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A client-driven management approachfor 802.11 (and other) networks
Suman Banerjee
Email: suman@cs.wisc.edu
http://www.cs.wisc.edu/~suman
Department of Computer Sciences
University of Wisconsin-Madison
Wisconsin Wireless and NetworkinG Systems (WiNGS) Laboratory
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Wireless devices
Experiencing phenomenal growth
Dell Oro group prediction:
wireless LAN sales will grow 47% annually
through 2008. Wireless LAN industry annual sales is more than 2
billion dollar industry in the US
Increasing deployment of Access Points (APs) inoffices, homes, neighborhoods, etc.
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Wireless LAN coverage
Chicago area
Bay area
A handful of hotspots in 1998
Today: more than 2.5 million hotspots just in urban areas *
* Source: war-driving reports in wigle.net
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Management objectives
Reduce costs
Eliminate the human in the loop
Improve performance
At the clients
Problem is inherently hard
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Management in wired networks
Mostly performed through central entities
Firewalls
Nameservers
DHCP servers
A logical approach for many basic networking tasks
But needs some re-thinking in the wireless domain
Many properties in wireless domain are location-specific
Can only be observed at the clients and by the clients
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Impact of location
Sent: 1, 2, 3, 4, 5
Client-A
AP-1
AP-2Recvd: 1, 3, 4, 5
Recvd: 1, 2, 4
Experience is property of location and cannot be always replicated
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Talk outline
Introduction
Client-driven management example
Channel assignment and load balancing in wireless LANs
An architecture for client-driven management
Virtualized wireless grids
Other examples within this architectural framework
Secure localization
Network management: fault monitoring and diagnosis
Fast handoffs
Summary of other activities in WiNGS
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Channel assignment in WLANs
Current best practices
RF site survey based approaches
Fairly tedious signal strength maps of the area under consideration
Least Congested Channel Search (LCCS)
Each AP examines congestion-level in a channel
If high congestion (i.e., it hears other APs), it tries to move to different channel
Repeat the process
Other proprietary approaches (Airespace)
None of them are client-centric in nature
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Channel assignment problem
AP-2AP-3
What channels to assign to APs?
AP-1
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Channel assignment problem
AP-2AP-3
What channels to assign to APs?LCCS may assign same to all APs
AP-1
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Channel assignment problem
AP-2AP-3
Correct answer depends on client distribution and association
AP-1
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Channel assignment problem
AP-2AP-3
Correct answer should also adapt with client distributions
AP-1
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Channel assignment problem
AP-2AP-3
AP-1
Correct answer should also adapt with client distributions
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A possible client-driven approac
Client provide feedback to about observed interference
Construct a virtual graph and do weighted graph coloring
And then minimize graph weight
AP-1
AP-2
AP-3
(4)(2)
(0)
Edge weight
corresponds to
number of
interfered
clients
Higher edge weight
implies greater importance
of assigning APs to
different channels
[Vertex coloring: MC2R05]
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Graph coloring approach
Iterative approach
Start with any initial coloring (even derived from LCCS)
Each instant:
Pick an edge with maximum contribution to graph weight
Re-assign channel of one of its APs with a minimization objective
Leads to reduction to total graph weight(20) (0)
(4)
(6)
(0)
(7)
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Graph coloring approach
Iterative approach
Start with any initial coloring (even derived from LCCS)
Each instant:
Pick an edge with maximum contribution to graph weight
Re-assign channel of one of its APs with a minimization objective
Leads to reduction to total graph weight(20) (0)
(4)
(6)
(0)
(7)
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Graph coloring approach
Iterative approach
Start with any initial coloring (even derived from LCCS)
Each instant:
Pick an edge with maximum contribution to graph weight
Re-assign channel of one of its APs with a minimization objective
Leads to reduction to total graph weight(20) (0)
(4)
(6)
(0)
(7)
(0) (8)
(0)
(6)
(0)(7)
37
21
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Graph coloring approach
Iterative approach
Start with any initial coloring (even derived from LCCS)
Each instant:
Pick an edge with maximum contribution to graph weight
Re-assign channel of one of its APs with a minimization objective
Leads to reduction to total graph weight(20) (0)
(4)
(6)
(0)
(7)(0) (0)
(4)
(0)
(9)(0)
37
13
Better
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Graph coloring approach
Iterative approach
Start with any initial coloring (even derived from LCCS)
Each instant:
Pick an edge with maximum contribution to graph weight
Re-assign channel of one of its APs with a minimization objective
Leads to reduction to total graph weight
Algorithm converges Every step we are reducing the graph weight
Stops when cannot reduce further
(20) (0)
(4)
(6)
(0)
(7)
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Vertex coloring approach
Client provide feedback to about observed interference
Construct a virtual graph and do weighted graph coloring
Minimize: Wt of graph
Evaluation insimulations and ondeployed testbed
of 70+ APsLCCS
Vertex coloring
Number of channels
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Limitations of vertex coloring
Overly conservative:
Does not examine how client-AP associations should be made
?
?
?
For conflict freedom, how many channels do we need?
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(3)
(0)
(0)
For conflict freedom, need 3 channels?
It depends on client association
Overly conservative:
Does not examine how client-AP associations should be made
Limitations of vertex coloring
(2) (2)(0) (0)
(2)(0)
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Overly conservative:
Does not examine how client-AP associations should be made
Limitations of vertex coloring
We should look at load-balancing (AP-client association) too!
In this paper we define channel managementto be:
Channel assignment + load balancing through client-AP associations
(3)
(0)
(0) (2) (2)(0) (0)
(2)(0)
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Conflict set coloring approach
CFAssign algorithms
Jointly solve channel assignment and load balancing
through client association
Problem formulated as a set coloring problem, where
each client is a set, and each AP is an element in one or
more sets
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Conflict set coloring approach
Conflict-free set coloring formulation (a simplified view) Each client is a set of one or more APs
A1
A3A2
C1 C2C3
C4
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Conflict set coloring approach
Conflict-free set coloring formulation (a simplified view) Each client is a set of one or more APs
A1
A3A2
C1 C2C3
C4
A1
A3A2
C1
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Conflict set coloring approach
Conflict-free set coloring formulation (a simplified view) Each client is a set of one or more APs
A1
A3A2
C1 C2C3
C4
A1
A3A2
C2
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Conflict set coloring approach
Conflict-free set coloring formulation (a simplified view) Each client is a set of one or more APs
A1
A3A2
C1 C2C3
C4
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Conflict set coloring approach
Conflict-free set coloring formulation (a simplified view) Each client is a set of one or more APs
Color all elements s.t. each set has an element with a uniquecolor
A1
A3A2
C1 C2C3
C4
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Conflict set coloring approach
Conflict-free set coloring formulation (a simplified view) Each client is a set of one or more APs
A1
A3A2
C1 C2C3
C4
A1
A3A2
C2
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Conflict set coloring approach
Conflict-free set coloring formulation (a simplified view) Each client is a set of one or more APs
A1
A3A2
C1 C2C3
C4
A1
A3A2
C1
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Conflict set coloring approach
Conflict-free set coloring formulation (a simplified view) Each client is a set of one or more APs
Color all elements s.t. each set has an element with a uniquecolor
Associate each client to the unique colored AP in its set
A1
A3A2
C1 C2C3
C4
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Conflict set coloring approach
Conflict-free set coloring formulation (a simplified view) Each client is a set of one or more APs
Color all elements s.t. each set has an element with a uniquecolor
Associate each client to the unique colored AP in its setA1
A3A2
C1 C2C3
C4
This is a conflict-free assignment of clients to APs(Prior vertex coloring approach will have used 3 colors)
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Details
What if conflict-freedom cannot be guaranteed?
Minimize the amount of conflict
Load balancing fits into this objective function
It increases with number of clients added to the same AP
Handle client-client interference
Sets consist of APs both in direct and indirect interference
[Range and Interference sets]
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A centralized algo (CFAssign-RaC)
Pick an AP ordered by a random permutation
Perform compaction step
For that AP, pick the best color assignment that maximizes thenumber of conflict-free clients based on the set formulation
Repeat with another AP
Can be repeated multiple times to obtain best solution
Also have two distributed algorithms
[See our upcoming Mobicom 2006 paper]
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Implementation details
Feedback from clients to APs (infrastructure) usesmechanisms available in IEEE 802.11k standards
Site report
Process is periodic in general, but triggered by clientmobility
Implementation is easy (~100 lines of code)
Channel switching can be made quite fast
< 1 ms latency is achievable (ongoing work)
New Intel cards promising very fast switching (~ 100 us)
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CFAssign (Set approach)
Throughput
Std-dev of throughput even indicates greater fairness
> factor
of 2
Vertex coloring
Vertex coloring
CFAssign
CFAssign
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CFAssign (Set approach)
MAC level collisions
LCCS
CFAssign
CFAssign
LCCS
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CFAssign (Set approach)
Adaptation to node mobility (3 channels)
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We can do EVEN better!
Should we restrict to non-overlapped
channels?
In 802.11b: 1, 6, and 11
By using partially-overlapped channels
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We can do EVEN better!
Should we restrict to non-overlapped channels? In 802.11b: 1, 6, and 11
How about 1, 4, 7, 11? These are partially-overlapped channels
Tradeoff between increased interference due to partially overlappedchannels and more efficient utilization of spectrum
Questions: Can we define a mechanism to systematically model interference of partially-
overlapped channels and extend existing channel assignment algorithms?
What performance improvement can we expect?
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Talk outline
Introduction
Client-driven management example Channel assignment and load balancing in wireless LANs
Partially overlapped channels and how to use them
An architecture for client-driven management Virtualized wireless grids
Other examples within this architectural framework Secure localization
Network management: fault monitoring and diagnosis Fast handoffs
Summary of other activities in WiNGS
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Wireless channels
Wireless communication happens over a restricted setof frequencies
Collectively they constitute a channel
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Wireless channels
Available spectrum is typically divided intodisjoint channels
Radio Frequency Spectrum
Channel A Channel B Channel C Channel D
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Partially Overlapped Channels
IEEE 802.11 defines 11 partially overlapped channels in 2.4GHz band
Only channels 1, 6 and 11 are non-overlapping
54 / 12 partially overlapped / non-overlapping channels in 5
GHz ISM band
2.4 GHz ISM BandCh 1 Ch 6 Ch 11
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Partially Overlapped Channels
Partially overlapped channels are avoided
In order to avoid such interference
Ch 1 Ch 6Ch 3
Amount of Interference
Link A Ch 1
Link C Ch 6
Link B Ch 3?
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Simple Experiment
Link A Ch 1
Link B Ch X
Channel Separation
5
210
Non-overlapping channels, A = 1, B = 6Partially Overlapped Channels, A = 1, B = 3
Partially Overlapped Channels, A = 1, B = 2
Same channel, A = 1, B = 1
LEGEND
3
4
5
6
0 10 20 30 40 50 60
Distance (meters)
UDP
Throughput(Mbps)
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Define Interference Factoror I-factor
Transmitter is on channel j
Pj denotes power received on channel j
Pi denotes power received on channel I
Captures amount of overlap between channels
I-Factor : Model for Partial Overlap
Pi
Pj
I-factor(i,j) =
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How do we use I-Factor ?
Given I-Factor Node B1 can `estimateinterference on all partially overlapped
channels
And choose the best one!
Link A Ch 1
Link B Ch X
A1 A2
B1 B2
PX= I-Factor(1,X) * P1
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Can we estimate I-factor?
Measurement is an active process
Best if avoided
We have designed a simple model of I-factor that is based onthe transmit spectrum mask (IEEE standards specified) and thereceivers band-pass filter profile
Fc Fc + 22 MhzFc - 22
Maximum power
Fc + 10 Mhz
Amount of powerreceived on Fc + 10
centered at Fc + 10Band-pass filter
Logscale
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Estimating I-Factor
Actual frequency response is hard to compute
Transmit Spectrum Mask specified by IEEE802.11
Fc +11 Mhz +22 Mhz-11 Mhz-22 Mhz
-30 dB
-50 dB -50 dB
-30 dB
0 dB
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Estimating I-Factor
Empirical Estimation:Measure Piand Pj
Take multiple samples
Calculate I-Factor = Pi/ Pj
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10 12
Norma
zeI
-actor
Receiver Channel
I(theory)
I(measured)
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Overall methodology
Wireless communication technologySuch as 802.11, 802.16
Estimate I-factorTheory/empirical
I-Factor
Model
Algorithm for
channel assignment
Channel assignmentwith overlapped channels
Estimated once per
wireless technology
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How much Improvement to Expect ?
Randomly distributed nodes
Ad-hoc single hop network
M channels in all, N non-overlapping
M = 5*N - 4 for 802.11 (2.4 and 5 GHz)
Throughput Improvement = 5 N 41.2 N
= a factor of 3.05 for 802.11 channels !
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Can we use POV to pack more APs?
Square grid, clients distributed uniformly at random Compare between:
3 non-overlapping channels 1, 6, 11
4 partially-overlapping channels 1, 4, 7, 11
Same amount of wireless spectrum being used
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Systematic scenario
Three channels, the best case - three clique(three colorable)
1
611
11
1
0
0.2
0.4
0.6
0.8
1.0
400 600 800 1000
3 channels
i i
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Systematic scenario
Four partially overlapped channels: 1, 4,7, 11
Use four clique, to cover the same region
More APs can be placed closer
Use I-factor to compute optimal placement
1
115
7
1
10000
0.2
0.4
0.6
0.8
1.0
400 600 800
3 channels
4 POV channels
5
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Arbitrary Wireless LAN
Modifications to existing CFAssign algorithm
High density random topologies
2.6 x
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Modifications to CFAssign algorithm
Low density random topologies
1.7 x
Arbitrary Wireless LAN
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Summary of channel assignment
Adaptation
Better spectrum re-use
Solution implicitly solves Client-AP association Extensions also provide load balancing
Interoperates with legacy systems
Even systems that do not implement CFAssign benefit
See papers [Infocom 2006], [MC2R 2005], [IMC 2005],[Mobicom 2006]
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