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

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

Wired Network

Wireless Network

Router

Internet

User A AP 1AP 2

AP3Switch

User B

disconnection

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

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

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

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

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

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 heavy-

tailed 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 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

WitMeMo’06

2nd International Workshop on

Wireless Traffic Measurements and Modeling

August 5th, 2006

Boston

http://www.witmemo.org