1. Decision
Support
for
Amazon
EC2
Spot
Instances
Fei
Dong
2011-­‐11-­‐28
11/27/11
1

2. A
Glimpse
of
Amazon
EC2
• Reserved
Instance,
On-­‐demand
Instance,
and
SI
• Different
scenarios:
Cluster
×
Workload
EC2
Node
CPU
Memory I/O
Per-­‐hour
Type (#EC2
units) Performance Cost
m1.small 1 1.7
GB moderate $0.085
m1.large 4 7.5
GB high $0.34
m1.xlarge 8 15
GB high $0.68
c1.medium 5 1.7
GB moderate $0.17
c1.xlarge 20 7
GB high $0.68
cc1.4xlarge 33.5 23
GB very
high $1.60
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2

3. MulU-­‐objecUve
Cluster
Provisioning
1,200
Running Time
1,000
800
(min)
600 Actual
400
200
0
m1.small m1.large m1.xlarge c1.medium c1.xlarge
10.00
8.00
Cost ($)
6.00
4.00 Actual
2.00
0.00
m1.small m1.large m1.xlarge c1.medium c1.xlarge
EC2 Instance Type for Target Cluster
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4. Spot
Instance
• Spot
instances
enable
you
to
bid
for
unused
Amazon
EC2
capacity.
Instances
are
charged
the
Spot
Price
which
is
set
by
Amazon
EC2
and
fluctuates
periodically
depending
on
the
supply
of
and
demand
for
Spot
Instance
capacity.
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4

5. Challenges
&
AssumpUons
• Challenges:
– Minimize
monetary
costs
for
a
user
while
meeUng
Service
constrains.
– Know
nothing
about
Amazon
pricing
strategy
and
other
bid
strategy.
• AssumpUons:
– Bid
price
is
fixed.
– Instance
Type
is
fixed
(no
mix
strategy)
– Not
consider
the
overhead
to
recover
spot
instances.
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5

6. Pricing
PredicUon
Model
• Linear
Regression
• Normal
DistribuUon
• ExponenUal
DistribuUon
n
∑ p (1− p )i −1 (i )
H
i =1
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6

7. Predict
Price
Algorithm
1. Collect
the
prices
over
a
period
of
Ume,
in
order
to
esUmate
mean
and
variance.
2. Use
the
exponenUal
approximaUon
fidng,
calculate
x
given
the
CDF(X<x)
=
Prob.
3. Compare
other
models
and
pick
a
maximum
value
as
a
bid
price.
4. If
the
bid
price
is
smaller
than
the
spot
price,
thus
increase
the
bid
by
33%
for
the
next
interval.
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8. Price
PredicUon
eu
linux.m1.small
spot
price
on
11/18/2011
0.35
0.3
0.25
0.2
Price
($)
Actual
0.15
Predict
Adjust
Predict
0.1
0.05
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Time
(Hours)
Var(Predict
Adj)
=
0.000769
11/27/11
Var(Predict)
=
0.001786
8

9. Bid
Strategy
Utility = F(d, b, e, t, n) Deadline d, budget b,
Estimated Time e,
copt = arg max F(d, b, e, t, n)
c∈S Cluster Type t,
Number n
Min
Time
Mode:
Can
the
job
be
execute
as
soon
as
possible
under
specified
budget
and
deadline
constrains?
Min
Money
Mode:
What
is
the
bid
price
and
instance
type
that
minimize
the
total
monetary
cost?
ExhausUve
Search
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10. Experimental
EvaluaUon
• Choose
5
Spot
Instance
Types
– M1.small,
m1.large,
m1.xlarge,
c1.medium,
c1.xlarge
• Run
5
Instances
compared
with
on
demand
instances.
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10

11. Experimental
EvaluaUon
(Ctd.)
1600
1400
Running
Time
(Min)
1200
1000
800
on-­‐demand
600
SI
budget
intensive
400
SI
Ume
intensive
200
0
m1.small
m1.large
m1.xlarge
c1.medium
c1.xlarge
EC2
Instance
Type
14
12
10
Cost
($)
8
on-­‐demand
6
SI
budget
intensive
4
SI
Ume
intensive
2
0
m1.small
m1.large
m1.xlarge
c1.medium
c1.xlarge
EC2
Instance
Type
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11

12. Case
Analysis
M1.small
Linux
on
11/18/2011
M1.small
spot
instance,
bid
strategy
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12

13. Conclusions
&
Future
Work
• Conclusions
– More cost-efficient than fixed-size instance choice
– Spot Instances not always provide inexpensive resources
for transient workloads
• Future works
– Consider to mix other instance types (e.g. spot
instances & reserved instances)
– Disaster Recovery, checking point.
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13

14. Reference
• hjp://aws.amazon.com/ec2/instance-­‐types
• H.
Herodotou,
F.
Dong,
and
S.
Babu.
No
One
(Cluster)
Size
Fits
All:
AutomaUc
Cluster
Sizing
for
Data-­‐intensive
AnalyUcs.
(Slides)In
Proc.
of
the
ACM
Symposium
on
Cloud
CompuUng
2011
(SOCC
'11),
October
2011.
• D.
Ardagna,
B.
Panicucci
and
M.Passacantando.
A
Game
TheoreUc
FormulaUon
of
the
Service
Provisioning
Problem
in
Cloud
Systems.
WWW2011
Proceedings,
2011
• N.
Jain,
I.
Menache,
and
O.
Shamir.
On-­‐demand
or
Spot?
Learning-­‐based
Resource
AllocaUon
for
Delay-­‐Tolerant
Batch
CompuUng.
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14

15.
Thank
youJ
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15