Self-Management in Chaotic Wireless Deployments

1. Self-Managementin Chaotic Wireless Deployments Authors: Aditya Akella, Glenn Judd, Srinivasan Seshan, Peter Steenkiste MobiCom 2005

2. Introduction Characterizing current 802.11 deployments Impact on end-user performance Limiting the impact of interference Power and rate selection algorithms Conclusions Outline

3. Introduction • Chaotic deployment • Unplanned • highly variable AP densities • Unmanaged • not configured to minimize interference • Questions: • Impact of interference on end-user performance? • How to improve end-user performance in chaotic deployments?

4. Related work • Management in wired networks [IETF zeroconf, Thomson 1998, Droms 1997] • Rate adaptation in ad-hoc networks [Sadeghi 2002] • Traffic scheduling in sensor networks and 802.11 networks [Qiao 2003, Kompella 2003] • Power and rate control in ad-hoc routing protocols [Kawadia2005, Draves 2004, Santhanam 2003, Holland 2003] • Commercial Products

5. Introduction Characterizing current 802.11 deployments Impact on end-user performance Limiting the impact of interference Power and rate selection algorithms Conclusions Outline

6. Characterizing Current 802.11 Deployments • Measurement data sets • Place Lab • WifiMaps • Pittsburgh Wardrive • Measurement observations • 802.11 deployment density • 802.11 channel usage • 802.11b vs. 802.11g • Vendors and AP management support

7. 802.11 Deployment Density Data set: Place Lab Degree: # of neighbor APs (within 50m) Deployment: high density Degree≥3 (interference)

8. Measurement Observations • 802.11 channel usage • 802.11b vs. 802.11g • 20% are 802.11g • Vendors and AP management support Channel usage Popular AP vendors

9. Introduction Characterizing current 802.11 deployments Impact on end-user performance Limiting the impact of interference Power and rate selection algorithms Conclusions Outline

10. Simulation Topology • D clients with an AP • Clients 1m away from AP • APs on channel 6 • Transmit power: 15 dBm • Transmission rate: 2Mbps • RTS/CTS turned off • Loss models Data set: Pittsburgh Wardrive

11. Simulation Set-up • Http • Client run HTTP with AP • Two HTTP transfers separated by a think time (Poisson distribution) • Comb-ftpi • i clients run long-lived FTP

12. Interference at Low & High Client-Densities • Interference increases with client density • More degradation when traffic load is high One client per AP Three clients per AP

13. Introduction Characterizing current 802.11 deployments Impact on end-user performance Limiting the impact of interference Power and rate selection algorithms Conclusions Outline

14. Limiting the Impact of Interference • Optimal static channel allocation • Transmit power control

15. Optimal Static Channel Allocation • Optimal channel allocation helpful, but cannot eliminate interference Single channel Three channels

16. Transmit Power Control Power level: 15dBm Power level: 3dBm Optimal channel allocation + Transmit power control Optimal channel allocation • Transmit power control improve application performance, and network capacity & fairness

17. Introduction Characterizing current 802.11 deployments Impact on end-user performance Limiting the impact of interference Power and rate selection algorithms Conclusions Outline

18. Power and Rate Selection Algorithms • Benefits of transmit power reduction • Fixed-power rate selection algorithms • Auto Rate Fallback (ARF) • Estimated Rate Fallback (ERF) • Power-controlled rate selection algorithms • Power-controlled Auto Rate Fallback (PARF) • Power-controlled Estimated Rate Fallback (PERF) • Performance evaluation

19. Benefits of Transmit Power Reduction Distance between client and AP: 10m • Lower transmit power supports higher AP density • Determine transmit power for a given AP density control to achieve a certain throughput

20. Fixed-power Algorithm 1: Auto Rate Fallback (ARF) • Intuition: a failed transmission indicates transmission rate too high • A number of packets transmitted successfully => select higher transmission rate • A number of packets dropped => decrease transmission rate • Idle for a certain amount of time => use the highest possible transmission rate for next transmission

21. Fixed-power Algorithm 2: Estimated Rate Fallback (ERF) • Determines highest transmission rate based on SNR • Estimate SNR: tag transmission power, path loss and noise estimate in packets • SNR = txPower – pathloss – noise • Accommodate uncertainty in SNR measurements

22. Power-controlled Algorithms • Each AP acts socially • reduce transmit power (interference to other APs) as long as not reduce its transmission rate • Power-controlled Auto Rate Fallback (PARF) • At certain rate, reduce power level after a number of successful sends • Power-controlled Estimated Rate Fallback (PERF) • Reduce transmit power while maintain the required SNR for the transmission rate

23. Performance Evaluation • Effect of power & rate selection algorithms used by aggressor pair on victim pair

24. Performance Evaluation Aggressor-pair rate limited Aggressor-pair rate unlimited • PERF almost eliminates the interference on the victim pair

25. Introduction Characterizing current 802.11 deployments Impact on end-user performance Limiting the impact of interference Power and rate selection algorithms Conclusions Outline

26. Conclusions • Main characteristics of a chaotic network • Unplanned • Unmanaged • Reduce interference while ensuring robust end-client performance • PERF: reduces transmission power as much as possible without reducing transmission rate