Time-based Fairness Improves Performance in Multi-rate WLANs

1. Time-based Fairness Improves Performance in Multi-rate WLANs Godfrey Tan and John Guttag MITComputer Science & Artificial Intelligence Laboratory

2. Outline • Multi-rate WLANs support variable rates • Problems with throughput-based fairness • Alternate notion: time-based fairness • What it is • Why it’ s good • How to achieve it (Time-based Regulator) • Evaluation

3. WLANs Facilitate Varying Speeds Sending at 5.5 Mbps is better TCP Throughput with RTS/CTS (Mbps) • Tradeoff between data rate and loss rate • Multiple standards compete in same channel • e.g. 802.11b vs. 802.11g

4. AP sees Multiple Rates Percentage of Bytes Transmitted • Card manufactures implement auto-rate protocols • Varying channel conditions at clients lead to rate diversity

5. Aggregate Throughput Reduced

6. Aggregate Throughput Reduced

7. Aggregate Throughput Reduced • Total throughput lower than expected • Faster node suffers • Slower node benefits • Less incentive to upgrade to 802.11g

8. n1 n1’s transmission time n2’s Root Cause: DCF’s “Fairness” Notion • Carrier sense multiple access protocol • Distributed randomized access • Goal: equal number of frame transmissions • Aim seems to be throughput-based fairness • Assuming equal frame size and loss rate • Irrespective of frame transmission time • Consequence: Aggregate thruput closer to slower node’s

9. 1 Ri = jI 1 j Throughput-based Fairness (RF) • Nodes achieve equal throughputs • Suitable for • Wired networks • Single-rate wireless LANs Ri: i’s achieved throughput j:j’s maximum achievable throughput I: the set of competing nodes

10. Not Efficient; Maybe Not Fair • Throughput of node ishould depend upon • Number of competing nodes • Transmission strategy used by node i • Should not depend upon • Transmission strategies used by other nodes • Channel time is the shared resource • Transmission opportunities are not

11. i Ri = |I| n1 n1 n2 Time-based Fairness (TF) • Nodes achieve equal channel time shares Ri: i’s achieved throughput i: i’s maximum achievable throughput I: the set of competing nodes • Desirable in multi-rate WLANs • Node’s throughput depends only upon • Its transmission strategy • Number of competing nodes

12. n2 at 11 Mbps n1 at 11Mbps RF TF RF TF Throughputs Unchanged in Single-rate WLANs 11vs11 5.5vs5.5 1vs11 2vs2 1vs1

13. TF Improves Throughput in Multi-rate WLANs • Total throughput improves by 115% • Faster node achieves 273% more • Slower node achieves 42% less 11vs11 1vs11 1vs1

14. TF does not favor slower nodes • Under RF, n1 achieves 84% of channel time • Under TF, each node achieves 50% 11vs11 1vs11 1vs1

15. Outline • Multi-rate WLANs support variable rates • Problems with throughput-based fairness • Alternate notion: time-based fairness • What it is • Why it’ s good • How to achieve it (Time-based Regulator) • Results

16. How to Achieve Time-based Fairness? • Is tweaking DCF enough? • Each node still achieves equal chance to transmit • Number of transmissions depends on data rate • Faster node can transmit more in each opportunity • No! Not enough for AP-based WLANs! • Downlink frames are transmitted at varying data rates • Existing queuing schemes lead to thruput-based fairness • AP's queuing scheme needs modifications

17. How to Achieve Time-based Fairness? • Is having N queues at the AP enough? • One queue for each data rate • Faster queue gets dequeued more in each round • Dequeue 6 packets from 11-Mbps-queue & 1 from 1-Mbps-queue • No! • Non-uniform client distribution at queues problematic • E.g. 6 users at 11 Mbps and 1 user at 1 Mbps leads to RF • Per-client queuing, monitoring and policing necessary

18. Our Time-based Regulator (TBR) • AP shapes traffic to clients, i.e. downlink only • Monitors channel time usage of each client • Account both downlink and uplink traffic • Deal with differing loss rates and varying demands • Transmit frames to node i • Only if it has not utilized its share of channel time

19. Is Shaping Downlink Traffic Enough? • Yes for feedback-based congestion controlled apps • Limiting rate of downlink traffic slows sending rate • Regardless of clients’ traffic directions • E.g. Applications using TCP, RTCP, etc. • No for non-congestion controlled apps • Modify clients so that AP can ask them to slow down • Drop packets if clients do not react appropriately • E.g. Applications using raw UDP • TCP makes up 90% of WLAN traffic [Tang02,Kotz02]

20. A TBR Implementation • Only runs at AP; No modifications to clients • Uses leaky buckets to shape downlink traffic • Sets up a queue for each client • Works with DCF • Implemented in Linux HostAP Driver

21. TBR Impelementation Cont. • tokensi : available channel time (seconds not bits) • ratei : channel time share (e.g. 1/n) • bucketi : maximum amount of tokens • Policing: • Packet to node i is transmitted if tokensi> 0 • Tokens are periodically filled at ratei • Accounting: • For each packet P transmitted, tokensi -= chantime(P)

22. Example: TBR Operations At t = 0, rate1 = 0.5 rate2 = 0.5 tokens1 = 0.025 tokens2 = 0.025 AP 11 1 11 1 Mbps 11 At t = 0.074, tokens1 = 0.025 + 0.037 – 0.062= 0 tokens2 = 0.025 + 0.037 – 0.012 = 0.05 TCP Data 1 Mbps 1 11 11 TCP Ack TCP Ack 1 Mbps 1 11 TCP Data 11 11 1 From this time onwards, n1 can only use 50% of channel time. n1 n2

23. Computing Channel Occupancy Time • Total time used to transfer each layer-2 frame layer-2 frame layer-2 ack Idle Per-frame Channel Occupancy Time • Take into account retransmissions • AP knows lost frames in downlink direction • For uplink direction • Client marks each header with retry info., or • AP estimates based on heuristics

24. Dealing with Varying Traffic Conditions • Not all nodes need 1/n of capacity • Achieves time-based max-min allocation • Smallest ratei must be as large as possible • Second smallest ratej must be as large as possible, etc. • Periodically adjusts ratei to fully utilize channel • If under-utilized, ratei is reduced • Excess capacity redistributed among other nodes

25. TBR Achieves Higher Downlink Throughputs TBR achieves higher throughputs as analytically predicted 5.5vs11 2vs11 1vs11

26. TBR Achieves Higher Uplink Throughputs 5.5vs11 2vs11 1vs11

27. Related Work • Performance anomaly of 802.11b • [Heusse et al., Infocom03] • Opportunistic MAC protocol • [Sadeghi et al., Mobicom02] • 802.11e Qos Support (being drafted)

28. Conclusions • Time-based fairness is desirable • Better overall system performance • In terms of throughput and completion time • Faster nodes see significant improvement • More incentive to upgrade to 11g • Slower nodes not penalized severely • APs shape downlink traffic to achieve TF • Uplink & downlink must both be considered • No modifications to clients or DCF necessary

29. TF Improves Wait Time in Multi-rate WLANs • n1 transfers X bytes @1 Mbps at t0 • n2 transfers X bytes @11 Mbps at t0 • Under time-based fairness, • n2 completes earlier at t1 • n1 completes later at t2 • Under throughput-based fairness • Both n1 and n2 complete at t2 t0 t1 t2 n2 n1 n2 n1 % of channel time used by n1

30. TBR Keeps Channel Utilization High • Achieves max-min allocation • Adapts to varying demands • Redistributes excess capacity of underutilized nodes

31. TDMA • TDMA provides equal time slots to clients each round • Converges to time-based fairness • If every node utilizes the entire slot each round • Not very suitable for bursty traffic • Time-based fairness notion: • Provides predictable long-term channel time shares • Under bursty traffic, varying demands and loss rates • Compatible with any MAC protocol • CSMA (e.g. DCF) • TDMA (e.g. HiperLAN)

32. TF does not favor slower nodes 11vs11 1vs11 1vs1

33. Traffic Models • Fluid Model • Finite number of flows transfer infinite streams • Efficiency measured by aggregate throughput • Corresponds to very busy networks • Task Model • Finite number of flows transfer finite number of bits • Efficiency measured by average & final completion time • Corresponds to sometimes congested networks

34. Comparison

35. Long-term Time-share Guarantees Necessary