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On Scalable Measurement-driven Modeling of Traffic Demand in Large WLANs
1 Foundation for Research & Technology-Hellas (FORTH) & University of Crete2 University of North Carolina at Chapel Hill
1IBM Faculty Award, EU Marie Curie IRG, GSRT “Cooperation with non-EU countries” grants
Merkouris Karaliopoulos2 Haipeng Shen2 Elias Raftopoulos1
Maria Papadopouli 1,2
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Wireless landscape
Growing demand for wireless access Mechanisms for better than best-effort service provision Performance analysis of these mechanisms Majority of studies make high-level observations about traffic
dynamics in tempo-spatial domain Models of network & user activity in various spatio-temporal
scales are required
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Wireless infrastructure
Wired Network
Wireless Network
Router
Internet
User A AP 1AP 2
AP3Switch
User B
disconnection
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Wireless infrastructure
Wired Network
Wireless Network
Router
Internet
User A
User B
AP 1 AP 2
AP3Switch
roaming
roaming
disconnection
1 2 3 0
Flows
Associations
Packets
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Modelling objectives
Important dimensions on wireless network modelling user demand (access & traffic) topology (network, infrastructure, radio propagation)
Structures that are well-behaved, robust, scalable & reusable
Publicly available analysis tools, traces, & models
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1 2 3 0Association
Session
Wired Network
Wireless Network
Router
Internet
User A
User B
AP 1 AP 2
AP3Switch
disconnection
Flow
time
Events
Arrivals
t1 t2 t3 t7t6t5t4
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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 Packet-header traces:
175GB (April 2005), 365GB (April 2006) captured on the link between UNC & rest of Internet via a high-
precision monitoring card
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Our models Session
arrival process starting AP
Flow within session arrival process number of flows size
Captures interaction between clients & network
Above packet level for traffic analysis & closed-loop traffic generation
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Our parameters and models
Parameter Model Probability Density FunctionRelated Papers
Association, session duration BiPareto EW' 06
Session arrival Time-varying Poisson with rate λ(t)
N: # of sessions between t1 and t2
WICON '06
Client arrival Time-varying Poisson with rate λ(t) Same as above 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
Spatio-temporal phenomena in wireless Web access INFOCOM'04
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Related modeling approaches
Hierarchical modeling by Papadopouli [wicon ‘06] Parameters: Session & in-session flow:
Time-varying Poisson process for session arrivals biPareto for in-session flow numbers & flow sizes Lognormal for in-session flow interarrivals
Flow-level modeling by Meng [mobicom ‘04] No session concept, flow interarrivals follow Weibull AP-level over hourly intervals
Larger deviation from real traces than our models
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Number of Flows Per Session
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Related modeling approaches (cont’d)
Sufficient spatial detail Scalable Amenable to analysis
Hourly period @ AP
Network-wide
ObjectiveScales
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Main research issues
Hierarchical modeling traffic workload
AP-level vs. network-wide
Other spatio-temporal levels ?
Model validation @ different spatial scales using data from different periods
Scalability, reusability, accuracy tradeoffs
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Hourly session arrival rates
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Session-level flow variation
Number of flows in a session (k)
Broad variation of the in-session number of flows per building-type distribution
More activeweb browsingbehavior
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Session-level flow size variation
Mean flow size f (bytes)
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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
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Starting building & “roaming”
Number of visited bldgs x
Small % of building-roaming flowsLittle dependence on what kind of building a session is initiated
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Model validation
Simulations: synthetic data vs. original trace
Metrics: variables not explicitly addressed by our models aggregate flow arrival count process aggregate flow interarrival (1st & 2nd order statistics)
Increasing order of spatial aggregation AP-level, building-level (bldg), building-type-level (bldg-type),
network-wide
Different tracing periods (April 2005 & 2006)
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Simulations
Produce synthetic data based on aforementioned models Synthesize sessions & flows for simulations Session arrivals are modeled after hourly bldg-specific data Flow-related data: bldg (day, trace), bldg-type, network-wide
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Number of flows per session
Simplicity at the cost of higher loss of information
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Number of aggregate flow arrivals
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Autocorrelation of flow interarrivals
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Flow interarrivals time
Aggregation in time-dimensionmay cancel out the benefit of getting higher spatial resolution
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Conclusions
Multi-level parametric modelling of wireless demand Network-wide models:
o Time-varying Poisson process for session arrivalso biPareto for in-session flow numbers & flow sizeso Lognormal for in-session flow interarrivals
Validation of models over two different periods
Same distributions apply for modeling at finer spatial scales building-level, groups of buildings with similar usage
Evaluation of scalability-accuracy tradeoff
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UNC/FORTH web archive
Online repository of models, tools, and traces Packet header, SNMP, SYSLOG, signal quality
http://netserver.ics.forth.gr/datatraces/
Free login/ password to access it
Joint effort of Mobile Computing Groups @ FORTH & UNC
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Appendix
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Related research
Modeling traffic in wired networks Flow-level
several protocols (mainly TCP) Session-level
FTP, web traffic session borders heuristically defined by intervals of inactivity
Modeling traffic in wireless networks Flow-level modeling by Meng [mobicom04]
No session concept, flow interarrivals follow Weibull Modelling flows to specific APs over one-hour intervals Does not scale well Larger deviation from real traces than our models
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Flow interarrival time
[Hinton-James
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Hourly number of flow arrivals
[Hinton-James
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Autocorrelation of flow interarrivals
[Hinton-James
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HT James
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McColl
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Our models 2/2
Modeled variable Model Probability Density Function (PDF) Parameters
Session arrival Time-varying Poisson with rate
Hourly rate: 44(min),
1132(max), 294(median)
AP of first association/session
Lognormal
Flow interarrival/session
Lognormal Same as above
Flow number/session BiPareto
Flow size BiPareto Same as above
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Related work in wireless traffic modeling
Over hourly intervals at AP-level Captures finer spatial detail required for evaluating network functions
with focus on AP-level (e.g., load-balancing, admission control) Does not scale for large infrastructures Data do not always amenable to statistical analysis
Infrastructure-wide Models amenable to statistical analysis Concise summary of traffic demand at system-level Fails to capture finer spatial detail required for evaluating network
functions with focus on AP-level