A Comparative Measurement Study of the Workload of Wireless Access Points in Campus Networks

1. 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 IEEE PIMRC 2005

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

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

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

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

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

7. Aggregate Traffic Load Analysis IEEE PIMRC 2005

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

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

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

11. 5-Minute Traffic Load AP sent traffic load: • Similarity between Dartmouth & UNC • [500KB, 2MB] during most active intervals • 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 AP receive traffic load: IEEE PIMRC 2005

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

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

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

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

16. Future Research Plan • Analysis of packet headers for better understanding the uploading behavior • 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) • Topological properties of wireless infrastructure • Analysis of traces from diverse set of testbeds & contrast of their traffic models • UNC-FORTH data repository of traces & benchmarks IEEE PIMRC 2005

17. More Info • http://www.cs.unc.edu/~maria • http://www.ics.forth.gr/mobile/ • maria@cs.unc.edu Thank You! IEEE PIMRC 2005