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Designing High Performance Enterprise Wi-Fi Networks

Rohan Murty Harvard University Jitendra Padhye , Ranveer Chandra, Alec Wolman, and Brian Zill Microsoft Research. Designing High Performance Enterprise Wi-Fi Networks. Trends in Enterprise Wi-Fi Networks. Increased adoption and usage [ Forrester ]

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Designing High Performance Enterprise Wi-Fi Networks

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  1. Rohan Murty Harvard University JitendraPadhye, Ranveer Chandra, Alec Wolman, and Brian Zill Microsoft Research Designing High Performance Enterprise Wi-Fi Networks

  2. Trends in Enterprise Wi-Fi Networks • Increased adoption and usage [Forrester] • Culture of mobility: Users tend to use Wi-Fi even when wired connections are available [Gartner, Forrester, Economist] • Move towards an all wireless office Users want wire-like performance from wireless networks

  3. Capacity of Conventional Corporate WLANs • Corporate WLAN Study: • 12 users • < 1 Mbps each

  4. Characteristics of Conventional Corporate WLANs • Focus on coverage • Fewer APs than clients • Clients talk to APs far away; worsens rate anomaly • Clients pick APs to associate with • Use RSSI of beacon packets • Agnostic to channel load at APs • Lack adaptive behavior • No load balancing; fixed channel assignments • Congestion and hotspots worsen

  5. DenseAP • Focus on capacity • Lots of APs; densely deployed • Clients can talk to APs near by; mitigates rate anomaly • Infrastructure picks client-AP associations • Global view of network conditions (channel load, interference, etc.) • Adaptability • Load balance associations; Dynamic channel assignment • Redistributes load away from local hotspots

  6. DenseAP is Practical • No client modifications • Works with legacy clients • Changes limited to the infrastructure • Easy to deploy • Self-managing

  7. DenseAP System Architecture Interface with clients Send summaries to DC DenseAP Nodes (DAPs) Summarized Data Commands Wired Network Commands to DenseAP nodes Summarized Data from DenseAP nodes Associations Channel Assignments Load Balancing DenseAP Central Controller (DC)

  8. Key Challenges • Controlling Associations • Mechanisms • Policy • Dynamic Channel Assignment • Mechanism • Policy • Load Balancing • Mechanism • Policy

  9. Association Control in DenseAP Probe Request Probe Request 00:09:5B:5A:1F:4F Probe Request

  10. Association Control in DenseAP Probe Request MAC = 00:09:5B:5A:1F:4F RSSI = 30 Probe Request MAC = 00:09:5B:5A:1F:4F RSSI = 42 00:09:5B:5A:1F:4F Probe Request MAC = 00:09:5B:5A:1F:4F RSSI = 40

  11. Association Control in DenseAP Probe Response Accept Client 00:09:5B:5A:1F:4F Client only sees one DAP at any given time

  12. Association Policy • What is the quality of a connection between a client and a DAP? (rate) • How busyis the medium around each DAP? Overall goal: Associate client with a DAP that will yield good throughput

  13. A Metric for DAP Selection Expected Transmission-Rate (Mbps) Available Capacity (AC) (Mbps) Free Air Time (%) = X

  14. RSSI = 10 Free air time = 0.22 DAP2 Probe Request Probe Request Probe Response Accept Client RSSI = 30 DAP3 Probe Request Free air time = 0.45 DAP1 RSSI = 20 Free air time = 0.35

  15. RateMap: Estimating Expected Transmission-Rate • Correlation between • RSSI of Probe Request packets • Avg. throughput between a DAP-client pair • Rough approximation - ordering of DAPs • Online profiling method that builds RSSI to data-rate estimates Upload and RSSI correlation = 0.71 Download and RSSI correlation = 0.61

  16. Estimating Free Air Time • Estimate how busy is the medium around at a DAP • Technique similar to ProbeGap* • Measure time taken to finish a packet transmission • Estimates match up closely with offered traffic load *Lakshminarayan et al., 2004 *Vasudevan et al., 2005

  17. Channel Assignment • Integrated into the association process • DAPs not discovered by clients don’t need channels • A DAP is assigned a channel only when it goes from being passive (no clients)to active (services at least one client) • Central controller assigns channel with least load

  18. Re-evaluating Associations • So far, associations when a new client joins the network • No association is perfect • Client traffic demands change • Local hotspots created

  19. Load Balancing • Central controller monitors load on every DAP • When channel load on a DAP crosses a certain threshold • Client causing most load is determined • Moved to less loaded DAP nearby • Ensure client continues to get at least as much available capacity at the new DAP • Load balancing achieved via handoffs • Use association control; manipulate ACLs on DAPs

  20. Results

  21. Testbed 1 Corp AP 24 DAPs 24 Clients 802.11 a/bg

  22. Results: Roadmap • Performance • Density • Channels • Intelligent Association • Load Balancing

  23. Overall DenseAP Performance: 802.11a • Gains due to • More channels • DAP density • Intelligent associations 1250% gain Why?

  24. Exploring the impact of density • Put all DAPs on the same channel • Factors out • Channels • Intelligent Associations: same load on all DAPs • Single out impact of • Density

  25. Impact of Density: Using only 1 channel Higher density provides better performance

  26. Is intelligent association control necessary?

  27. Why does intelligent association matter? • Client-Driven • Disable intelligent association control • Let clients pick DAP to associate with (conventional WLANs) • Compare with DenseAP • Factors out • Channels • Density • Single out impact of • Intelligent association

  28. Necessity of the Association Policy 160% gain Intelligent association policy is necessary

  29. Load Balancing

  30. Load Balancing Client 1 improves Clients 2 & 3 improve Client 1 moved Client 2 moved

  31. Other Details and Results in the Paper • Load balancing algorithm and mechanism • Mobility • Performance • Fewer DAPs • Fewer channels • 802.11g • ….. • Scalability

  32. Related Work • Plenty of prior work on static channel assignment, power control and associations • Each studied each aspect in isolation • Require client modifications [Ramani and Savage, Infocom 2005] • SMARTA [Ahmed et al., CoNext 2006] • Examines channel and power control • Increase overall network capacity • Does not consider associations, load balancing • MDG [Broustis et al., MOBICOM 2007] • Identified tuning channel, power and associations • Studies the order in which these knobs must be tuned • Requires client modifications

  33. Overall Contributions • Practical system • How do density, intelligent association, and more channels affect capacity? • Adaptive system • Future directions • Impact of hidden terminals • Heterogeneous mix of client traffic patterns • Other backhauls: e.g. Wireless, powerline

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