1. Lottery Meets Wireless
Sharad Saha Shravan Rayanchu
Dec 14, 2007

2. The Problem
Flexible, fine-grained bandwidth allocation in enterprise wireless LANs
Motivation
Wireless bandwidth is a scarce resource
Emerging applications require QoS guarantees
Voice-over-IP, Streaming Multimedia
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3. Use case
Enterprise WLAN
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4. Use case
Enterprise WLAN
Media contention
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5. Current Practice IEEE 802.11e provides service differentiation
Four classes: Data, Voice, Video, Best Effort
Not fine grained, (IP, port, protocol), Time
Not dynamic, no proportion
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6. Current Practice IEEE 802.11e provides service differentiation
Four classes: Data, Voice, Video, Best Effort
Limitations
Coarse granularity
Not fine grained, (IP, port, protocol), Time
Not dynamic, no proportion
Dec 14, 2007

7. Current Practice IEEE 802.11e provides service differentiation
Four classes: Data, Voice, Video, Best Effort
Limitations
Coarse granularity
Inflexible
Not fine grained, (IP, port, protocol), Time
Not dynamic, no proportion
Dec 14, 2007

8. Current Practice IEEE 802.11e provides service differentiation
Four classes: Data, Voice, Video, Best Effort
Limitations
Coarse granularity
Inflexible
No concept of proportions
Not fine grained, (IP, port, protocol), Time
Not dynamic, no proportion
Dec 14, 2007

9. Current Practice IEEE 802.11e provides service differentiation
Four classes: Data, Voice, Video, Best Effort
Limitations
Coarse granularity
Inflexible
No concept of proportions
Ignorant of traffic characteristics
Not fine grained, (IP, port, protocol), Time
Not dynamic, no proportion
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10. Our Approach Lottery Scheduling at the Access Point
Policy file defines flows (similar to ACLs) and proportions
Contending flows participate in lottery with tickets given in proportion
Lucky winner gets to send the packet
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11. Does it work ? Fine grained allocation : Flows
Flexibility : Dynamic policies
Proportions : Tickets
Traffic characteristics : Ticket inflation
Scalability : Stateless, Simple
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12. Outline Introduction Lottery : Design and Implementation Evaluation
Conclusions
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13. Lottery Implementation
Define flow:ticket pairs
(Port 5001 : 100)
(IP : 60)
(TCP, Port 80 : 200)
On a TX opportunity, draw a lottery and send packet
Linux Kernel, Madwifi wireless driver, Proc FS for policy inputs
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14. Transmit Path
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15. Transmit Path
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16. Transmit Path
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17. Transmit Path
Currently queuing happens in the firmware
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18. Transmit Path Currently queuing happens in the firmware
A callback function is invoked after every packet transmission
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19. Lottery Implementation
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20. Lottery Implementation
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21. Lottery Implementation
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22. Lottery Implementation
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23. Lottery Implementation
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24. Lottery Implementation
Generic Framework
Lottery Scheduling
Stride Scheduling
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25. Ticket Inflation Packet size affects the throughput
Flow 1 : Flow 2 = 100 : 100
Flow 1 (100 bytes) , Flow (200 bytes)
Effective ratio = 1 : 2
Per-packet Ticket Inflation
Actual Ticket = Ticket * (Base Size) / (Pkt Size)
Flow 1 = 100 * 200/100 = 200
Flow 2 = 200 * 200/200 = 200
Effective ratio = 1 : 1
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26. Outline Introduction Lottery : Design and Implementation Evaluation
Conclusions
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27. Exp #1 : Does it work ? Setup One AP, One client
UDP Flows from AP to client
Flow 1 : Flow 2 = 1 : 2
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28. Exp #1 : Does it work ?
OFF
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29. Exp #1 : Does it work ?
OFF ON
(Desired ratio 2 : 1)
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30. Exp #1 : Does it work ? ON
(Desired ratio 2: 1)
OFF
OFF
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31. Exp #1 : Does it work ? Yes, it works! ON (Desired ratio 2: 1) OFF OFF
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32. Exp #2 : How well does it work ?ON
(Desired ratio 2: 1)
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33. Exp #2 : How well does it work ?
Received Proportion : 1
It works pretty well ! ON
(Desired ratio 2: 1)
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34. Exp #3 : Multiple clients
Setup
One AP, two clients
UDP Flows from AP to clients
Client 1 : Flow 1, Flow 2
Client 2 : Flow 3
Flow 1: Flow 2: Flow3
= 1 : 2 : 4
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35. Exp #3 : Multiple clientsF1
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36. Exp #3 : Multiple clientsF1
F1, F2
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37. Exp #3 : Multiple clientsF1
F1, F2
F1, F2, F3
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38. Exp #3 : Multiple clientsF1
F1, F2
F1, F2, F3
Received Proportion 1 : 2.01 : 3.98
Works well across clients
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39. Exp #4 : Different protocols ?
OFF
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40. Exp #4 : Different protocols ?
OFF ON
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41. Exp #4 : Different protocols ?
Received proportion 1 : 1.93
Works well across protocols
F1, F2
F1, F2, F3
OFF ON
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42. Exp #4 : Effect of packet size
OFF
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43. Exp #4 : Effect of packet sizeON
(NO Inflation)
OFF
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44. Exp #4 : Effect of packet sizeON
(NO Inflation) ON
(with Inflation)
OFF
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45. Exp #4 : Effect of packet size
Agg. Throughput
6.94 Mbps
Agg. Throughput
5.83 Mbps ON
(NO Inflation) ON
(with Inflation)
OFF
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46. Exp #4 : Effect of packet size
F1, F2, F3
Ticket Inflation makes packet size irrelevant
Tradeoff: Efficiency Vs Fairness ON
(NO Inflation) ON
(with Inflation)
OFF
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47. Exp #5 : Effect of Bursty Traffic
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48. Exp #5 : Effect of Bursty Traffic
No Contention
Contention
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49. Exp #5 : Effect of Bursty Traffic
No Contention
Contention
Lottery makes sense when there is contention
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50. Exp #6 : Is it Scalable ?
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51. Lottery is scalable, does fine-grained allocation!
Exp #6 : Is it Scalable ?
Contention
Lottery is scalable, does fine-grained allocation!
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52. Exp #7 : Lottery Vs Stride
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53. Exp #7 : Lottery Vs Stride
Stride is more accurate than Lottery
But needs more state, processing
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54. Exp #8 : TCP and Lossy Links
Bad Links
Good Links
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55. Conclusions Lottery Scheduling
Flexible, Fine-grained bandwidth allocation
Works across different clients, packet sizes, traffic types, protocols
Interaction with TCP in presence of lossy links
Simple and a powerful concept
Dec 14, 2007