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http://www.cs.virginia.edu/~mm5bw/papers/CloudAutoScaling.pdf http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5697966&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5697966 www.mingmao.org
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Ming Mao, Jie Li, Marty Humphrey
eScience Group
CS Department, University of Virginia
Grid 2010 – Oct 27, 2010
A fast growing computing platform IDC - Cloud spending increases 27.4% a year to $56
billion (compared 5% a year of traditional IT)
$16.5 billion (2009) -> $55.5 billion (2014) src: Worldwide and Regional Public IT Cloud Service 2010-2014 Forecast
Two most quoted benefits
Scalable computing and storage
Reduced cost
Concerns
Security, availability, cost management, integration interoperability, etc.
Q1. Cost – the most important factor in practice?
Q2. Moving into Cloud == Reduced Cost ?
54.00%
63.90%
64.60%
67.00%
68.50%
75.30%
77.70%
77.90%
0.00% 20.00% 40.00% 60.00% 80.00% 100.00%
Seems like the way of future
Sharing systems with partners simpler
Alwasys offers latest functionality
Requires less in-house IT staff, costs
Encourages standard systems
Monthly payments
Easy/fast to deply to end-users
Pay only for what you use
Source: IDC Enterprise Panel, 3Q09, n = 263, Sep 2009
Rate the benefits commonly ascribed to the cloud on-demand model
72.90%
78.30%
79.20%
81.00%
82.10%
84.50%
86.00%
87.80%
88.60%
91.60%
0.00% 20.00% 40.00% 60.00% 80.00% 100.00%
Have local presence, can come to my offices
Are a technology and business model innovator
Offer both on-premise and public cloud services
Support many of my IT needes
Allow managing on-premise & cloud together
Understand my business and industry
Provide a complete solution
Option to move cloud offerings back on premise
Offer Service Level Agreements
Offer competitive pricing
Source: IDC Enterprise Panel, 3Q09, n = 263, Sep 2009
How important is it that Cloud service providers...
Resource utilization information based triggers (e.g.
AWS auto-scaling, RightScale, enStratus, Scalr, etc)
Multiple instance types
Current billing models Full hour billing
Non-ignorable instance acquisition time 7-15 min in Windows Azure
More specific performance goals
Budget awareness (e.g. dollars/month, dollars/job)
Deadline
(Job finish time)
Cost
Problem Statement – how to enable cloud
applications to finish all the submitted jobs
before user specified deadline with as little
money as possible using auto-scaling.
CloudApplication
Users
Job
Cloud Server
Workload are non-dependent jobs submitted in the job queue
FCFS manner and fairly distributed
Different classes of jobs
Same performance goal (e.g.1 hour deadline)
VM instances take time to startup
ijinijiViI idiV,i jt
Key variables used in the model
Workload
Computing Power of Instance
Running Instance
Pending Instance
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t n
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, ( )
( ( ))( , )i
i
type I i j
i j
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Scale up
Sufficient budget
Insufficient budget
Scale down
'iiP W P ( ')( )
itype IiMin c
( ')iMax P ( ') ( )i itype I type Ii ic C c
i siP P W
Workload Required Computing Power
1
2
3
21
: 60 10 10 40
: 60 5 20 35
: 60 20 5 35
'
j x
j y
j z
P W I I
1
2 1 2 3
3
1 2 3
: 10 10 10 45
: ' 5 ' 20 ' 10 35
: 20 5 10 35
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j x
j n n n y
j z
V V V P
1 1 2 2 3 3( ' ' ')Min c n c n c n
1 21 1 2 2 3 3 ( ) ( )' ' ' type I type Ic n c n c n c c C where
Cloud Cruise Control
Decider
&
Monitor RepositoryVM
Manager
Config
VM instancesHistorical
Data
workload
dequeue
enqueue
update update
+ , –
vm plan
vm info
( ')( )itype Ii
Min c 'jjP W P admin
users
dynamic
configuration
notify
Mix
Avg 30 jobs/hour
STD 5 jobs/hour
Computing
Intensive
Avg 30 jobs/hour
STD 5 jobs/hour
IO Intensive
Avg 30 jobs/hour
STD 5 jobs/hour
General
0.085$/hour
Delay 600s
Average 300s
STD 50s
Average 300s
STD 50s
Average 300s
STD 50s
High-CPU
0.17$/hour
Delay 720s
Average 210s
STD 25s
Average 75s
STD 15s
Average 300s
STD 50s
High-IO
0.17$/hour
Delay 720s
Average 210s
STD 25s
Average 300s
STD 50s
Average 75s
STD 15s
Workload & VM simulation parameters
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
0
1000
2000
3000
4000
5000
6000
7000
0 10 20 30 40 50 60 70 80
Utilization (%)Response (sec)
Time (hour)
Stable Worload & Changing Deadline
utilization deadline avg max min
0
50
100
150
200
250
300
350
0
500
1000
1500
2000
2500
3000
3500
4000
0 10 20 30 40 50 60 70 80
Worload (job/h)Response (sec)
Time (hour)
Changing Workload & Fixed Deadline
deadline avg max min workload
VM Types Total Cost ($)
% more than optimal
Choice #1 General 98.52$ (43%)
Choice #2 High-CPU 128.86$ (87%)
Choice #3 High-IO 129.71$ (88%)
Choice #4 General, High-CPU, High-IO 78.62$ (14%)
Optimal General, High-CPU, High-IO 68.85$
MODIS 200X – Year Terra & Aqua – Satellite
(X - Y) – Day X to day Y 15 images / day
Moderate scale test (up to 20 instances)
Large Scale test (up to 90 instances)
* C.H. – computing hour 1C.H. = 0.12$ in Windows Azure
1hour deadline 2hour deadline 3hour deadline
Terra 2004(10-12)
Total 45 jobs
4 C.H.* or 0.48$
18 min late 8 min early 20 min early
9 C.H.or 1.08$ 6 C.H or 0.72$ 5 C.H.or 0.6$
Aqua 2008(30-32)
Total 45 jobs
4 C.H. or 0.48$
15min late 20 min early 29 min early
10 C.H or 1.2$ 7 C.H.or 0.84$ 5 C.H.or 0.6$
2 hour deadline 4 hour deadline
Terra & Aqua 2006(1-75)
Total 1125 jobs
93 C.H. or 11.16$
20min late
170 C.H. or 20.4$
6 min early
132 C.H. or 15.84$
Terra & Aqua 2006(1-150)
Total 2250 jobs
185 C.H. or 22.2$
Admission Denied 22 min early
243 C.H. or 29.16$
Test: Terra & Aqua 2006(1-75) - total 1125 jobs
6min early
theoretical cost - 93 C.H. or 11.16$
actual cost - 132 C.H. or 15.84$
0 1 2 3 4 5
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
Time (hour)
Inst
an
ce N
um
ber
Instance Acquisition and Release
Released Acquiring Ready
Conclusions
More cost-efficient than fixed-size instance choice
VM startup delay can affect hugely in practice
Future works
More general cloud application model
Multiple job classes
Consider other instance types (e.g. spot instances &
reserved instances)
Data transfer performance and storage cost