Traffic-Aware Channel Assignment in Enterprise Wireless LANs Eric Rozner University of Texas at Austin Yogita Mehta University of Texas at Austin Aditya

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 AustinIEEE ICNP 2007October 18, 2007

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MotivationMotivation• 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

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Related WorkRelated 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

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Motivating ExampleMotivating Example

a b

c d

Demand(b) = 5 Mbps

Demand(d) = 5 Mbps

Demand(a) = 5 Mbps

Demand(c) = 0 Mbps

Traffic-Aware

Channel 1

Channel 6

Channel 6

Channel 11

Channel 1

Channel 11

Channel 6

Channel 1

Traffic-Agnostic

Throughput: 5 Mbps Throughput: 5 Mbps

Throughput: 0 Mbps Throughput: 5 Mbps

Throughput: 15 Mbps

Throughput: 2.5 Mbps Throughput: 5 Mbps

Throughput: 0 Mbps Throughput: 2.5 Mbps

Throughput: 10 Mbps

Traffic-aware channel assignmentcan be beneficial!

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Traffic-Aware FrameworkTraffic-Aware FrameworkMeasure interference graph

Obtain traffic demandsfrom previous interval

Predict demands for current interval

Compute traffic-awarechannel assignment

Change channel assignment

New assignment≠old assignment

Yes

No

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Key Questions to Achieve Key Questions to Achieve Traffic-Aware Channel Traffic-Aware Channel

AssignmentsAssignments• 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?

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Traffic-AwarenessTraffic-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

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Channel Separation MetricChannel 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]

Metric Traffic-agnostic Traffic-aware

Client-

agnostic

Max:∑i,j ∈AP Sepi,j

Max:∑i,j ∈AP Wij × Sepi,j

Client-aware

Max:∑i,j ∈AP∪Clients Sepi,j

Max:∑i,j ∈AP ∪Clients Wij×Sepi,j

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Obtaining Channel Obtaining Channel AssignmentsAssignments

• 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)

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Estimating Traffic DemandsEstimating 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.

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Estimating the Interference Estimating the Interference GraphGraph

• 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

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Non-Binary InterferenceNon-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

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Channel SwitchingChannel 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

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Evaluation MethodologyEvaluation 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

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Synthetic ResultsSynthetic 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

21Traffic-awareness provides benefits under real demands

Trace-Driven ResultsTrace-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%

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Prediction ResultsPrediction ResultsM.A.E. EWMA PREV PEAK2

ResBldg 0.48 0.49 0.70

LibBldg 0.43 0.47 0.57

Prediction algorithms still perform well (EWMA usually within 6%)

Prediction error can be high due to low aggregation

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• TCP results shown, error bars denote standard deviation

• Zipf-like slope (X-axis) generates demands– Higher slope → more uneven the demands

Testbed ResultsTestbed Results

Traffic-awareness beneficial for both fixed-rate and multi-rate

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Channel Switching OverheadChannel 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

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ConclusionConclusion• 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)

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Questions?Questions?

•Thanks!– Eric Rozner– [email protected]

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Non-Binary InterferenceNon-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

Documents

Traffic-Aware Channel Assignment in Enterprise Wireless LANs Eric Rozner University of Texas at Austin Yogita Mehta University of Texas at Austin Aditya