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
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
2
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
Approaches assume network traffic is static or uniform!
• Wireline traffic engineering
– Benefits of traffic-awareness [Awduche99, Awduche02,
Xiao00]
Our Contribution: Effective channel assignment
schemes that adapt to prevailing WLAN traffic demands 3
Motivating Example
Traffic-Agnostic
Throughput: 10 Mbps
Demand(a) = 5 Mbps
Channel 1
a
Throughput: 2.5
5 Mbps
Mbps
Traffic-Aware
Throughput: 15 Mbps
Demand(b) = 5 Mbps
b
Channel 6
Throughput: 5 Mbps
Traffic-aware channel assignment
canc be beneficial!
d
Demand(c) = 0 Mbps
Channel 11
6
Throughput: 0 Mbps
Demand(d) = 5 Mbps
Channel
Channel11
1
Throughput:
Throughput:2.5
5 Mbps
Mbps
4
Traffic-Aware Framework
Measure interference graph
Obtain traffic demands
from previous interval
Predict demands for current interval
Compute traffic-aware
channel assignment
New assignment≠
old assignment
Yes
Change channel assignment
No
5
Key Questions to Achieve TrafficAware 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?
6
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
7
Channel Separation Metric
• SepA,B = min(|chan(A) - chan(B)|, 5) if A, B interfere
= 5 otherwise
Metric
Traffic-agnostic
Traffic-aware
Clientagnostic
Max:
∑i,j ∈AP Sepi,j
Max:
∑i,j ∈AP Wij × Sepi,j
Max:
Max:
∑i,j ∈AP ∪Clients Wij×Sepi,j
Clientaware
∑i,j ∈AP∪Clients Sepi,j
• Traffic-awareness can be applied to other metrics
• Finding optimal solution is NP-Hard [Mishra06]
8
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
9
Key Questions to Achieve TrafficAware 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?
10
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.
11
Key Questions to Achieve TrafficAware 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?
12
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 13
Key Questions to Achieve TrafficAware 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?
14
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
15
Key Questions to Achieve TrafficAware 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?
16
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 loss17
Key Questions to Achieve TrafficAware 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?
18
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
19
– Study TCP/UDP and fixed rate/auto rate
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]
20% of runs:
At least 8.5% improv
20% of runs:
At least 33% improv
Higher benefit when traffic-distribution is more uneven20
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%
21
Traffic-awareness provides benefits under real demands
Prediction Results
M.A.E.
EWMA
PREV
PEAK2
ResBldg
0.48
0.49
0.70
LibBldg
0.43
0.47
0.57
Prediction error can be high due to low aggregation
Prediction algorithms still perform well
(EWMA usually within 6%)
22
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
23
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
24
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)
25
Questions?
•Thanks!
– Eric Rozner
– erozner@cs.utexas.edu
26
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
27