IEEE PIMRC A Comparative Measurement Study of the Workload of Wireless Access Points in Campus Networks Maria Papadopouli Assistant Professor Department of Computer Science University of North Carolina at Chapel Hill (UNC) This work was partially supported by the IBM Corporation under an IBM Faculty Award 2004

2IEEE PIMRC 2005 Coauthor & collaborator Felix Hernandez-Campos Felix Hernandez-Campos Department of Computer Science Department of Computer Science University of North Carolina at Chapel Hill (UNC) (UNC)

3IEEE PIMRC 2005 Roadmap Motivation & Research Objectives Motivation & Research Objectives Main Contributions Main Contributions Data Acquisition Data Acquisition Performance Analysis Performance Analysis Related work Related work Future work Future work

4IEEE PIMRC 2005 Motivation Optimization of the performance of wireless networks Optimization of the performance of wireless networks Better capacity planning & load balancing Better capacity planning & load balancing Support of applications with real-time constraints Support of applications with real-time constraints Models for simulation studies Models for simulation studies

5IEEE PIMRC 2005 Research Objectives Analyze traffic characteristics at each AP Analyze traffic characteristics at each AP Total number of bytes & packets (sent & received) Total number of bytes & packets (sent & received) Different time scales Different time scales Number of associations and roaming operations Number of associations and roaming operations Impact of building types Impact of building types Contrast traffic models & distributions from large production wireless networks (UNC & Dartmouth) Contrast traffic models & distributions from large production wireless networks (UNC & Dartmouth)

6IEEE PIMRC 2005 Data Acquisition 729-acre campus with 26,000 students, 3,000 faculty, 9,000 staff 729-acre campus with 26,000 students, 3,000 faculty, 9,000 staff Diverse environment Diverse environment 14,712 unique MAC addresses 14,712 unique MAC addresses 488 APs (Cisco 1200, 350, 340 Series) 488 APs (Cisco 1200, 350, 340 Series) SNMP polling every AP every 5minutes using a non- blocking library calls SNMP polling every AP every 5minutes using a non- blocking library calls Tracing period of 63 days Tracing period of 63 days Data cleaning follows …

7IEEE PIMRC 2005 Aggregate Traffic Load Analysis

8IEEE PIMRC 2005 Dichotomy among APs (1/2) APs with uploaders & APs with downloaders

9IEEE PIMRC 2005 Dichotomy among APs (2/2) APs with uploaders & APs with downloaders Dichotomy among APs (2/2) APs with uploaders & APs with downloaders

10IEEE PIMRC 2005 Modeling Aggregate Sent & Received Traffic Lognormal distribution Significant heavier tail than lognormal Two lognormals with different parameters

11IEEE PIMRC Minute Traffic Load Dartmouth APs receive has a lighter body (75% < 100KB) UNC has two regions: 40% of intervals with very light traffic another close to sent distribution Similarity between Dartmouth & UNC Similarity between Dartmouth & UNC [500KB, 2MB] during most active intervals [500KB, 2MB] during most active intervals AP sent traffic load: AP receive traffic load:

12IEEE PIMRC 2005 Total Number of Mcast/Bcast Packets Received vs. Sent by AP

13IEEE PIMRC 2005 Total Number of Unicast Packets Sent vs. Received by AP

14IEEE PIMRC 2005 Summary of Contributions (1/2) Similarities between UNC vs Dartmouth traces Log-normality is prevalent Log-normality is prevalent Light traffic load but with long tails Light traffic load but with long tails Majority of APs send & receive packets of small size Majority of APs send & receive packets of small size Prominent dichotomy among APs Prominent dichotomy among APs  APs dominated by uploaders  AP dominated by downloaders APs with average large sent & small receive packet APs with average large sent & small receive packet APs with small sent & large receive packet APs with small sent & large receive packet Substantial number of non-unicast packets Substantial number of non-unicast packets

15IEEE PIMRC 2005 Conclusions (2/2) Heavy distribution of associations/roaming operations Heavy distribution of associations/roaming operations Daily averages between 170-4,310 Daily averages between 170-4,310 Weak correlation with traffic load Weak correlation with traffic load  Need more efficient association mechanisms

16IEEE PIMRC 2005 Future Research Plan Analysis of packet headers for better understanding the uploading behavior Analysis of packet headers for better understanding the uploading behavior Formal parametric models for traffic load Formal parametric models for traffic load Spatial correlations of APs and classification of APs based on various parameters (traffic characteristics, number of associations, distinct clients) Spatial correlations of APs and classification of APs based on various parameters (traffic characteristics, number of associations, distinct clients) Topological properties of wireless infrastructure Topological properties of wireless infrastructure Analysis of traces from diverse set of testbeds & contrast of their traffic models Analysis of traces from diverse set of testbeds & contrast of their traffic models UNC-FORTH data repository of traces & benchmarks UNC-FORTH data repository of traces & benchmarks

17IEEE PIMRC 2005 More Info Thank You! Thank You!