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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