hourly aggregate throughput FLOW SIZE—FLOW (INTER)ARRIVAL EMPIRICAL Impact of flow size Fixed flow sizes & empirical flow arrivals (aggregate traffic as in EMPIRICAL) BIPARETO-LOGNORMAL-AP Pareto flow sizes, empirical flow arrivals BIPARETO-LOGNORMAL

27. Per-flow throughput FLOWSIZE—FLOWARRIVAL Pareto flow sizes & uniform flow arrivals BIPARETO-LOGNORMAL EMPIRICAL BIPARETO-LOGNORMAL-AP due to large % of small size flows (= MSS) Pareto flow sizes Fixed flow sizes & empirical number of flows

28. Aggregate hourly downloaded traffic

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

30. Amount of Trx Bytes & Queue Size

31. m=4 m=12 Forwarded bytes @ router In various times scales (2m ms) m=8 m=14

32. 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

33. Conclusions Model validation • over two different periods (2005 and 2006) • over two different campus-wide infrastructures(UNC & Dartmouth) BiPareto captures well the flow sizes • over heavy & normal traffic conditions @ AP • using statistical-based metrics • using system-based metrics hourly aggregate throughput per-flow delay per-flow throughput

34. Conclusions (con’t) Accuracy: • our models perform very close to the empirical traces • popular models deviate substantially from the empirical traces Scalability: Accurate and scalable models of wireless demand • same distributions at various spatial & temporal scales • group of APs per bldg addresses scalability-accuracy tradeoffs

35. Conclusions (con’t) Impact of various parameters • Application mix of AP traffic • mostly web: very accurate models • both web & p2p : models are ok • mostly p2p: large deviations from empirical data  Modelling P2P traffic is challenging due to the increased number, diversity, complexity & unpredictability in user interaction  Both flow size and flow interarrivals

36. In progress … • Evaluate the performance of AP or channel selection, load balancing & admission control protocols under real-life traffic conditions • IEEE802.11 Mesh & infrastructure-basedtestbeds • Heterogeneous wireless networks

37. Revisiting modelling approach • Physical meaning of the models and their parameters • Client profile • e.g., depending on the application-mix, amount of traffic • Group mobility • Multiple network interfaces • Cooperative client models • Dependencies among traffic demand & network conditions • Impact of underlying network conditions on application & usage patterns

38. 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