Transcript Document
Spatio-Temporal Modeling of
Traffic Workload in a Campus WLAN
Felix Hernandez-Campos3
Maria Papadopouli
1,2,3
Merkouris Karaliopoulos2
Haipeng Shen2
1 Foundation
for Research & Technology-Hellas (FORTH) & University of Crete
2 University of North Carolina at Chapel Hill
3 Google
1IBM
Faculty Award 2005, EU Marie Curie IRG, GSRT “Cooperation with non-EU countries” grants
Motivation
Growing demand for wireless access
Mechanisms for better than best-effort service provision need to be
deployed
Examples:
capacity planning, monitoring, AP selection, load balancing
Evaluate these mechanisms via simulations & analytically
Models for network & user activity are fundamental requirements
Wireless infrastructure
disconnection
Internet
Router
Wired
Network
Switch
Wireless
Network
User A
AP 1
User B
AP 2
AP3
Wireless infrastructure
Internet
disconnection
Router
Wired Network
Switch
AP3
Wireless
Network
User A
AP 1
AP 2
roaming
User B
roaming
Session
Associations
1
Flows
2
Packets
3
0
Modeling Traffic Demand
Multi-level spatio-temporal nature
Different spatial scales
Entire infrastructure, AP-level, client-level
Time granularities
Packet-level, flow-level, session-level
Modelling objectives
Distinguish two important dimensions on wireless network
modelling
User demand (access & traffic)
Topology (network, infrastructure, radio propagation)
Find concepts that are well-behaved, robust to network
dependencies & scalable
Internet
disconnection
Router
Wired Network
Switch
Wireless
Network
User A
AP 1
AP3
AP 2
Events
User B
Session
Association
1
2
3
0
Flow
Arrivals
t1
t2
t3
t4
t5
t6
t7
time
Our Models
Session
Arrival process
Starting AP
Captures interaction between clients & network
Flow within a session
Arrival process
Number of flows
Size
Above packet level for traffic analysis & closed-loop traffic generation
Systems-wide & AP-level
Wireless Infrastructure
488 APs, 26,000 students, 3,000 faculty, 9,000 staff over 729acre campus
SNMP data collected every 5 minutes
Packet-header traces:
8-day period April 13th ‘05 – April 20th ‘05
175GB
captured on the link between UNC & the rest of the Internet
using a high-precision monitoring card
Time Series on Session Arrivals
Session Arrivals
Time-varying Poisson Process
AP Preference Distribution
Number of Flows Per Session
Stationarity of the
Distribution of Number of Flows within Session
Flow Inter-Arrivals within Session
Flow Size Model
Model Validation Methodology
Produced synthetic data based on
Our models on session and flows-per-session
Session arrivals: Time-Varying Poisson
Flow interarrival in session: Lognormal
Compound model (session, flows-per-session)
Session arrivals: Time-Varying Poisson
Flows interarrival in session: Weibull
Flat model
No session concept
Flows: renewal process
Model Validation Methodology
Simulations -- Synthetic data vs. original trace
Metrics: Variables not explicitly addressed by our models
Aggregate flow arrival count process
Aggregate flow interarrival time-series (1st & 2nd order
statistics)
Systems-wide & AP-based
Different tracing periods (in 2005 & 2006)
Simulations
Produce synthetic data based on aforementioned models
Synthesize sessions & flows for a 3-day period in simulations
Consider flows generated during the third day (due to heavytailed session duration)
Validation
Number of Aggregate Flow Arrivals
Validation
Coefficient of Variation
Validation: Autocorrelation
Aggregate Flow Inter-arrivals
99.9th percentile
Related Work in
Modeling Traffic in Wired Networks
Flow-level
in several protocols (mainly TCP)
Session-level
FTP, web traffic
Session borders are heuristically defined by intervals of
inactivity
Related work in
Modeling Wireless Demand
Flow-level modelling by Meng et al. [mobicom04]
No session concept
Flow interarrivals follow Weibull
Modelling flows to specific APs over one-hour intervals
Does not scale well
Conclusions
First system-wide, multi-level parametric modelling of wireless
demand
Enables superimposition of models for demand on a given
topology
Focuses on the right level of detail
Masks network-related dependencies that may not be relevant
to a range of systems
Makes the wireless networks amenable to statistical analysis
& modeling
Future Work
Explore the spatial distribution of flows & sessions at various
scales of spatial aggregation
Examples: building, building type, groups of buildings
Model the client dynamics
UNC/FORTH Web Archive
Online repository of
wireless measurement data
models
tools
Packet header, SNMP, SYSLOG, signal quality
http://www.cs.unc.edu/Research/mobile/datatraces.htm
Login/ password access after free registration
Joint effort of Mobile Computing Groups @ UNC & FORTH
WitMeMo’06
2nd International Workshop on
Wireless Traffic Measurements and Modeling
August 5th, 2006
Boston
http://www.witmemo.org