Traffic-Aware Channel Assignment in Enterprise Wireless LANs

1. Traffic-Aware Channel Assignment in Enterprise Wireless LANs Eric Rozner University of Texas at Austin Yogita Mehta University of Texas at Austin Aditya Akella University of Wisconsin-Madison Lili Qiu University of Texas at Austin IEEE ICNP 2007 October 18, 2007

2. Motivation • Increasing campus & enterprise WLAN popularity • Laptops, smart phones, wireless gaming consoles, etc • Increased density and usage → interference • Limited number of non-overlapping channels • 802.11b and 802.11g only have 3 (1, 6, and 11) • Not always feasible to assign non-overlapping channels

3. Related Work • Previous channel assignment schemes • Manual configuration [Grier] • Maximize RSS at expected high-demand points [Lee02] • Client-side interference [Mishra06] • Commercial products [AutoCell, AirView] • No public information due to proprietary nature • Wireline traffic engineering • Benefits of traffic-awareness [Awduche99, Awduche02, Xiao00] Approaches assume network traffic is static or uniform! Our Contribution: Effective channel assignment schemes that adapt to prevailing WLAN traffic demands

4. Traffic-Agnostic Traffic-Aware Throughput: 10 Mbps Throughput: 15 Mbps Demand(a) = 5 Mbps Demand(b) = 5 Mbps Channel 1 Channel 1 Traffic-aware channel assignmentcan be beneficial! Channel 6 Channel 6 Throughput: 5 Mbps Throughput: 2.5 Mbps Throughput: 5 Mbps Throughput: 5 Mbps a b Demand(c) = 0 Mbps Demand(d) = 5 Mbps c d Channel 11 Channel 6 Channel 11 Channel 1 Throughput: 0 Mbps Throughput: 0 Mbps Throughput: 2.5 Mbps Throughput: 5 Mbps Motivating Example

5. Traffic-Aware Framework Measure interference graph Obtain traffic demandsfrom previous interval Predict demands for current interval Compute traffic-awarechannel assignment New assignment≠ old assignment No Yes Change channel assignment

6. Key Questions to Achieve Traffic-Aware Channel Assignments • How to develop traffic-aware channel assignment algorithms? • How to estimate traffic that varies over time? • How to estimate the interference graph? • How to handle non-binary interference? • How to efficiently change channels? • How much does traffic-awareness improve network performance and when is it beneficial?

7. Traffic-Awareness • Weigh interference metric by traffic demands • SA - Node A’s sending demands • RA - Node A’s receiving demands • WA,B = SA×SB + SA×RB + SB×RA • 1st term: sender-side interference • 802.11 MAC is CSMA/CA: One sender at a time • 2nd and 3rd terms: interference at receivers • Collisions increase loss, contention window

8. Channel Separation Metric • SepA,B= min(|chan(A) - chan(B)|, 5) if A, B interfere = 5 otherwise • Traffic-awareness can be applied to other metrics • Finding optimal solution is NP-Hard [Mishra06]

9. Obtaining Channel Assignments • Initialization algorithm • Inspired by Chaitin’s approach to register allocation problem [Chaitin82] • Basic notion: Wait to assign channels of APs with many conflicts b/c such assignments are more important • Simulated annealing to improve initial assignment • Randomly change channel of one AP and its clients • If metric improves, select current assignment; If not, select it with some non-zero probability P • Probability P decreases as # iterations increases • Output: best assignment over all iterations • We use 1000 iterations (computation << 1 second)

10. Key Questions to Achieve Traffic-Aware Channel Assignments • How to develop traffic-aware channel assignment algorithms? • How to estimate traffic that varies over time? • How to estimate the interference graph? • How to handle non-binary interference? • How to efficiently change channels? • How much does traffic-awareness improve network performance and when is it beneficial?

11. Estimating Traffic Demands • Measure past traffic demands • Most commercial APs export SNMP interface • SNMP provides demands in 5 min intervals • Predict current demands based on history • EWMA: Exponentially-weighted moving average • PREV: Use previous interval’s demands • PREV_N: Find channel assignment that’s optimized over past N intervals • PEAK_N: Find channel assignment that’s optimized over the worst case in past N intervals.

12. Key Questions to Achieve Traffic-Aware Channel Assignments • How to develop traffic-aware channel assignment algorithms? • How to estimate traffic that varies over time? • How to estimate the interference graph? • How to handle non-binary interference? • How to efficiently change channels? • How much does traffic-awareness improve network performance and when is it beneficial?

13. Estimating the Interference Graph • Measure max throughput on any 2 links [Padhye05] • A’s max broadcast rate when it sends alone • A’s max broadcast rate when it sends with node B • BR = Total throughput together/Total throughput alone • BR close to 0.5 → A, B interfere (take turns sending), close to 1.0 → A, B don’t interfere • Estimate max throughput on any 2 links via an interference model [Reis06] • Estimate max throughput on any set of links via a general interference model [Qiu07] • Use coordinated probing [Ahmed06] • Further improvement of interference graph estimation directly benefits our channel assignment

14. Key Questions to Achieve Traffic-Aware Channel Assignments • How to develop traffic-aware channel assignment algorithms? • How to estimate traffic that varies over time? • How to estimate the interference graph? • How to handle non-binary interference? • How to efficiently change channels? • How much does traffic-awareness improve network performance and when is it beneficial?

15. Non-Binary Interference • Interference can be non-binary in practice • Variations in RSS cause intermittent interference • SNR under one sender ≥ SNR_Threshold • SNR under two (or more) senders ≤ SNR_Threshold • Extend the channel assignment metric to handle non-binary interference • Degree of interference is weighed by the throughput reduction based on BR

16. Key Questions to Achieve Traffic-Aware Channel Assignments • How to develop traffic-aware channel assignment algorithms? • How to estimate traffic that varies over time? • How to estimate the interference graph? • How to handle non-binary interference? • How to efficiently change channels? • How much does traffic-awareness improve network performance and when is it beneficial?

17. Channel Switching • Switching delay - hardware (AP & client) • 200μs Intel ProWireless • 10-20ms Netgear Atheros, Cisco Aironet, Prism 2.5 • Re-association delay - software (client only) • Default: clients scan all channels to assoc. • Scanning time dominates (100’s of ms [Ramani05]) • Explicit Notification: APs broadcast channel • Can send multiple times to protect against loss • We send 5 times for our switching results

18. Key Questions to Achieve Traffic-Aware Channel Assignments • How to develop traffic-aware channel assignment algorithms? • How to estimate traffic that varies over time? • How to estimate the interference graph? • How to handle non-binary interference? • How to efficiently change channels? • How much does traffic-awareness improve network performance and when is it beneficial?

19. Evaluation Methodology • NS-2 Simulation • Synthetic traces: when traffic-awareness is beneficial • Trace-driven simulations: more realistic settings • SNMP data from Dartmouth 2004 and IBM 2002 traces • 1024 UDP packet + fixed rate • Testbed Experiments • 25 nodes (MadWifi, 802.11g); 2 floors of office building • Run at night to avoid interference from resident WLAN • Empirically measure non-binary interference graph • Study TCP/UDP and fixed rate/auto rate • Performance metric: total throughput and fairness

20. 20% of runs: At least 33% improv 20% of runs: At least 8.5% improv Synthetic Results • Uniform: AP demands uniform over [0:MAX] • Hotspot: Pick 1 AP & all other APs in range as a hotspot, Hotspot APs uniform: [0:MAX]; others: [0:LOW] Higher benefit when traffic-distribution is more uneven

21. Trace-Driven Results • Compare against client-agnostic/traffic-agnostic baseline • Average improvements against baseline over 3 buildings: • Traffic-aware, client-agnostic: 5.2-11.5% • Traffic-aware, client-aware: 8.3-12.8% Traffic-awareness provides benefits under real demands

22. Prediction Results Prediction error can be high due to low aggregation Prediction algorithms still perform well (EWMA usually within 6%)

23. Testbed Results • TCP results shown, error bars denote standard deviation • Zipf-like slope (X-axis) generates demands • Higher slope → more uneven the demands Traffic-awareness beneficial for both fixed-rate and multi-rate

24. Channel Switching Overhead • Measure AP-Client throughput over a 10 minute transfer • Vary frequency of switching AP’s channel • Examine different levels of client activity Overhead is minimal for ≥ 2 min switching interval

25. Conclusion • Main contributions • Traffic-aware channel assignment algorithms in WLANs • Considered several practical issues • Measure wireless interference • Cope with realistic wireless interference patterns • Measure & predict traffic demands • Minimize the overhead of channel switching • Extensive evaluation via simulations and experiments • Traffic-awareness benefits under uneven demand distribution • Traffic-awareness benefits TCP/UDP and Fixed/Multi-Rate • Future work • Develop traffic-aware techniques for other wireless network operations (e.g. power control, routing)

26. Questions? • Thanks! • Eric Rozner • erozner@cs.utexas.edu

27. Non-Binary Interference • BR metric review: • BR = Total throughput together/Total throughput alone • BR close to 0.5 → A, B interfere (take turns sending), close to 1.0 → A, B don’t interfere • Extend the BR metric: • BR = min(1, max(0.5, BR)); //BR in range 0.5 .. 1 • LocInterf = 2 − 2 × BR; //map BR to range 0 .. 1 • ChannelDiff = min(|Ci − Cj|, 5); • ChannelInterf = 1 − ChannelDiff × 0.2; • OverallInterf = ChannelInterf × LocInterf ; • Traffic-aware, client-agnostic metric becomes: • Min: ∑i,j∈AP W × OverallInterf(i, j) //others follow