Guiding Personal Choices in a Quality Contracts Driven Query Economy

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QoSbest worst

QoD

best

worst

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What if you could specify your preferences (on the trade-off between QoS and QoD)?

4

% of audience

asleep

# of slides

Motivation

Background

AQC Algorithm

Experiments

Conclusions

PersDB 2009

5

Queries

Updates

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Queries

Updates

User preferences can help system

with resource allocationPersDB 2009

Impact of scheduling A simple test

FIFO FIFO-UH (Update High) FIFO-QH (Query High)

Nonebest on both dimensions

Combining performance metricsSet constraint on one metric and optimize another [Kang04] Construct a single metric based on weighted aggregation [Abadi05]

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8

worth= $8

Response time = 30ms quality metric

worth

PersDB 2009

User preferences

9

Grid computing [AuYoung, et al., 2006] [Buyya et al., 2005] [Wolski et al., 2001] …

Distributed databases [Braumandl et al., 2003] [Benatallah et al., 2002] [Naumann et al., 1999] …

PersDB 2009

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% of audience

asleep

# of slides

Motivation

Background

AQC Algorithm

Experiments

Conclusions

PersDB 2009

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qodmaxQ

oS

pro

fit (

$)

Response Time (ms)

Qo

D p

rofit

($

)

Staleness (# UU)

+qosmax

uumaxrtmax

PersDB 2009

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RAN

$10

time

0

5

10

15

20

0 200000 400000 600000 800000 1e+006 1.2e+006 1.4e+006 1.6e+006 1.8e+006

Qmax

Paid

DYN

$10

time

$10

FIX

time Future average (DYN)Unfair distribution of the budget

Future average (DYN)Unfair distribution of the budget

Fixed average (FIX, RAN)Not fully make use of the budget

Fixed average (FIX, RAN)Not fully make use of the budget

PersDB 2009

Overbid -bid more than you can afford Deposit- bid less when continuous successes occur

14

If failureQ.size> 0 Overbid Modeelse if successQ.size>cDeposit Mode

AQC Mode Selection

PersDB 2009

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Solve for

PersDB 2009

QoS

pro

fit (

$)

Response Time (ms)

qosmax

rtmax

16

Getting expected payment from QoS function S(x)

Probability of returning before rtmax

Percentage of returning before rtmax

S(1) = 5

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smaller than 1

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qosmax = $10

qospaid = $8

qospaid = $1PersDB 2009

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% of audience

asleep

# of slides

Motivation

Background

AQC Algorithm

Experiments

Conclusions

PersDB 2009

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1-class 2-class

AQC beats other strategy up to 3X!

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RAN

$10

time

0

5

10

15

20

0 200000 400000 600000 800000 1e+006 1.2e+006 1.4e+006 1.6e+006 1.8e+006

Qmax

Paid

DYN

$10

time

$10

time

$10

FIX

time

AQC 4

6

8

10

12

14

16

18

0 200000 400000 600000 800000 1e+006 1.2e+006 1.4e+006 1.6e+006 1.8e+006

Qmax

Paid

AQC makes fully use of user budget!

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More competitive users decreases overall success ratio

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Sharing more information increases success ratio and reduce the risk

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% of audience

asleep

# of slides

Motivation

Background

AQC Algorithm

Experiments

Conclusions

PersDB 2009

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29PersDB 2009