Measurement-based models enable predictable wireless behavior Ratul Mahajan Microsoft Research...

Measurement-based models enable predictable wireless behavior

Ratul Mahajan Microsoft Research

Collaborators: Yi Li, Lili Qiu, Charles Reis, Maya Rodrig, Eric Rozner, David Wetherall, John Zahorjan, Yin Zhang,

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Wireless Mesh Networks

Can enable ubiquitous and cheap broadband accessWitnessing significant research and deploymentBut early performance reports are disappointing

ratul | kaist | june '09

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Wireless performance is unpredictable

Even basic questions are hard to answer

Arguably the most frustrating aspect of wireless• Mysteriously inconsistent performance• Makes it almost impossible to plan and manage


Wireless Wired

How much traffic can be supported?

What if a node fails?

Optimize for a given objective

4

An example of performance weirdness


Source Relay SinkGood Bad

Source Relay SinkBad Good

UD

P th

roug

hput

(Kbp

s)

Loss rate on the bad link

good-bad

bad-good

Testbed

Loss rate on the bad link

UD

P th

roug

hput

(Kbp

s)

good-bad

bad-good

2x

Simulationbad-good

good-bad

Source rate (Kbps)

UD

P th

roug

hput

(Kbp

s)

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Predictable performance optimization

Given a (multi-hop) wireless network:1. Can its performance for a given traffic pattern be

predicted?

2. Can it be systematically optimized per a desired objective such as fairness or throughput?

Yes, and Yes, at least in the context of WiFi


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Predictability needs models

To predict if specific nodes interfere and what happens when a set of nodes send together

Without models, we must measure each possibility separately


S1 S2

R1 R2

Success of failure?

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Traditional wireless models

Typical assumptions• Transmission range is circular• Interference range is twice the transmission range

Then predict the result of various sending configurations


S1 S2

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Shortcomings of traditional models

RF propagation is a very complex, esp. indoors• The assumptions almost always do not hold in practice

Great for asymptotic behavior characterization• E.g., expected max throughput as a function of

number of nodes

Pretty much useless for predicting behavior in a specific wireless network


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A move towards experimentation

Instead of relying on models, test performance of new protocols on testbeds

Hard to say if results generalize

The lack of predictability remains• Unless all possible configurations are tested


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Measurement-based models

Can offer the best of traditional modeling and experimentation worlds


Capture the “RF profile” of the network by measuring

simple configurations

Use modeling to predict the behavior under more complex configurations

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Lessons learned

Simple measurements on off-the-shelf hardware can provide usable RF profile [SIGCOMM2006]

It is possible to model interference, MAC, and traffic in a way that balances fidelity and tractability [MobiCom2007]

Holistically controlling source rates is key to achieving desired outcomes [HotNets2007, SIGCOMM2008]


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Measurement-based modeling and optimization


Measure the RF profile of the network

Constraints on sending rate and loss rate of each link

Find compliant source rates that meet the objective

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Measurements

One or two nodes broadcast at a time– O(n2) measurements

Other nodes listen and log received packets

Yields information on loss and carrier sense probabilities





S1 S2

R

14

Modeling




Makes no assumptions about topology, traffic, or MAC

Lightweight yet realistic

O(# active links) constraints capture the feasible operating region

1. Throughput constraints2. Loss rate constraints3. Sending rate constraints


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Throughput constraints• Divide time into variable-length slot (VLS)

– 3 types of slots: idle, transmission, deferral

j ijjjijiislotj

iiii TDTT

pEPg

)1(

)1(

Expected payloadtransmission time

Probability of starting transmission in a slot

Success probability

Expected slot duration


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Loss rate constraints

Inherent and collision loss are independent Inherent loss is directly measuredCollision loss

Synchronous loss• Two senders can carrier sense each other• Occur when two transmissions start at the same time

Asynchronous loss• At least one sender cannot carrier sense the other• Occur when two transmissions overlap


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Sending rate feasibility constraints

2/)(1

10

ipCWi

802.11 unicast– Random backoff interval uniformly chosen [0,CW]– CW doubles after a failed transmission until CWmax, and

restores to CWmin after a successful transmission

DIFS Data TransmissionRandomBackoff

ACKTransmission

SIFS

Expected contention window size under loss rate pi


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Extensions to the basic modelRTS/CTS

– Add RTS and CTS delay to VLS duration– Add RTS and CTS related loss to loss rate constraints

Multi-hop traffic demands– Link load routing matrix e2e demand– Routing matrix gives the fraction of each e2e demand that

traverses each link

TCP traffic– Update the routing matrix:

where reflects the size & frequency of TCP ACKsackdataTCP RRR


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Optimization




Inputs: • Traffic matrix• Routing matrix• Optimization objective

– Total throughput, fairness, …

Output: • Per-flow source rate

Predictable: output rates are actually achievable


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Flow throughput feasibility testing

• Building block for optimization

• Uses an iterative procedure

Initialize τ= 0 and p = pinherent

Check feasibility

constraintsConverged?

noyes

Estimate τ from throughput and p

Estimate p from throughput andτ

Output:feasible/infeasible

Input: throughput


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Fair rate allocationInitialization: add all demands to unsatSet

Scale up all demands in unsatSet until some demand is saturated or scale1

Output X

Move saturated demands from unsatSet to X

If unsatSet≠

if (scale 1)yes

no

yes

no


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Total throughput maximization

*0

2/)(1

10

)1(

)1(..

max

d

i

xx

pCW

TDT

pEPxRts

x

d

i

jjjijslot

jj

iii

ddid

dd

Formulate a non-linear optimization problem (NLP)Solve NLP using iterative linear programming

Sending rate is feasible

E2e throughput is bounded by demand

Link load is bounded bythroughput constraints

Maximize total txput


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The network is capable of achieving its model-predicted throughput


0 1 2 3 4 5 6 7 8 9

10

0 1 2 3 4 5 6 7 8 9 10Act

ual t

hrou

ghpu

t (M

bps)

Estimated throughput (Mbps)

0 1 2 3 4 5 6 7 8

0 1 2 3 4 5 6 7 8Act

ual t

hrou

ghpu

t (M

bps)

Estimated throughput (Mbps)

UDP TCPResults for a 19-node testbed

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The network cannot achieve higher than model-predicted throughput


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

0 0.2 0.4 0.6 0.8 1

Frac

tions

of r

uns

Ratios between actual and estimated throughput

scale=1.0scale=1.1scale=1.2scale=1.5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

0 0.2 0.4 0.6 0.8 1Fr

actio

ns o

f run

sRatios between actual and estimated throughput

scale=1.0scale=1.1scale=1.2scale=1.5

UDP TCP

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Measurement-based models enable fair throughput distribution (predictably)


0 4 8 12 160

0.10.20.30.40.50.60.70.80.9

1

Model based opt.Default

Number of flows

Jain

fairn

ess

inde

x

0 4 8 12 160

0.10.20.30.40.50.60.70.80.9

1

Model based opt.Default

Number of flowsJa

in fa

irnes

s in

dex

UDP TCP

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Measurement-based models boost network throughput (predictably)


UDP TCP

0 4 8 12 160

1

2

3

4

5

6Model based opt.Default

Number of flows

Thro

ughp

ut (M

bps)

0 4 8 12 160

1

2

3

4

5

6Model based opt.Default

Number of flowsTh

roug

hput

(Mbp

s)

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Future work: Making it real

Online measurement of RF profile

Decentralized computation of source rates

Joint optimization of routing and source rates


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Conclusions

Wireless behavior is unpredictable • Complex RF propagation• Interactions between MAC, traffic, and interference

Measurement-based models: a new approach to obtain predictable behavior• Measure the RF profile and model the rest

Promising results in our experiments on real test beds• Enables predictable optimization


Measurement-based models enable predictable wireless behavior Ratul Mahajan Microsoft Research...

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