spatio-temporal modeling of traffic workload in a campus wlan felix hernandez-campos 3 merkouris...
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Spatio-Temporal Modeling of Traffic Workload in a Campus WLAN
Felix Hernandez-Campos3 Merkouris Karaliopoulos2
Maria Papadopouli 1,2,3 Haipeng Shen2
1 Foundation for Research & Technology-Hellas (FORTH) & University of Crete2 University of North Carolina at Chapel Hill3 Google
1IBM Faculty Award 2005, EU Marie Curie IRG, GSRT “Cooperation with non-EU countries” grants
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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
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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
Session
Flows
Associations
Packets
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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
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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
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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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Our Models Session
Arrival process Starting AP
Flow within a session Arrival process Number of flows Size
Systems-wide & AP-level
Captures interaction between clients & network
Above packet level for traffic analysis & closed-loop traffic generation
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Wireless Infrastructure
488 APs, 26,000 students, 3,000 faculty, 9,000 staff over 729-acre 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
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Time Series on Session Arrivals
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Session Arrivals Time-varying Poisson Process
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AP Preference Distribution
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Number of Flows Per Session
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Stationarity of the Distribution of Number of Flows within Session
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Flow Inter-Arrivals within Session
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Flow Size Model
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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
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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)
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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 heavy-
tailed session duration)
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Validation Number of Aggregate Flow Arrivals
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Validation Coefficient of Variation
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Validation: Autocorrelation
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Aggregate Flow Inter-arrivals
99.9th percentile
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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
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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
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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
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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
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UNC/FORTH Web Archive
Online repository of wireless measurement datamodels 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
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WitMeMo’06
2nd International Workshop on
Wireless Traffic Measurements and Modeling
August 5th, 2006
Boston
http://www.witmemo.org