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
deficiencies of a wireless technology
different phenomena of the wireless access & workload
Impel modelling efforts to produce more realistic models & 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. Wireless infrastructure
Internet
disconnection
Router
Wired Network
Switch
AP3
Wireless
Network
User A
AP 1
AP 2
roaming
roaming
User B
Associations 1 2 3
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 Wired Network Switch Wireless Network Events Session Flow
disconnection
Wired Network
Router
Switch
AP3
Wireless
Network
User A
AP 1
AP 2
Events
User B
Session 1 2 3
Flow
Arrivals t1 t2 t3 t4 t5 t6 t7
time

12. Traffic Demand Parameters
Session
arrival process
starting AP
Flow within session
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 months of SNMP & SYSLOG data from all APs
Packet-header traces:
Two weeks (in April 2005 and April 2006)
Captured on the link between UNC & rest of Internet via a high-precision monitoring card
captured on the link between UNC and the rest of the Internet using a high-precision monitoring card (Endace DAG 4.3GE)
8-day period April 13th ‘05 – April 20th ’05
Custom snmp-polling system relying on a non-blocking snmp library. APs are polled independently so that delays incurring during the processing of SNMP polls bhy the slower APs do not affect the other APs

14. Related modeling approaches
Flow-level modeling by Meng [mobicom ‘04]
No session concept
Weibull for flow interarrivals
Lognormal for flow sizes
AP-level over hourly intervals
Hierarchical modeling by Papadopouli [wicon ‘06]
Time-varying Poisson process for 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
Evaluation and comparison of models
Visual inspection
e.g., CCDFs & QQ plots of models vs. empirical data
Statistical-based criteria
e.g., QQ/simulation envelopes, Kullback-Liebler divergence
Systems-based criteria
e.g., throughput, delay, jitter, queue size
Validation of 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 of flows
for each flow:
generate flow arrivals & sizes based on specific models
Session arrivals:
using hourly, building-specific empirical traces
Flow-related data:
using empirical 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 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 explicitly addressed by the models
Statistical-based
aggregate flow arrival count process
aggregate flow interarrival (1st & 2nd order statistics)
System-based: performance of an IEEE LAN
traffic load and queue size in various time scales
per-flow & hourly aggregate throughput
per-flow delay and jitter
 Compare their performance (empirical traces: “ground truth”) 19

20. Modeling in Various Spatio-temporal Scales
Sufficient spatial detail
Scalable
Amenable to analysis
Hourly AP  
Network-wide
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 explicitly addressed by the models
Statistical-based
aggregate flow arrival count process
aggregate flow interarrival (1st & 2nd order statistics)
System-based: performance of an IEEE LAN
traffic load and queue size 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. Simulation/Emulation Testbed
Internet
Router
Wired
Network
AP3
Switch
User D
Wireless
Network
User A
AP 1
AP 2
User B
User C
Assign traffic demand
Scenario of
wireless access
Various
traffic
conditions
Scenario:
User A generates a flow of size T1
User B generates a flow of size T2 ▪

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 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
NA to epalh8eusw auto me ton Elia kai na doume ean yparxoun kai plots (px tou Februariou pou na voh8oun
Empirical: 1.7 Kbps
Bipareto-Lognormal-AP: 9.7 Kbps
Bipareto-Lognormal: 10.3 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 AP
using statistical-based metrics
using system-based metrics
hourly aggregate throughput
per-flow delay
per-flow throughput
Enables superimposition of models for demand on a given topology
proposed statistical distributions valid over two different periods
Explores spatial distribution of flows & sessions at various scales of spatial aggregation
individual buildings / groups of buildings (clusters

34. Conclusions (con’t) Accurate and scalable models of wireless demand
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

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
Enables superimposition of models for demand on a given topology
proposed statistical distributions valid over two different periods
Explores spatial distribution of flows & sessions at various scales of spatial aggregation
individual buildings / groups of buildings (clusters

36. In progress …
Evaluate the performance of AP or channel selection, load balancing & admission control protocols under real-life traffic conditions
IEEE Mesh & infrastructure-based testbeds
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, …
 Free login/ password to access it
 Simulation & emulation testbeds that replay synthetic traces
for various traffic conditions
Mobile Computing University of Crete/FORTH 