Spatio-Temporal Modeling of Traffic Workload in a Campus WLAN Felix Hernandez-Campos 3 Merkouris Karaliopoulos 2 Maria Papadopouli 1,2,3 Haipeng Shen 2 1 Foundation for Research & Technology-Hellas (FORTH) & University of Crete 2 University of North Carolina at Chapel Hill 3 Google 1 IBM 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 1 AP 2 AP3 Switch User B disconnection

Wireless infrastructure Wired Network Wireless Network Router Internet User A User B AP 1 AP 2 AP3 Switch roaming disconnection 1230 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

1230 Association Session Wired Network Wireless Network Router Internet User A User B AP 1 AP 2 AP3 Switch disconnection Flow time Events Arrivals t1 t2t3t7t6t5t4

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 13 th ‘05 – April 20 th ‘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 (1 st & 2 nd 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 99.9 th 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   Login/ password access after free registration Joint effort of Mobile Computing UNC & FORTH

WitMeMo’06 2 nd International Workshop on Wireless Traffic Measurements and Modeling August 5 th, 2006 Boston