Cloud auto-scaling with deadline and budget constraints
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Ming Mao, Jie Li, Marty Humphrey
eScience Group
CS Department, University of Virginia
Grid 2010 – Oct 27, 2010
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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.
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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...
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Resource utilization information based triggers (e.g.
AWS auto-scaling, RightScale, enStratus, Scalr, etc)
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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)
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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
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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
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ijinijiViI idiV,i jt
Key variables used in the model
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Workload
Computing Power of Instance
Running Instance
Pending Instance
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Scale up
Sufficient budget
Insufficient budget
Scale down
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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
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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
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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
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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
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0.00%
10.00%
20.00%
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40.00%
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60.00%
70.00%
80.00%
90.00%
100.00%
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Utilization (%)Response (sec)
Time (hour)
Stable Worload & Changing Deadline
utilization deadline avg max min
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Worload (job/h)Response (sec)
Time (hour)
Changing Workload & Fixed Deadline
deadline avg max min workload
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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$
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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$
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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$
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Time (hour)
Inst
an
ce N
um
ber
Instance Acquisition and Release
Released Acquiring Ready
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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
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