cloud economics in training and simulation
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CLOUD ECONOMICS IN TRAINING AND SIMULATION
Nane Kratzke
1 Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems
The next 20 to 25 minutes are about ...
• What is cloud computing?
• (Economical) characteristics of cloud computing
• Postulated use cases for cloud computing
• Some data from real world
• Decision making is not always obvious => How to decide?
• Some findings Prof. Dr. rer. nat. Nane Kratzke
Computer Science and Business Information Systems 2
What is a cloud computing (definition)
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 3
„Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.“
National Institute of Standards and Technology, NIST: „The NIST definition of cloud computing“; Peter Mell, Timothy Grance, 2011
http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf
Essential Characteristics of Clouds
Prof. Dr. rer. nat. Nane Kratzke Praktische Informatik und betriebliche Informationssysteme 4
• No human interaction necessary
• Programmable data center
On-demand self-service
• Remote access via thin or fat client platforms
• No physical access
Network access
• Resources are pooled to serve multiple consumers
• Little control or knowledge over exact location
Resource pooling
• Rapid provisioning • Autoscaling • Ressources are
virtually unlimited
Rapid elasticity
• Pay-per-use business model
• Resource usage can be monitored, controlled, and reported
Measured Service
Programmable Data Center
Software defined Infrastructure
Business Characteristics
Pay as you go Fixed costs become variable
Cost are associative • 100 servers for one hour • 1 server for 100 hours • (Almost) same price
Business gains flexibility • no long-term financial
commitment to resources
Prof. Dr. rer. nat. Nane Kratzke Praktische Informatik und betriebliche Informationssysteme 5
Economical Cloud Usage Patterns have to do with peak loads
Prof. Dr. rer. nat. Nane Kratzke Praktische Informatik und betriebliche Informationssysteme 6
„In other words, even if cloud services cost, say, twice as much, a pure cloud solution makes sense for those demand curves where the peak-to-average ratio is two-to-one or higher.“
Weinman, Mathematical Proof of the Inevitability of Cloud Computing, 2011
http://www.joeweinman.com/Resources/Joe_Weinman_Inevitability_Of_Cloud.pdf
Peak loads are cloud economics best friends
Postulated use cases
• Research shows that cost advantages of cloud computing are deeply use case specific
• Be aware of comparing non comparable use cases
• This contribution presents some data of educational use cases (similar usage characteristics of simulation use cases)
hosting websites support software development cycles
short-term system demonstrations
data storage, disaster recovery
and business continuity
Training and education
media processing and rendering
overflow processing or large-scale scientific data
processing simulation
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 7
These use cases (among others) are postulated to be cloud compatible:
Analyzed use case
• Web technology lecture/practical course for computer science students (bachelor) in summer 2011 and summer/winter 2012.
• Projects: Development of web information systems (Drupal based)
• All groups were assigned cloud service accounts provided by Amazon Web Services (AWS).
• Analysis of billing as well as usage data provided by AWS.
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 8
Cost analysis
9 Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems
Total costs: 846.99 $
Total students: 49
Cost per student: 17.28 $ CW 13 CW 14 – CW 17 CW 18 – CW 21 CW 22 – CW 25
(A)Costs per Month (aligned to Weeks)
Calendar Weeks (CW)
Cos
ts in
US
D
0100
200
300
400
500
instancehour (62%)
datastorage (34%)
adressing (3%)datatransfer (0%)
(B)Main Cost Drivers
B 1 (5%)
B 2 (7%)
B 3 (7%)B 4 (4%)B 5 (6%)
A 1 (10%)
A 2 (31%)
A 3 (10%)
A 4 (19%)
(C)Costresponsibilty of Groups
(D) Histogram of Costs per Group
Cost Ranges in USD
# G
roup
s
0 50 100 150 200 250 300
01
23
4
Main identified cost drivers:
(1) Server uptime (2/3)
(2) Data storage (1/3)
Usage Analysis
10 Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems
13 14 15 16 17 18 19 20 21 22 23 24 25
Average Box UsageMaximum Box Usage in an hour
(A)Maximum and Average Box Usage
Calendar Week
Use
d S
erve
r Box
es
010
2030
4050
13 14 15 16 17 18 19 20 21 22 23 24 25
(B)Accumulated Processing Hours per Week
Calendar Week
Pro
cess
ing
Hou
rs
0500
1000
1500
2000
14 16 18 20 22 24
0.0
0.2
0.4
0.6
0.8
1.0
(C)Average Box to Maximum Box Ratio
according to Weinman
Calendar Week
Avg
to M
ax B
ox U
sage
Rat
io
Training
Project 24x7 Migration
Average to Peak Ratio per Week
11 Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems
13 14 15 16 17 18 19 20 21 22 23 24 25
Average Box UsageMaximum Box Usage in an hour
(A)Maximum and Average Box Usage
Calendar Week
Use
d S
erve
r Box
es
010
2030
4050
13 14 15 16 17 18 19 20 21 22 23 24 25
(B)Accumulated Processing Hours per Week
Calendar WeekP
roce
ssin
g H
ours
0500
1000
1500
2000
14 16 18 20 22 24
0.0
0.2
0.4
0.6
0.8
1.0
(C)Average Box to Maximum Box Ratio
according to Weinman
Calendar Week
Avg
to M
ax B
ox U
sage
Rat
io
Cloud computing is economical reasonable
Cloud computing might be reasonable
Cloud computing is economical not reasonable
Economical Decision Analysis A four step process to decide for or against cloud based solutions
Determine your atp ratio
Determine your dedicated costs
Determine your maximal cloud costs
Determine appropriate cloud ressources
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 12
Max instances: 49
Processing hours: 7612
Average: 7612 / (26 * 7 * 24) = 1.74
Overall atp ratio: 1.74 / 49 = 0.035
13 14 15 16 17 18 19 20 21 22 23 24 25
Average Box UsageMaximum Box Usage in an hour
(A)Maximum and Average Box Usage
Calendar Week
Use
d S
erve
r Box
es
010
2030
4050
13 14 15 16 17 18 19 20 21 22 23 24 25
(B)Accumulated Processing Hours per Week
Calendar Week
Pro
cess
ing
Hou
rs
0500
1000
1500
2000
14 16 18 20 22 24
0.0
0.2
0.4
0.6
0.8
1.0
(C)Average Box to Maximum Box Ratio
according to Weinman
Calendar Week
Avg
to M
ax B
ox U
sage
Rat
io
Economical Decision Analysis A four step process to decide for or against cloud based solutions
Determine your atp ratio
Determine your dedicated costs
Determine your maximal cloud costs
Determine appropriate cloud ressources
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 13
Example Server: 500 US Dollar Amortization: 3 years
€
d3years(500$) =500$
3• 365 • 24h= 0.019$ h
„In other words, even if cloud services cost, say, twice as much, a pure cloud solution makes sense for those demand curves where the peak-to-average ratio is two-to-one or higher.“ Weinman, Mathematical Proof of the Inevitability of Cloud Computing, 2011
Economical Decision Analysis A four step process to decide for or against cloud based solutions
Determine your atp ratio
Determine your dedicated costs
Determine your maximal cloud costs
Determine appropriate cloud ressources
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 14
€
cMax =0.019$ h0.035
= 0.54 $h
According to Weinman the peak-to-average ratio should be greater than the ratio between the variable costs c and your (assumed) dedicated costs d:
Economical Decision Analysis A four step process to decide for or against cloud based solutions
Determine your atp ratio
Determine your dedicated costs
Determine your maximal cloud costs
Determine appropriate cloud ressources
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 15
€
cMax =0.019$ h0.035
≈ 0.54 $h
Exam
ple:
Am
azon
Web
Ser
vice
s EC
2-Pr
icin
gs fo
r EU
regi
on, 1
9th
Mar
ch, 2
012
Economical Decision Analysis A four step process to decide for or against cloud based virtual labs
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 16
A cloud based solution provides a more than 25 times cost advantage.
The measured ATP ratio of 0.035 means in fact a 1/0.035 == 28.57 times cost advantage.
This means for the presented use case:
Compared to necessary investment efforts for a classical dedicated system implementation.
Why this big cost advantage?
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 17
13 14 15 16 17 18 19 20 21 22 23 24 25
Average Box UsageMaximum Box Usage in an hour
(A)Maximum and Average Box Usage
Calendar Week
Use
d S
erve
r Box
es
010
2030
4050
13 14 15 16 17 18 19 20 21 22 23 24 25
(B)Accumulated Processing Hours per Week
Calendar Week
Pro
cess
ing
Hou
rs
0500
1000
1500
2000
14 16 18 20 22 24
0.0
0.2
0.4
0.6
0.8
1.0
(C)Average Box to Maximum Box Ratio
according to Weinman
Calendar Week
Avg
to M
ax B
ox U
sage
Rat
io
How to dimensionize the data center? peak load
What is the need?
average load
And the delta?
Measures the overdimension of a data center
In other words ...
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 18
13 14 15 16 17 18 19 20 21 22 23 24 25
Average Box UsageMaximum Box Usage in an hour
(A)Maximum and Average Box Usage
Calendar Week
Use
d S
erve
r Box
es
010
2030
4050
13 14 15 16 17 18 19 20 21 22 23 24 25
(B)Accumulated Processing Hours per Week
Calendar Week
Pro
cess
ing
Hou
rs
0500
1000
1500
2000
14 16 18 20 22 24
0.0
0.2
0.4
0.6
0.8
1.0
(C)Average Box to Maximum Box Ratio
according to Weinman
Calendar Week
Avg
to M
ax B
ox U
sage
Rat
io
You have to finance a really big house ...
... knowing that you
will inhabit only some rooms of it.
Findings
• Cloud computing loves peak load scenarios (be happy) • 25 times cost advantage (analyzed use case)
• Cloud generated costs are use case specific (be carefull) • Decision making must not be obvious • Four step decision making model (to determine your ATP ratio)
• Main cost drivers are (try to minimize) • Server uptime • Data storage (server volumes) • Data transfer (in communication intensive use cases)
• Uneconomical use cases (try to avoid) • 24x7 and • constant loads
• So if you have to deal with peak load scenerios it is likely that cloud based solutions might be an economical option ...
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 19
Thank you for listening
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 20
Prof. Dr. Nane Kratzke Computer Science and Business Information Systems Lübeck University of Applied Sciences Mönkhofer Weg 239 23562 Lübeck Germany
Mail: nane.kratzke@fh-luebeck.de
Slideshare: i21aneka
XING: Nane_Kratzke
LinkedIn: nanekratzke
WEB: http://praktische-informatik.fh-luebeck.de
Find this presentation here: http://www.slideshare.net/i21aneka/itis-ws-2013
Twitter: @nanekratzke
Qualitative IT-Management Impact of Clouds
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 21
Governance
(COBIT)
Enterprise system design
(TOGAF)
Operation
(ITIL)
12 x Positive
8 x Negative
3 x Positive
0 x Negative
6 x Positive
3 x Negative
Advantages and short comings of cloud computing
Advantages Short comings
Inherent scalability, continuousity and
availability
Cost transparency (ex post)
Provision of automated functional
services
Physical infrastructure and low level
service free (customer perspective)
More complex service, process and
configuration management
More complex security management
More complex compliancy
management
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 22
So – everthing is beautifull? No substantial show stoppers?
• Higher order showstoppers for cloud approaches • Security and Compliance Management • Incompatible SLAs • Especially national laws, privacy, data ownership,
confidentiality, data location, forensic evidence, auditing, etc.
• Decision making showstoppers for cloud approaches • Ex post but no ex ante cost transparency • Relevant costs of cloud approaches must be known before a
system enters operation • Otherwise IT investment decisions pro or contra cloud based
approaches can not been made
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 23
Hard to handle
Could be solved
Typical Cost Structure
Service Level
• IaaS + Scalability • PaaS • SaaS
Cost category
• datatransfer • dataprocessing • datastorage • network • monitoring • per request • per user/account
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 24
Infrastructure ... Platform ... Software ...
... as a Service
Assignment of cost categories to Cloud Service Levels
Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 25
Data storage
Data processing
Data transfer
Net-work
Moni-toring
Per Request/
User
Scalability X X X
IaaS X X X X X X
(per micro request)
PaaS X X X X
(per request)
SaaS X X X (per user)
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