Modeling the Wireless 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 _at_ 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.

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Wireless infrastructure
Internet
disconnection
Router
Wired Network
Switch
AP3
Wireless Network
User A
AP 1
AP 2
roaming
roaming
User B
Associations
Flows
Packets
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Dimensions in modeling wireless access

• Intended user demand
• User mobility patterns
• Arrival at APs
• Roaming across APs
• Link conditions
• Network topology

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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

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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

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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
t7
t6
t5
t4
time
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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
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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

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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
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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

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Synthetic trace generation
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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

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Model validation

• ?Use empirical data from different
• tracing periods
• April 2005 2006
• spatial scales
• AP-level lt building-level lt
  building-type-level lt network-wide
• traffic conditions _at_ AP
• campus-wide wireless infrastructures
• UNC, Dartmouth
• Do the same distributions persist across these
  traces ?
• ? Compare their performance (empirical traces
  ground truth)

YES!
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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 IEEE802.11 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)

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Modeling in Various Spatio-temporal Scales
Sufficient spatial detail Scalable Amenable to analysis
Hourly period _at_ AP ? ? ?
Network-wide ? ? ?
Objective
Scales
? Tradeoff with respect to accuracy, scalability
reusability
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Scalability vs. Accuracy Flow Interarrivals
Spatial /Temporal Scales
EMPIRICAL
BDLG(DAY)
BDLGTYPE(DAY)
NETWORK(TRACE)
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Scalability vs. Accuracy Number of Flow
Arrivals in an Hour
BDLGTYPE(TRACE)
BDLG(DAY)
EMPIRICAL
NETWORK(TRACE)
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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 IEEE802.11 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?

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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 _at_
T1 User B generates a flow of size Y _at_ T2
?
?
Various traffic conditions
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Simulation/Emulation testbed

• TCP flows
• UDP

• Wired clients senders
• Wireless clients receivers

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Hourly aggregate throughput
FLOW SIZEFLOW (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
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Per-flow throughput
FLOWSIZEFLOWARRIVAL
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
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Aggregate hourly downloaded traffic
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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
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Amount of Trx Bytes Queue Size
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m4
m12
Forwarded bytes _at_ router In various times scales
(2m ms)
m8
m14
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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
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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 _at_ AP
• using statistical-based metrics
• using system-based metrics
• hourly aggregate throughput
• per-flow delay
• per-flow throughput

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Conclusions (cont)
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

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Conclusions (cont)
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

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In progress

• Evaluate the performance of AP or channel
  selection, load balancing admission control
  protocols under real-life traffic conditions
• IEEE802.11 Mesh infrastructure-based testbeds
• Heterogeneous wireless networks

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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

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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 _at_
  University of Crete/FORTH
• http//www.ics.forth.gr/mobi
  le/
• ? maria_at_csd.uoc.gr