IEEE PIMRC 2005 1
A Comparative Measurement A Comparative Measurement Study of the Workload of Wireless Study of the Workload of Wireless
Access Points in Campus Access Points in Campus NetworksNetworks
Maria PapadopouliMaria PapadopouliAssistant ProfessorAssistant Professor
Department of Computer ScienceDepartment of Computer ScienceUniversity of North Carolina at Chapel Hill University of North Carolina at Chapel Hill
(UNC)(UNC)
This work was partially supported by the IBM Corporation under an IBM Faculty Award 2004
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Coauthor & collaboratorCoauthor & collaborator
Felix Hernandez-Felix Hernandez-CamposCampos
Department of Computer Department of Computer ScienceScience
University of North Carolina at University of North Carolina at Chapel Hill Chapel Hill
(UNC)(UNC)
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RoadmapRoadmap
Motivation & Research ObjectivesMotivation & Research Objectives Main ContributionsMain Contributions Data AcquisitionData Acquisition Performance AnalysisPerformance Analysis Related workRelated work Future workFuture work
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MotivationMotivation
Optimization of the performance of Optimization of the performance of wireless networkswireless networks
Better capacity planning & load Better capacity planning & load balancingbalancing
Support of applications with real-time Support of applications with real-time constraintsconstraints
Models for simulation studiesModels for simulation studies
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Research ObjectivesResearch Objectives
Analyze traffic characteristics at each APAnalyze traffic characteristics at each AP Total number of bytes & packets (sent & Total number of bytes & packets (sent &
received)received) Different time scalesDifferent time scales Number of associations and roaming Number of associations and roaming
operationsoperations Impact of building typesImpact of building types
Contrast traffic models & distributions Contrast traffic models & distributions from large production wireless networks from large production wireless networks (UNC & Dartmouth)(UNC & Dartmouth)
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Data AcquisitionData Acquisition 729-acre campus with 26,000 students, 3,000 729-acre campus with 26,000 students, 3,000
faculty, 9,000 staff faculty, 9,000 staff DiverseDiverse environment environment 14,712 unique MAC addresses14,712 unique MAC addresses 488 APs (Cisco 1200, 350, 340 Series)488 APs (Cisco 1200, 350, 340 Series) SNMP pollingSNMP polling every AP every 5minutes using every AP every 5minutes using
a non-blocking library calls a non-blocking library calls Tracing period of Tracing period of 63 days63 days
Data cleaning follows …
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Aggregate Traffic Load Aggregate Traffic Load AnalysisAnalysis
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Dichotomy among APs (1/2) Dichotomy among APs (1/2) APs with uploaders & APs APs with uploaders & APs
with downloaderswith downloaders
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Dichotomy among APs Dichotomy among APs (2/2) (2/2)
APs with uploaders & APs with APs with uploaders & APs with downloadersdownloaders
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Modeling Aggregate Sent & Modeling Aggregate Sent & Received TrafficReceived Traffic
Lognormal distribution
Significant heavier tail than lognormalTwo lognormals with different parameters
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5-Minute Traffic Load5-Minute Traffic Load
Dartmouth APs receive has Dartmouth APs receive has
a lighter body (75% < 100KB)a lighter body (75% < 100KB)
UNC has two regions:UNC has two regions:
40% of intervals with very light 40% of intervals with very light traffic traffic
another close to sent distributionanother close to sent distribution
Similarity between Dartmouth & UNCSimilarity between Dartmouth & UNC [500KB, 2MB] during most active intervals[500KB, 2MB] during most active intervals
AP sent traffic load:
AP receive traffic load:
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Total Number of Mcast/Bcast Total Number of Mcast/Bcast Packets Packets
Received vs. Sent by APReceived vs. Sent by AP
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Total Number of Unicast Total Number of Unicast Packets Packets
Sent vs. Received by APSent vs. Received by AP
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Summary of Summary of Contributions (1/2)Contributions (1/2)
Similarities between UNCSimilarities between UNC vs vs Dartmouth Dartmouth traces traces Log-normalityLog-normality is prevalent is prevalent Light traffic loadLight traffic load but with but with long tailslong tails Majority of APsMajority of APs send & receive packets of send & receive packets of
small sizesmall size Prominent Prominent dichotomy among APsdichotomy among APs
APs dominated by uploadersAPs dominated by uploaders AP dominated by downloadersAP dominated by downloaders APs with average large sent & small receive packetAPs with average large sent & small receive packet APs with small sent & large receive packetAPs with small sent & large receive packet
Substantial numberSubstantial number of of non-unicast packetsnon-unicast packets
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Conclusions (2/2)Conclusions (2/2)
Heavy distribution of Heavy distribution of associations/roaming operationsassociations/roaming operations Daily averages between 170-4,310Daily averages between 170-4,310 Weak correlation with traffic loadWeak correlation with traffic load Need more efficient association Need more efficient association
mechanismsmechanisms
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Future Research Plan Future Research Plan Analysis of packet headers for better Analysis of packet headers for better
understanding the understanding the uploading behavioruploading behavior Formal parametric models for traffic loadFormal parametric models for traffic load Spatial correlations of APs and classification of APs Spatial correlations of APs and classification of APs
based on various parameters (traffic based on various parameters (traffic characteristics, number of associations, distinct characteristics, number of associations, distinct clients)clients)
Topological propertiesTopological properties of wireless infrastructure of wireless infrastructure Analysis of traces from Analysis of traces from diverse set of testbedsdiverse set of testbeds & &
contrast of their traffic modelscontrast of their traffic models
UNC-FORTH data repository of traces & UNC-FORTH data repository of traces & benchmarksbenchmarks
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More InfoMore Info
http://www.cs.unc.edu/~mariahttp://www.cs.unc.edu/~maria
http://www.ics.forth.gr/mobile/http://www.ics.forth.gr/mobile/
[email protected]@cs.unc.edu
Thank You!Thank You!