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 g 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 Motivating Example ab cd Demand(b) = 5 Mbps Demand(d) = 5 Mbps Demand(a) = 5 Mbps Demand(c) = 0 Mbps Traffic-Aware Channel 1 Channel 6 Channel 11 Channel 1 Channel 11 Channel 6 Channel 1 Traffic-Agnostic Throughput: 5 Mbps Throughput: 0 MbpsThroughput: 5 Mbps Throughput: 15 Mbps Throughput: 2.5 MbpsThroughput: 5 Mbps Throughput: 0 MbpsThroughput: 2.5 Mbps Throughput: 10 Mbps Traffic-aware channel assignment can be beneficial!

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

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 –S A - Node A’s sending demands –R A - Node A’s receiving demands W A,B = S A ×S B + S A ×R B + S B ×R A –1 st term: sender-side interference MAC is CSMA/CA: One sender at a time –2 nd and 3 rd terms: interference at receivers Collisions increase loss, contention window

8 Channel Separation Metric Sep A,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] MetricTraffic-agnosticTraffic-aware Client- agnostic Max: ∑ i,j ∈ AP Sep i,j Max: ∑ i,j ∈ AP W ij × Sep i,j Client- aware Max: ∑ i,j ∈ AP ∪ Clients Sep i,j Max: ∑ i,j ∈ AP ∪ Clients W ij ×Sep i,j

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, g); 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 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 20% of runs: At least 33% improv 20% of runs: At least 8.5% improv

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

22 Prediction Results M.A.E.EWMAPREVPEAK2 ResBldg LibBldg Prediction algorithms still perform well (EWMA usually within 6%) Prediction error can be high due to low aggregation

23 TCP results shown, error bars denote standard deviation Zipf-like slope (X-axis) generates demands –Higher slope → more uneven the demands Testbed Results 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

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 –LocInterf = 2 − 2 × BR; //map BR to range –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