838 chanmgmt lec

8/13/2019 838 Chanmgmt Lec

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A client-driven management approachfor 802.11 (and other) networks

Suman Banerjee

Email: [email protected]

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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AP-2AP-3

What channels to assign to APs?LCCS may assign same to all APs

AP-1


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AP-2AP-3

Correct answer depends on client distribution and association

AP-1


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AP-2AP-3

Correct answer should also adapt with client distributions

AP-1


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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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Iterative approach


Each instant:




(4)

(6)

(0)

(7)


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Iterative approach


Each instant:




(4)

(6)

(0)

(7)

(0) (8)

(0)

(6)

(0)(7)

37

21


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Iterative approach


Each instant:




(4)

(6)

(0)

(7)(0) (0)

(4)

(0)

(9)(0)

37

13

Better


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Iterative approach


Each instant:



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




(2) (2)(0) (0)

(2)(0)


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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-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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A1

A3A2

C1 C2C3

C4

A1

A3A2

C1


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A1

A3A2

C1 C2C3

C4

A1

A3A2

C2


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A1

A3A2

C1 C2C3

C4


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Color all elements s.t. each set has an element with a uniquecolor

A1

A3A2

C1 C2C3

C4


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A1

A3A2

C1 C2C3

C4

A1

A3A2

C2


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A1

A3A2

C1 C2C3

C4

A1

A3A2

C1


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Associate each client to the unique colored AP in its set

A1

A3A2

C1 C2C3

C4


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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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MAC level collisions

LCCS

CFAssign

CFAssign

LCCS


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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]

838 chanmgmt lec

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