Predictable Performance Predictable Performance Optimization for Wireless NetworksOptimization for Wireless Networks

Lili Qiu Lili Qiu University of Texas at AustinUniversity of Texas at Austin

lili@cs.utexas.edulili@cs.utexas.edu

Joint work with Joint work with

Yi Li, Yin Zhang, Ratul Mahajan, and Eric RoznerYi Li, Yin Zhang, Ratul Mahajan, and Eric Rozner

ACM SIGCOMM 2008ACM SIGCOMM 2008August 21, 2008August 21, 2008

2

MotivationMotivation• Wireless networks are becoming ubiquitous• Managing wireless networks is hard

• Our goal: develop systematic techniques to optimize the performance of wireless networks

Predict if given sending rates are achievable

Perform what-if analysis

Optimize sending rates for different objectives

Wireline Wireless

3

0 200 400 600 800

1000 1200 1400

0 1000 2000 3000 4000 5000

Thro

ughp

ut (K

bps)

Sending rate (Kbps)

bad-goodgood-bad

Unpredictability of wireless networksUnpredictability of wireless networks

Need predictable wireless performance optimization.

S

S

R

R

D

DSS

50%

100%

100%

50%

bad-good

good-bad

4

Model-driven optimization frameworkModel-driven optimization framework

Given network

Network measureme

nt

Network model

Optimization

Constraints

Traffic demands

Optimizedflow rates

Performance objectives: - Maximize fairness, total throughput, …

Routing

5

Existing models are insufficientExisting models are insufficient• Models of asymptotic performance bounds

– Cannot model any specific networks [GP00,LB+01,GT01,GV02]

• Conflict graph based model– Assume perfect scheduling and over-estimate

802.11 performance [JPPQ03]

• 802.11 DCF models– Restricted topologies or traffic demands

[Bianchi00,KA+05,GLC06,GSK05 QZWH+07,KDG07]– They aim to estimate performance and cannot be

easily incorporated into optimization procedure

Need a better 802.11 network model for optimization.

6

Our network modelOur network model• Provide a compact characterization of feasible sol

ution space to facilitate optimization• Simple yet flexible and accurate

– Handle asymmetric link loss rate– Handle asymmetric interference– Handle hidden terminals– Handle heterogeneous, multihop traffic demands

Network measureme

nt

Network model

Throughput constraints

Loss rate constraints

Sending rate constraints

7

Throughput constraintsThroughput constraints• Divide time into variable-length slot

(VLS)– 3 types of slots:

idle slot, transmission slot, deferral slot

j ijjjijiislotj

iiii TDTT

pEPg

)1(

)1(

Expected payloadtransmission time

Probability of starting tx in a slot

Success probability

Expected duration of a variable-length slot

8

Loss rate constraintsLoss rate constraints • Inherent and collision loss are independent • Inherent loss

– Based on one-sender broadcast measurement

• Collision 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

9

Sending rate feasibility Sending rate feasibility constraintsconstraints

• 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 or when max retry count is reached

– CW(pi): the expected contention window size under packet loss rate pi [Bianchi00]

• Sending rate feasibility constraints

2/)(1

10

ipCWi

DIFS Data TransmissionRandomBackoff

ACKTransmission

SIFS

10

Extensions to the basic modelExtensions to the basic model• RTS/CTS

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

• Multihop traffic demands– Link load routing matrix e2e demand– Routing matrix gives the fraction of each e2e demand th

at traverses each link• TCP traffic

– Update the routing matrix:

where reflects the size & frequency of TCP ACKsackdataTCP RRR

11

Model-driven optimization frameworkModel-driven optimization framework

Given network

Network measureme

nt

Network model

Optimization

Constraints

Traffic demands

Optimizedflow rates

Performance objectives:

- Maximize fairness, total throughput, …

Routing

12

Flow throughput feasibility testingFlow throughput feasibility testing• Test if given flow throughput are achievable• Challenge: strong interdependency• Our approach: iterative procedure

Initializeτ= 0 and p = pinherent

Check feasibility

constraints

Converged?

noyes

Estimate τ from throughput and p

Estimate p from throughput andτ

Estimate throughput from p andτ

Output:feasible/infeasible

Input: throughput

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Fair rate allocationFair 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 maximizationTotal throughput maximization• Formulate a non-linear optimization problem

(NLP)

• Solve NLP using iterative linear programming

*0

2/)(1

10

)1(

)1(..

max

d

i

xx

pCW

TDT

pEPxRts

x

d

i

jjjijslot

jj

iii

ddid

dd

Sending rate is feasible

E2e throughput is bounded by demand

Link load is bounded bythroughput constraints

Maximize total throughput

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Evaluation methodologyEvaluation methodology• Testbed experiment

– Capture real-world complexities– 19 mesh nodes at UTCS building; up to 7 hops

• Qualnet simulation – Controlled environment for a broad range of evaluation

• Rate optimization schemes– No optimization– Conflict graph (CG) model: assume perfect scheduling– Our scheme

• Traffic– TCP and UDP; saturated and random demands

• Routing – Use hop count, ETX, MIC, and CG-based routing

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Model validation: UDP trafficModel validation: UDP traffic

0 1 2 3 4 5 6 7 8 9

10

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

l throu

ghpu

t (Mbp

s)

Estimated throughput (Mbps)

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

unsRatios between actual and estimated throughput

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

1) Most estimated rates are achievable within 20%.2) Rates scaled up by just 10% become unachievable.

y=x

y=0.8x

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Model validation: TCP trafficModel validation: TCP traffic

0 1 2 3 4 5 6 7 8

0 1 2 3 4 5 6 7 8Actua

l throu

ghpu

t (Mbp

s)

Estimated throughput (Mbps)

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 of

runs

Ratios between actual and estimated throughput

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

Our model is accurate for TCP traffic.

y=x

y=0.8x

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Model validation: conflict graph Model validation: conflict graph modelmodel

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8 9 10 11Actua

l throu

ghpu

t (Mbp

s)

Estimated throughput (Mbps)

0 2 4 6 8

10 12 14

0 2 4 6 8 10 12 14Actua

l throu

ghpu

t (Mbp

s)

Estimated throughput (Mbps)

CG model significantly over-estimates sending rates.

UDP TCP

y=0.8x

y=x y=x

y=0.8x

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0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14 16Fa

irness

inde

x

Num of Flows

wo/ opt.CG opt.Our opt.

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14 16

Fairn

ess in

dex

Num of Flows

wo/ opt.CG opt.Our opt.

Maximizing fairnessMaximizing fairnessUDP TCP

Fairness index is close to 1 under our scheme, while it degrades quickly in other schemes.

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0.5 1

1.5 2

2.5 3

3.5 4

4.5 5

5.5

0 2 4 6 8 10 12 14 16

Throu

ghpu

t

Num of Flows

wo/ opt.CG opt.Our opt.

Maximizing total throughputMaximizing total throughputUDP

Our scheme significantly increases total throughput.

1 1.5

2 2.5

3 3.5

4 4.5

5

0 2 4 6 8 10 12 14 16Th

rough

put

Num of Flows

wo/ opt.CG opt.Our opt.

TCP

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0.5 1

1.5 2

2.5 3

3.5 4

4.5 5

5.5

0 2 4 6 8 10 12 14 16

Throu

ghpu

t (Mbp

s)

# Flows

wo/ opt.w/ our opt.

0.5 1

1.5 2

2.5 3

3.5 4

4.5 5

0 2 4 6 8 10 12 14 16Th

rough

put (M

bps)

# Flows

wo/ opt.w/ our opt.

Impact on different routing Impact on different routing schemesschemes

Our scheme helps all routing schemes considered.

TCPUDP

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ConclusionsConclusions• Main contributions

– Predictable wireless performance optimization• A simple yet accurate wireless network model• Effective model-driven optimization algorithms

– Demonstrate their effectiveness through testbed experiments and simulation

• Future work– Handle dynamic traffic and topologies– Use passive measurement to seed our

model

Thank you!Thank you!

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TCP Pathologies under no rate TCP Pathologies under no rate controlcontrol

S1

S2

R

D1

D2

No Rate Limit (Mbps)

Rate Limit

0.805, 0.740 1.066, 1.064

TCP cannot set the rate that maximizes throughput.

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Sensitivity of wireless network throughput Sensitivity of wireless network throughput to bottleneck location (I)to bottleneck location (I)

S

S

R

R

D

DSS

0

500

1000

1500

2000

2500

3000

3500

0 10 20 30 40 50 60 70 80 90

Thro

ughp

ut (

Kbp

s )

Link Loss Rates ( % )

bad - goodgood - bad

Performance degrade severely without rate limiting.

bad

bad

good

good

0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80 90

Thro

ughp

ut (K

bps)

Link Loss Rates (%)

bad-goodgood-bad

sim testbed

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How to determine safe sending How to determine safe sending rates under wireless rates under wireless

interference?interference?

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• Divide time into variable length slots– Idle slot, transmission slot, deferral slot

• Throughput constraint:

• VLS duration constraint

– EP(i): expected payload transmission time at link i– : probability of starting a transmission in a slot– : loss rate of link i– µ(i): expected VLS duration

Throughput & VLS duration Throughput & VLS duration constraintsconstraints

j ijjjijjjslotj

iiii TDTT

pEPg

)1(

)1(

jij

jijijslot

jji TDTT

)1(

ipi

29

Flow throughput feasibility testingFlow throughput feasibility testing

Capture interdependence– τdepends on link throughput and loss rate– Loss rate depends on link active probability– A link active probability depends on active probabil

ities of other links

Initialize τ=0 and p = 0

Check feasibility

constraintsConverged?

No

Yes

Goal: if a given set of link throughput is achievable

Estimate p from throughput andτ

Estimate τ from throughput and p

Compute throughput

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Related WorkRelated Work• Interference modeling

– Asymptotic performance bounds – Conflict graph based model– 802.11 DCF models

• Simple but restrictive– All nodes are within communication range of each other– Restricted traffic demands

• General but expensive• Both aim to predict performance and cannot facilitate

optimization

• Rate control and scheduling– Joint optimization of rate control and scheduling– IFRC: fair rate control for sensor networks and

specific to tree topology and workload• Routing

– Least cost path model [HopCount,ETX,WCETT,MIC]

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Motivation (Cont.)Motivation (Cont.)• Vision: Bring wireless network

management in par with wireline network management

• This work provides answers to basic management questions:– What traffic demands can be supported in a

network?– What is the impact of routing news and

addition of new flows?– What is safe sending rates for a given set of

flows?

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Throughput constraintsThroughput constraints

• EP(i): expected payload transmission time at link i• : probability of starting a transmission in a slot• : loss rate of link i• Variable length slots:

– Idle slot– Transmission slot– Deferral slot

iip

j ijjjijjjslotj

iiii TDTT

pEPg

)1(

)1(

33

Lessons learnedLessons learned• Rate limiting is necessary

• Proper rate limiting has to take into account of interference

• Q: How to systematically estimate the safe sending rates that a network can support?

34

Throughput constraintsThroughput constraints

j ijjjijjjslotj

iiii TDTT

pEPg

)1(

)1(

Expected payloadtransmission time

Probability of starting tx in a slot

Success probability

Expected variable slot duration-Idle slot duration-Transmission slot duration-Deferral slot duration