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Guiding Personal Choices in a Quality Contracts Driven Query Economy. Huming Qu 1 , Jie Xu 2 , Alexandros Labrinidis 2 1 IBM Watson Research Center 2 University of Pittsburgh. Audience Questions. - PowerPoint PPT Presentation
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PersDB 2009 2
QoSbest worst
QoD
best
worst
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What if you could specify your preferences (on the trade-off between QoS and QoD)?
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% of audience
asleep
# of slides
Motivation
Background
AQC Algorithm
Experiments
Conclusions
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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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worth= $8
Response time = 30ms quality metric
worth
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User preferences
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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
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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
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Overbid -bid more than you can afford Deposit- bid less when continuous successes occur
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If failureQ.size> 0 Overbid Modeelse if successQ.size>cDeposit Mode
AQC Mode Selection
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Solve for
PersDB 2009
QoS
pro
fit (
$)
Response Time (ms)
qosmax
rtmax
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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
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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
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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
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8
10
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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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