1.
Nane Kratzke
CLOUD ECONOMICS IN
TRAINING AND SIMULATION
Prof. Dr. rer. nat. Nane Kratzke
1
Computer Science and Business Information Systems

2.
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
2
Computer Science and Business Information Systems

3.
What is a cloud computing (definition)
„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
Prof. Dr. rer. nat. Nane Kratzke
3
Computer Science and Business Information Systems

4.
Essential Characteristics of Clouds
• No human • Remote access via • Resources are
interaction thin or fat client pooled to serve
necessary platforms multiple consumers
• Programmable data • No physical access • Little control or
center knowledge over
exact location
On-demand Network Resource
self-service access pooling
C enter
Data
• Rapid provisioning able Pay-per-use cture
amm
•
gr a s tr u
Pro are d Infr
• Autoscaling business model
e• Resource usage
• Ressources
virtuallyware defin can be monitored,
unlimited
S oft controlled, and
reported
Rapid Measured
elasticity Service
Prof. Dr. rer. nat. Nane Kratzke
4
Praktische Informatik und betriebliche Informationssysteme

5.
Business Characteristics
Fixed costs become
Pay as you go
variable
Cost are associative Business gains
• 100 servers for one hour flexibility
• 1 server for 100 hours • no long-term financial
• (Almost) same price commitment to
resources
Prof. Dr. rer. nat. Nane Kratzke
5
Praktische Informatik und betriebliche Informationssysteme

6.
Economical Cloud Usage Patterns
have to do with peak loads
„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
Prof. Dr. rer. nat. Nane Kratzke
6
Praktische Informatik und betriebliche Informationssysteme

7.
Postulated use cases
These use cases (among others) are postulated to be cloud compatible:
data storage,
support software short-term system disaster recovery
hosting websites
development cycles demonstrations and business
continuity
overflow processing
Training and media processing or large-scale
simulation
education and rendering scientific data
processing
• 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)
Prof. Dr. rer. nat. Nane Kratzke
7
Computer Science and Business Information Systems

8.
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
8
Computer Science and Business Information Systems

9.
(A)
Costs per Month (aligned to Weeks)
500
Cost analysis
400
Costs in USD
300
200
Total costs: 846.99 $
100
Total students: 49
Cost per student: 17.28 $
0
CW 13 CW 14 – CW 17 CW 18 – CW 21 CW 22 – CW 25
Calendar Weeks (CW)
(B)
Main Cost Drivers
instancehour (62%)
Main identified cost drivers:
(1) Server uptime (2/3)
datatransfer (0%)
adressing (3%)
(2) Data storage (1/3)
datastorage (34%)
Prof. Dr. rer. nat. Nane Kratzke
9
Computer Science and Business Information Systems

10.
Usage Analysis
(A)
Maximum and Average Box Usage
Training
50
Average Box Usage
Maximum Box Usage in an hour
40
Used Server Boxes
30
Project 24x7 Migration
20
10
0
13 14 15 16 17 18 19 20 21 22 23 24 25
Calendar Week
Prof. Dr. rer. nat. Nane Kratzke
10
Computer Science and Business Information Systems
(B)

11.
0
13 14 15 16 17 18 19 20 21 22 23 24 25
Average to Peak Ratio per Week
Calendar Week
(C)
Average Box to Maximum Box Ratio
according to Weinman
1.0
Cloud computing is
economical not reasonable
Avg to Max Box Usage Ratio
0.8
Cloud computing
0.6
might be reasonable
0.4
Cloud computing is
0.2
economical reasonable
0.0
14 16 18 20 22 24
Calendar Week
Prof. Dr. rer. nat. Nane Kratzke
11
Computer Science and Business Information Systems

12.
Economical Decision Analysis
A four step process to decide for or against cloud based solutions
(A)
Determine your atp Maximum and Average Box Usage
50
ratio Average Box Usage
Maximum Box Usage in an hour
40
Used Server Boxes
30
Determine your
20
dedicated costs
10
0
13 14 15 16 17 18 19 20 21 22 23 24 25
Calendar Week
Determine your
(B)
maximal cloud costs Max instances: 49 2000 Accumulated Processing Hours per Week
Processing hours: 7612
1500
Processing Hours
Determine appropriate
Average: 7612 / (26 * 7 * 24) = 1.74
1000
cloud ressources Overall atp ratio: 1.74 / 49 = 0.035
500
Prof. Dr. rer. nat. Nane Kratzke
12
0
Computer Science and Business Information Systems
13 14 15 16 17 18 19 20 21 22 23 24 25

13.
Economical Decision Analysis
A four step process to decide for or against cloud based solutions
Determine your atp „In other words, even if cloud services cost,
ratio 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.“
Determine your
dedicated costs Weinman, Mathematical Proof of the Inevitability
of Cloud Computing, 2011
Determine your Example Server: 500 US Dollar
maximal cloud costs
Amortization: 3 years
500$
Determine appropriate d3years (500$) = = 0.019 $ h
cloud ressources 3 • 365 • 24h
Prof. Dr. rer. nat. Nane Kratzke
13
Computer Science and Business Information Systems

14.
Economical Decision Analysis
A four step process to decide for or against cloud based solutions
According to Weinman the peak-to-average
Determine your atp
ratio ratio should be greater than the ratio between
the variable costs c and your (assumed)
dedicated costs d:
Determine your
dedicated costs
Determine your
maximal cloud costs
Determine appropriate 0.019 $ h $
cloud ressources c Max = = 0.54
0.035 h
Prof. Dr. rer. nat. Nane Kratzke
14
Computer Science and Business Information Systems

15.
Economical Decision Analysis
A four step process to decide for or against cloud based solutions
Determine your atp 0.019 $ h $
ratio c Max = ≈ 0.54
0.035 h
Pricings for EU region, 19th March, 2012
Determine your €
Example: Amazon Web Services EC2-
dedicated costs
Determine your
maximal cloud costs
Determine appropriate
cloud ressources
Prof. Dr. rer. nat. Nane Kratzke
15
Computer Science and Business Information Systems

16.
Economical Decision Analysis
A four step process to decide for or against cloud based virtual labs
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:
A cloud based solution provides a more
than 25 times cost advantage.
Compared to necessary investment efforts for a classical
dedicated system implementation.
Prof. Dr. rer. nat. Nane Kratzke
16
Computer Science and Business Information Systems

17.
Why this big cost advantage?
(A)
How to dimensionize the data center?
Maximum and Average Box Usage
peak load
50
Average Box Usage
And the delta?
Maximum Box Usage in an hour
40
Used Server Boxes
30
Measures the overdimension of a data center
20
average
10
load
0
13 14 15 16 17 18 19 20 21 22 23 24 25
Calendar Week
What is the need?
Prof. Dr. rer. nat. Nane Kratzke
17
Computer Science and Business Information Systems
(B)

18.
In other words ...
(A)
Maximum and Average Box Usage
You have to finance a really big house ...
50
Average Box Usage
Maximum Box Usage in an hour
40
... knowing
Used Server Boxes
that you
will inhabit
30
only some
rooms of it.
20
10
0
13 14 15 16 17 18 19 20 21 22 23 24 25
Calendar Week
Prof. Dr. rer. nat. Nane Kratzke
18
Computer Science and Business Information Systems
(B)

19.
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
19
Computer Science and Business Information Systems

20.
Thank you for listening
Find this presentation here:
http://www.slideshare.net/i21aneka/itis-ws-2013
Slideshare:
i21aneka
XING:
Nane_Kratzke
LinkedIn:
nanekratzke
Prof. Dr. Nane Kratzke
Computer Science and WEB:
Business Information Systems http://praktische-informatik.fh-luebeck.de
Lübeck University of Applied Sciences
Mönkhofer Weg 239
Mail: Twitter:
23562 Lübeck
nane.kratzke@fh-luebeck.de @nanekratzke
Germany
Prof. Dr. rer. nat. Nane Kratzke
20
Computer Science and Business Information Systems

21.
Qualitative IT-Management Impact of Clouds
Governance Enterprise system design Operation
(COBIT) (TOGAF) (ITIL)
12 x Positive 3 x Positive 6 x Positive
8 x Negative 0 x Negative 3 x Negative
Prof. Dr. rer. nat. Nane Kratzke
21
Computer Science and Business Information Systems

22.
Advantages and short comings of cloud computing
Advantages Short comings
l
cture and low leve
Physical infrastru mer perspective)
o
service free (cust
mpliancy
More complex co t
ted functional managemen
Pro vision of automa
services
t
cu rity managemen
More complex se
(ex post)
Cost transparency
d
rvice, process an
More complex se anagement
ntinuousity and configuration m
Inhe rent scalability, co
availability
Prof. Dr. rer. nat. Nane Kratzke
22
Computer Science and Business Information Systems

23.
So – everthing is beautifull?
No substantial show stoppers?
• Higher order showstoppers for cloud approaches
Hard to handle
• 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
Could be solved
• 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
23
Computer Science and Business Information Systems

24.
Typical Cost Structure
Infrastructure ... Platform ... Software ...
... as a Service
Service Level Cost category
• IaaS + Scalability • datatransfer
• PaaS • dataprocessing
• SaaS • datastorage
• network
• monitoring
• per request
• per user/account
Prof. Dr. rer. nat. Nane Kratzke
24
Computer Science and Business Information Systems

25.
Assignment of cost categories to Cloud Service Levels
Per
Data Data Data Net- Moni-
Request/
storage processing transfer work toring
User
Scalability X X X
X
IaaS X X X X X (per micro
request)
X
PaaS X X X (per
request)
X
SaaS X X
(per user)
Prof. Dr. rer. nat. Nane Kratzke
25
Computer Science and Business Information Systems