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http://www.winlab.rutgers.edu/WINTECH2009/Index.html

Online Optimization of 802.11

Wireless Mesh Networks

Theodoros Salonidis

with: Georgios Sotiropoulos, Ramesh Govindan, and Roch Guerin

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Thomson Paris Research LabNewest of 8 Thomson Labs (est. June ‘06)

Mission– Advanced P2P communication services and platforms for

the future Internet

– Help shape Thomson’s long-term research vision

– Collaboration with academia

People– 10 permanent research staff

– 8 students: 6 PhD, 2 MS

– Several visitors (interns, researchers, professors)

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Research Domains

Enablingtechnologies

Communicationarchitectures

New communicationservices

Content delivery – VoD, live streaming, games

Pocket Switched Networks –

opportunistic, dtn, socialWireless Networking – mesh, home, self-config

Network troubleshooting – traffic analysis,

anomaly detection

Peer-to-peer

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Wireless activities at PRLGoal

– Design and evaluate new network protocols and solutions for emerging applications that use heterogeneous wireless technologies

– Current focus on home/residential network applicationsCore mechanisms

– Link quality characterization (capacity, routing metrics)– Interference estimation

Self-organizing algorithms for– Spatial optimizations using sectorized antennas and channel

assignments– MAC design and max-weight / interference-aware scheduling– Routing and rate control using optimization, back-pressure or

network coding principles

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802.11 multi-hop mesh networksPros

– Widespread, low-cost, and flexible deployments using 802.11 multi-hop wireless backbones

Problem– Poor performance– Unfairness, starvation

Reason– 802.11 MAC does not

cooperate well with higher layers

Internet Internet

MAP

GW

user

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Example: Chaska Mesh Network

Chaska.net– 196 APs / 14 GWs

Few rich MAPs

Many poor MAPs

GWMAP

Th

rou

gh

pu

t

MAP ID

Th

rou

gh

pu

t

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Network layer rate control

Approach– Rate-limit the input traffic of all flows so that the 802.11 MAC

operates in a predictable manner

Advantages– Easy implementation with widely available traffic shapers

– Predictable performance and network optimization

Challenge– Estimate the capacity region of a real-world 802.11 mesh

network, i.e. all sets of flow throughputs that can be simultaneously sustained by the network at a given time

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Requirements for capacity region estimation

Accuracy– Neither under-utilize nor over-utilize the network

Simple representation– Easy incorporation to optimization procedures

Online operation– Light-weight measurements taken during network operation

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Existing 802.11 MAC modeling approaches

Characteristics – They focus on accurate throughput prediction by capturing

the detailed 802.11 MAC protocol operation

Limitations– Only “black box” functionality => cannot be incorporated to

optimization procedures

– They require measurements taken during network downtime

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Roadmap

Model

Validation

Capacity Estimation

Online optimization

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Model

Any link throughput vector y=(y1,...,yL) that lies within this convex polytope is estimated as feasible.

Capacity region is modeled as a convex polytope of K extreme points

K

kk k

1

cy

K

kk

1

1

Kkk ,...,1,0

kLklk ccck ,...,,...,1c

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Model

y1

y2

c[1]=(c11,0)

c[2]=(0,c22)

c[3]=(c31, c32)

Two-link example: L=2 link flows and K=4 extreme points

c[1], c[2] : Primary extreme points

c[3], c[4] : Secondary extreme points

c[4]=(c41, c42)

4143131111 cccy

4243232222 cccy

4

1

1k

k

4,...,1,0 kk

,

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Model

Primary extreme points– Correspond to link capacities defined as maximum UDP throughputs when the links

transmit alone in backlogged mode

– Estimated using measurements during network operation

Challenge: How should extreme points be defined and computed online in a real-world 802.11 network?

Secondary extreme points– Computed by combining primary extreme points with a pair-wise binary interference

model

– No measurements involved

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ModelTwo-link example of L=2 link flows

y1

y2

c[1]=(c11,0)

c[2]=(0,c22)

y1

y2

c[1]=(c11,0)

c[2]=(0,c22) c[3]=(c11,c22)

Time Sharing Region

Independent Region

If links interfere If links do not interfere

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ModelMultiple links

1 2 3

Create conflict graph

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3

c11 c22 c33

Estimate primary extreme points

Find maximal independent sets in the graph

{1}

{1,3}

{2}

{3}

Compute all extreme points based on independent sets and primary extreme points

(c11,0,0)

(0,c22,0)

(0,0,c33) (c11,0,c33)

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Roadmap

Model

Validation

Capacity Estimation

Online optimization

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Wireless Mesh TestbedParis Research Lab

Parking

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57

11

20

3

10

18 16

21 19

6

12 222 9

10m

Paris Research Lab

Parking

17

57

11

20

3

10

18 16

21 19

6

12 222 9

10m

Paris Research Lab

Parking

17

57

11

20

3

10

18 16

21 19

6

12 222 9

10m Ground

1st

2nd

3rd

4th

Floor

Ground

1st

2nd

3rd

4th

Floor

Thomson Boulogne– 15 nodes– 4 floors in 2 buildings +

parking lotHW

– Atheros 802.11a/b/g mini-PCI

– External 5 dBi omni-antenna

Experiments– 802.11g (2.4GHz) at

1Mb/s and 11Mb/s– RTS/CTS off, Tx power

19dBm– Iperf

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Validation on Interfering Link Pairs

Methodology Three link pair topology

classes

– Carrier sensing (CS)

– Information Asymmetry (IA)

– Near far hidden terminal (NF)

Multiple data rates {(1,1), (11,11), (11,1), (1,11)} Mb/s with and without packet losses.

o

*

false negative

false positive

o

*

infeasible

feasible

y1

y2

c[1]=(c11,0)

c[2]=(0,c22)

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All configurations: Few false positives (~2.8%) CS configurations: Few false negatives (accurate estimation) IA + NF configurations: Avg. 25% false negatives (under-estimation)

Validation on Interfering Link Pairs

Hidden terminalconfigurationsCarrier sense

configurations

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Roadmap

Model

Validation

Capacity Estimation

Online optimization

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Online capacity estimation

Solution: We use a capacity representation that relates maxUDP throughput of each link to the packet loss rate experienced by the MAC layer.

T is the maxUDP estimation and is a function of

– The nominal throughput Tnom in absence of losses

– The packet loss rate p due to channel errors

Problem: Primary extreme points correspond to link capacities when nodes transmit alone in backlogged mode. But nodes do not transmit like this during network operation!

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Filtering out collision losses

During network operation we measure the packet loss rate pm of each link using a broadcast probing system

However: Probes are subject to collisions, hence pm is due to both channel errors and collisions

We use a filtering technique to remove collisions and recover channel error rate p from the measured packet loss rate pm.

Finally p is used in the capacity representation formula

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Capacity estimation evaluation

AdHoc Probe (Packet pairs)– Fails to predict maxUDP throughput

Our capacity estimation technique– RMSE = 0.12, independent of background traffic

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Roadmap

Model

Validation

Capacity Estimation

Online optimization

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Decentralized optimization frameworkRouting module

– Uses Click’s Srcr link state protocol with ETT routing metric

Capacity estimation module– Uses Click’s broadcast probing system

Optimizer module

Rate limits xs: SspyxSl

lss

,)1(/

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UDP performance: Model over-estimation

Overestimation

Feasible points are on the right hand side and close to unity

70% of the runs had a prediction error smaller than 10%

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UDP Performance: Model under-estimation (1)

Underestimation

Scale-up the input rates

The overestimation error increases

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Not much throughput lost due to rate control– If input rates are scaled, the achieved throughput after scaling input

rates can be at most 20% higher than throughput without scaling

UDP Performance: Model under-estimation (2)

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TCP performance – Rate Control (RC) vs noRC

TCP-Prop achieves over 80% of the aggregate throughput of TCP-noRC in 80% of the scenarios and consistently improves fairness

TCP-Max achieves up to 45% more throughput than TCP-noRC

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TCP performance -- feasibility

70% of the TCP flows achieve above 90% of their optimized rates

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ConclusionsNew model to estimate the capacity region of real-world

802.11 mesh networks– Can adequately characterize 802.11 capacity region– Can be easily incorporated to convex optimization procedures

that support decentralized operation and various fairness objectives

– Can be estimated online using light-weight measurements

Optimization-based rate control framework– Click-based implementation

uses only network layer measurements and widely available traffic shapers

– Optimizes both UDP and TCP performance– Can be easily deployed today