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Nane Kratzke

CLOUD ECONOMICS IN
TRAINING AND SIMULATION




                                     Prof. Dr. rer. nat. Nane Kratzke
                                                                        1
                 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
                                                                                           2
                                    Computer Science and Business Information Systems
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
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
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
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
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
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
(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
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)
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
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
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
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
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
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
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)
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)
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
Thank you for listening


  Find this presentation here:
  http://www.slideshare.net/i21aneka/itis-ws-2013



                                                                           Slideshare:
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                                                                           XING:
                                                                           Nane_Kratzke
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                                                                           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
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
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
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
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
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

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Cloud Economics in Training and Simulation

  • 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