a cloud computing cost-benefit analysis assessing green it benefits

A Cloud Computing Cost-Benefit Analysis Assessing

Green IT Benefits

Based on the Use Case of Software Development and Tests in the Cloud for the

Engineering Center of A.C.M.E Corporation

MBA Final Project Dissertation

Grenoble Graduate School of Business

Submitted by

Patrick Petit

May 2010

Tutor

Rémi Zanda PhD

GGSB MBA-PT4 Patrick Petit

A Cloud Computing Cost-Benefit Analysis Assessing Green IT Benefits

This Page is Intentionally Left Blank

GGSB MBA-PT4 i Patrick Petit


Thanks

I would like to thank the all the people who have helped me write this master thesis.

First of all, my wife, Laurence, who has spent hours reviewing the grammar and spelling of

this document, as well as my children, Jérémie and Benjamin, who have shown a great deal of

understanding and patience during these numerous days and weekends during which I was not

available to do anything other than work on my MBA assignments.

My employer, Sun Microsystems, for offering me the possibility to work on my MBA

assignments, including this thesis, and who gave me the opportunity to use the engineering center of

A.C.M.E Corp. as the business use case for the cost-benefit analysis.

And finally, my MBA tutor, Rémi Zanda from GGSB, for giving me precious feedback and

helping me prepare this thesis.

GGSB MBA-PT4 ii Patrick Petit


Executive Summary

Purpose

This final MBA project is about Cloud Computing and Green IT. Through a thorough

examination of the cloud computing phenomenon, and its relationships to green IT, the dissertation

develops a cost-benefit analysis (CBA) for an information and communication technology (ICT)

project that strives to quantify the environmental benefits of cloud computing's higher computing

efficiency. The project aim is to deploy and maintain a software development and test environment

in a public infrastructure-as-a-service (Iaas) cloud to improve the overall efficiency of research and

development (R&D) activities, as well as cut datacenter operational costs at the Engineering Center

of A.C.M.E Corp.

Both cloud computing and green IT topics discussed in this dissertation address two of the most

important epochal challenges and business opportunities of our time.

Approach and Methodology

The dissertation employs a multi-stepped methodology to CBA, which provides an introduction

to CBA for people interested in its application to environmental management. The CBA structure is

roughly outlined in six essential steps, which main objective is to determine which costs and

benefits are economically relevant to the financial analysis of the business case under study. In

combination with the multi-stepped approach to CBA, I apply the Total Economic Impact™ (TEI)

methodology of Forrester. I have chosen to use TEI methodology as a guideline throughout the

dissertation because it helps provide a complete picture of the total economic impact of an ICT

investment project by measuring not only costs and benefits, but also by weighing the enabling

value of technology.

Project Scope

The (CBA) is done for the ICT team of the Engineering Center of A.C.M.E Corp. The project

under consideration consists in evaluating the business benefits of using the Amazon Web Services

(AWS) public cloud for the software development, test and quality assurance (QA) customary tasks

of GEC. As observed by a number of field practitioners, software development and test/QA ICT

GGSB MBA-PT4 iii Patrick Petit


service delivery functions constitute an interesting use case for cloud computing, because it allows

software R&D organizations to develop and test applications without having to build and maintain a

large datacenter infrastructure.

Key Findings

From the information provided in an in-depth interview with the ITC staff, I have constructed a

TEI framework to better assess the economic impact of the migration of parts the development and

test computing resources to the AWS cloud in what is referred to as an hybrid cloud architecture

using the network isolation schemes provided through the Amazon's Virtual Private Cloud services.

The hypothesis is that the benefits of cloud computing should substantially lower the TCO of

operating the hybrid cloud infrastructure that supports the software development and test activities

of GEC, but my research shows that many of the benefits that can be realized in this project are not

easily quantifiable in terms of return on investment (ROI). However, there are three areas in which

significant operational cost savings could be achieved in this project.

1. Hardware equipment cost savings as a result of externalizing computers that have a low

annual utilization ratio to the AWS cloud

2. Electricity consumption cost savings as result of externalizing a large chunk of the

computers and storage to the AWS cloud

3. Support staff cost savings as a result of a better efficiency of the solution.

In addition, productivity gains for the engineering staff could be achieved thanks to the overall

effectiveness of the solution, as well as an improved business agility, and a better software

engineering life-cycle management that are inducing of better quality products.

At the end of the three-year financial analysis period, the total risk-adjusted benefits that could

be achieved with this project amounts to $2,422,743 in present value (PV) terms, for a total risk-

adjusted operating cost of $1,958,679 in present value (PV) terms. Other risk-adjusted financial

metrics include:

➔ The ROI that is 297%

➔ The Payback for an initial investment outlay of $203,214 that is 9 months

➔ The net savings (Net Present Value) at the of the three-year period that is $464,063

GGSB MBA-PT4 iv Patrick Petit


➔ The internal rate of return (IRR) that is 117%

However, despite the positive results of the financial analysis, it was not possible to demonstrate

in the CBA that the assumed environmental benefits of cloud computing played a sensitive role.

This is partly due to the fact that while some cloud providers are reaching extremely low PUEs, and

are also looking to build massive datacenters in places so as to maximize energy efficiency and

harness renewable or clean energy, the primary motivation is cost containment, which doesn't

necessarily meet environmental and social responsibility objectives. Second, the current body of

environmental legislations that are enacted by governments and regulatory organizations such as the

European Commission have had, so far, minor to zero financial impacts for the datacenter sector.

GGSB MBA-PT4 v Patrick Petit


Table of Contents

1 Introduction....................................................................................................................................... 1

1.1 Purpose.......................................................................................................................................1

1.2 Motivations................................................................................................................................ 1

1.3 Approach.................................................................................................................................... 4

2 Project Description............................................................................................................................ 8

2.1 Challenges................................................................................................................................10

2.2 Opportunities........................................................................................................................... 13

2.3 Action Plan...............................................................................................................................14

3 Cloud Computing............................................................................................................................ 17

3.1 Overview..................................................................................................................................17

3.1.1 Hype versus Reality......................................................................................................... 20

3.1.2 Market Forecast................................................................................................................23

3.2 A View of the Cloud from the Ground.....................................................................................24

3.2.1 Defining the Concept....................................................................................................... 25

3.2.2 Cloud Computing Service Models................................................................................... 29

3.2.2.1 Examples of SaaS Providers.....................................................................................32

3.2.2.2 Examples of PaaS Providers.....................................................................................32

3.2.2.3 Example of IaaS Providers....................................................................................... 33

3.2.3 Fundamental Characteristics of Cloud Computing.......................................................... 33

3.2.4 Cloud Computing Deployment Models........................................................................... 36

3.2.4.1 Public Cloud............................................................................................................. 37

3.2.4.2 Private Cloud............................................................................................................ 37

3.2.4.3 Community Cloud.................................................................................................... 40

3.2.4.4 Hybrid Cloud............................................................................................................ 40

3.2.5 Conclusion........................................................................................................................41

3.3 Cloud Computing Benefits...................................................................................................... 42

3.3.1 Transference of Provisioning Risks................................................................................. 42

3.3.2 Economies of Scale Benefits............................................................................................46

3.3.3 Collaboration and Community Computing Benefits........................................................49

3.3.4 Environmental Benefits....................................................................................................49

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3.3.5 Financial Benefits.............................................................................................................50

3.3.5.1 Cash-flow Friendly...................................................................................................51

3.3.5.2 Lower Financial Risk............................................................................................... 51

3.3.5.3 Greater Financial Visibility...................................................................................... 51

3.3.5.4 Healthier Return on Assets....................................................................................... 52

3.3.6 Increased Business Agility Benefits.................................................................................52

3.3.7 Operational Benefits.........................................................................................................54

3.4 Cloud Computing Costs...........................................................................................................54

3.4.1 Hardware Equipment Costs..............................................................................................56

3.4.2 Power and Cooling Costs................................................................................................. 57

3.4.3 Reliability and Availability Costs.....................................................................................57

3.4.4 Data Security and Privacy Costs...................................................................................... 58

3.4.5 Regulatory Compliance Costs..........................................................................................59

3.4.6 Supply Chain Management Costs.................................................................................... 59

3.4.7 Maintenance and Administration Costs........................................................................... 59

3.4.8 Opportunity Costs............................................................................................................ 60

3.4.9 Conclusion........................................................................................................................60

3.5 Cloud Computing Risks...........................................................................................................61

3.5.1 Questioning the Cost-effectiveness of Cloud Computing................................................61

3.5.2 Risk-testing Risks.............................................................................................................69

3.5.3 Data Location Risks......................................................................................................... 69

3.5.4 Data and Code Portability Risks...................................................................................... 70

3.5.5 Data Loss Risks................................................................................................................70

3.5.6 Data Security and Privacy Risks...................................................................................... 70

3.5.7 Compliance Risks.............................................................................................................72

3.5.8 Legal and Contractual Risks............................................................................................ 73

3.5.9 Vendor Viability Risks..................................................................................................... 73

3.5.10 Vendor Lock-in Risks.....................................................................................................74

3.5.11 Conclusion......................................................................................................................74

4 Cloud Computing Relationships with Green IT.............................................................................. 76

4.1 What Is Green IT?....................................................................................................................78

4.1.1 Quick Overview............................................................................................................... 79

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4.1.2 Green IT Motivations....................................................................................................... 82

4.1.3 Energy Policies and Implications.....................................................................................84

4.1.3.1 Energy Performance of Buildings Directive & Revisions (EPBD)..........................86

4.1.3.2 The European Code Of Conduct On Datacenter Energy Efficiency........................86

4.1.3.3 Carbon Trading.........................................................................................................88

4.1.3.4 The Grenelle of the Environment ............................................................................ 89

4.2 Energy Efficiency Measurement Metrics................................................................................ 90

4.3 Cloud Computing as an IT Efficiency Strategy....................................................................... 91

4.4 Cloud Computing as a Green IT Strategy................................................................................93

4.5 Conclusion............................................................................................................................... 98

5 Total Economic Impact Methodology........................................................................................... 100

5.1 Benefits Measure Future Positive Impacts of the Project......................................................101

5.2 Cost Measures of the Negative Impact on ICT and the Business..........................................102

5.3 Risk Quantifies the Impact of Future Uncertainties.............................................................. 103

5.4 Future Options for Flexibility Values.................................................................................... 105

6 Financial Analysis..........................................................................................................................107

6.1 Introduction............................................................................................................................107

6.2 Framework Assumptions....................................................................................................... 107

6.3 Baseline Cost of Business......................................................................................................109

6.3.1 Cost of Support Staff .....................................................................................................109

6.3.2 On-Site Datacenter Costs............................................................................................... 110

6.3.2.1 Server Hardware Cost.............................................................................................111

6.3.2.2 Network Hardware Cost......................................................................................... 113

6.3.2.3 Hardware Maintenance Cost.................................................................................. 113

6.3.2.4 Server Operating Power and Server Cooling Power Cost......................................113

6.3.3 Summary of Baseline Cost of Business......................................................................... 115

6.4 Quantification and Monetary Valuation of Relevant Impacts................................................115

6.4.1 Project Implementation Costs........................................................................................ 115

6.4.1.1 Planning and Design Cost.......................................................................................115

6.4.1.2 Project Implementation Cost ................................................................................. 116

6.4.1.3 Hybrid Cloud Infrastructure Cost........................................................................... 117

6.4.1.4 Support Staff Cost.................................................................................................. 123

GGSB MBA-PT4 viii Patrick Petit


6.4.1.5 Summary of Hybrid Cloud Project Costs...............................................................123

6.4.2 Benefits and Savings Opportunities............................................................................... 124

6.4.2.1 Revenues Benefits.................................................................................................. 124

6.4.2.2 User Productivity Benefits..................................................................................... 125

6.4.2.3 Capital Efficiency Benefits.....................................................................................127

6.4.2.4 Compliance Benefits.............................................................................................. 127

6.4.2.5 Summary of Quantifiable Benefits.........................................................................128

6.4.3 Risk Quantification........................................................................................................ 128

6.4.4 Flexibility Quantification............................................................................................... 132

6.5 Discounting of Costs and Benefits Flows..............................................................................132

6.6 Financial Analysis Conclusion...............................................................................................133

6.7 Key Financial Metrics............................................................................................................133

7 Conclusion..................................................................................................................................... 136

8 Future Research Implications........................................................................................................ 138

Bibliography.................................................................................................................................... 140

Appendix I: Amazon Web Services (AWS)..................................................................................... 149

Elastic Compute Cloud (Amazon EC2)......................................................................................149

Amazon EC2 Instances Characteristics...................................................................................... 149

Amazon EC2 Pricing..............................................................................................................151

On-Demand Instances Pricing...........................................................................................152

Reserved Instances Pricing................................................................................................152

Spot Instances Pricing....................................................................................................... 153

Internet Data Transfer Pricing........................................................................................... 154

Amazon Elastic Block Storage (EBS) Pricing....................................................................... 154

AWS Import/Export Service...................................................................................................155

Appendix II: Gartner's Hype Cycle Explained................................................................................ 156

The Hype Cycle Graphic............................................................................................................ 156

The Priority Matrix Graphic....................................................................................................... 159

Appendix III: Gartner's Hype Cycle for Emerging Technologies in 2009......................................160

Appendix IV: Gartner's Hype Cycle for Cloud Computing in 2009................................................161

Appendix V: Gartner's Priority Matrix for Cloud Computing, 2009............................................... 162

Glossary of Terms............................................................................................................................164

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GGSB MBA-PT4 x Patrick Petit


List of Tables

Table 1: Key Characteristics of Public and Private Clouds................................................................38

Table 2: Key financial benefits of on-premises ICT versus cloud computing (Source: Forrester

Research, Inc.).................................................................................................................................... 51

Table 3: AWS EC2 Quick Costs Comparison....................................................................................65

Table 4: Key Energy Policies Applicable To The Case Study (Source: The Green Grid)..................86

Table 5: Projections of growth in ICT electricity and GHG emissions by 2020 (Source: Greenpeace

International)...................................................................................................................................... 99

Table 6: Five Categories Of Technology Benefits (Source: Forrester Research, Inc.).....................103

Table 7: Typical Cost Categories By Phase (Source: Forrester Research, Inc.)...............................104

Table 8: Typical Risk Categories and Impact (Source: Forrester Research, Inc.)............................105

Table 9: Common Flexibility Options (Source: Forrester Research, Inc.)....................................... 106

Table 10: Flexibility Options Pricing Models (Source: Forrester Research, Inc.)...........................106

Table 11: General Financial Assumptions and Metrics.................................................................... 108

Table 12: Compute Nodes Usage Patterns........................................................................................110

Table 13: Support Staff Salaries Estimates....................................................................................... 111

Table 14: Annual Cost of Support Staff Salaries.............................................................................. 111

Table 15: Description of the Server Specifications Mapping and Quantity.....................................112

Table 16: Annual Datacenter Cost Summary....................................................................................115

Table 17: Total Baseline Cost of Business........................................................................................116

Table 18: Planning and Design Phase Cost, Non-Risk-Adjusted..................................................... 117

Table 19: Project Implementation Cost, Non-Risk-Adjusted........................................................... 117

Table 20: Hybrid Cloud Computer Utilization Ratio Estimates....................................................... 119

Table 21: Datacenter Depreciation Cost Basis Adjustment..............................................................120

Table 22: Total Number of Elastic Bloc Storage (EBS) Volumes.................................................... 121

Table 23: Hybrid Cloud Infrastructure Costs, Non-Risk-Adjusted.................................................. 123

Table 24: Annual Support Staff Salaries' Cost, Non-risk-Adjusted..................................................124

Table 25: Total Project Costs, Non-Risk-Adjusted...........................................................................125

Table 26: Total Operational Benefits, Non-Risk-Adjusted...............................................................126

Table 27: Annual Engineering Salaries Benefits, Non-risk-Adjusted.............................................. 128

Table 28: Total Quantifiable Benefits, Non-Risk-Adjusted..............................................................129

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Table 29: Costs and Benefits Risk Scoring...................................................................................... 130

Table 30: Total Risk-Adjusted Costs................................................................................................ 133

Table 31: Total Risk-Adjusted Benefits............................................................................................133

Table 32: Key Financial Metrics, Original and Risk-Adjusted........................................................ 134

Table 33: Internal Rate of Return, Payback and ROI calculation (Non-Risk-Adjusted)..................135

Table 34: Internal Rate of Return, Payback and ROI calculation (Risk-Adjusted)..........................135

Table 35: Cash Flow Over Three-Year Period (Non-Risk-Adjusted)...............................................136

Table 36: Cash Flow Over Three-Year Period (Risk-Adjusted).......................................................136

Table 37: Graphic Representation of Cash Flow Over Three-Year Period (Risk-Adjusted)............136

Table 38: Amazon EC2 Instance Types (Source Amazon)............................................................... 152

Table 39: Number of days to transfer 1TB per connection speed (Source: Green datacenter Blog)

.......................................................................................................................................................... 156

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List of Illustrations

Illustration 1: Cloud-enabled applications quadrant (source: Syntec)..................................................9

Illustration 2: Amazon Virtual Private Cloud Architecture (Graphic Courtesy of Amazon)..............14

Illustration 3: Cloud Computing Evolution and Revolution (Graphic Courtesy of Gartner).............18

Illustration 4: Cloud Computing Market Forecast in Million € for the EU's 27 Member States

(Graphic Courtesy of Syntec - ©PAC ).............................................................................................. 24

Illustration 5: IaaS, Paas, SaaS: Who is maintaining what?............................................................... 30

Illustration 6: Traditional datacenter: Capacity vs. Usage (Graphic Courtesy of Amazon)...............43

Illustration 7: Amazon Web Services: Capacity vs. Usage (Graphic Courtesy of Amazon)..............44

Illustration 8: Economies of scale and skill drive cloud computing (Source: Forrester Research,

Inc.).....................................................................................................................................................46

Illustration 9: Rackspace customer views on cloud computing as a greener alternative to traditional

computing infrastructures? (Graphic courtesy of Rackspace)............................................................78

Illustration 10: Rackspace customer views on how cloud computing fit into their environmental

initiatives (Graphic courtesy of Rackspace).......................................................................................78

Illustration 11: More enterprises are implementing green IT action plans but actual implementation

is still lagging (Graphic courtesy of Forrester Research, Inc.)...........................................................82

Illustration 12: Barriers to Green IT Adoption (Graphic courtesy of Forrester Research, Inc)..........82

Illustration 13: Organizations’ top motivations for pursuing greener IT operations (Graphic courtesy

of Forrester Research, Inc)................................................................................................................. 84

Illustration 14: Datacenter Worldwide Spending (Graphic Courtesy of IDC)...................................92

Illustration 15: Comparison of significant cloud providers' datacenter fueling energy mix (Graphic

courtesy of Greenpeace International)................................................................................................98

Illustration 16: The Four Elements of TEI: Benefits, Cost, Risk and Flexibility for Financial

Analysis (Graphic courtesy of Forrester Research, Inc.)..................................................................102

Illustration 17: Amazon EC2 On-DEmand Instances Pricing.......................................................... 153

Illustration 18: Amazon EC2 Reserved Instances Pricing................................................................154

Illustration 19: Amazon EC2 Spot Instances Pricing....................................................................... 154

Illustration 20: Amazon Internet Data Transfer Pricing................................................................... 155

Illustration 21: Amazon Elastic Block Storage (EBS) Pricing......................................................... 155

Illustration 22: Gartner Hype Curve (Source: Gartner)....................................................................157

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Illustration 23: Gartner's Hype Curve Breakdown (Source: Gartner).............................................. 159

Illustration 24: Hype Cycle for Emerging Technologies, 2009 (Source Gartner)............................161

Illustration 25: Hype Cycle for Cloud Computing, 2009 (Source: Gartner).................................... 162

Illustration 26: Gartner's Priority Matrix for Cloud Computing, 2009 Source: Gartner).................163

GGSB MBA-PT4 xiv Patrick Petit


1 Introduction

1.1 Purpose

This final MBA project is about Cloud Computing and Green IT.

Through a thorough examination of the cloud computing and green IT phenomena, including

their associated costs, benefits and risks, the dissertation develops a cost-benefit analysis (CBA) for

a cloud computing project. The dissertation strives to highlight the relationships between cloud

computing and green IT, and discusses whether cloud computing in the context of this project bears

a positive economic outcome, taking into account—among other benefits—the assumed lower

environmental impact due to cloud computing's higher computing efficiency.

To this end, the dissertation provides the cost-benefit analysis of an information and

communication technology (ICT) project, which aims to deploy and maintain a software

development and test environment in a public infrastructure-as-a-service (IaaS) cloud to improve

the efficiency of research and development (R&D) activities, as well as cut datacenter operational

costs at the engineering center of A.C.M.E Corporation.

The secondary objective of this dissertation is to propose a framework for businesses wishing to

use a public cloud in a similar application use case, but who are still unclear about the financial and

business impacts of such a strategic decision.

1.2 Motivations

Both cloud computing and green IT topics discussed in this dissertation address two of the most

important epochal challenges and business opportunities of our time.

• Cloud computing represents a paradigm shift that has the potential to disrupt the overall

industry by displacing the traditional on-premises datacenter computing style toward a

cloud-based computing style delivered as a service over the Internet, where company

business processes should gain in flexibility and cost. This prospect is dizzying, but

ultimately a very inspiring innovation and technology management business topic.

• Understanding to what extent cloud computing can help mitigate the devastating effects

GGSB MBA-PT4 1 Patrick Petit


of global warming through a reduction of the ICT industry carbon footprint, and how

businesses can benefit from it, is yet another most inspiring business management topic.

Here is why:

Operating system and middleware software vendors like Microsoft and Sun Microsystems (the

company I currently work for), perceive cloud computing as a disruptive innovation to their

business as it is accelerating the software commoditization trend that started in the nineties with the

open-source software phenomenon - such as the Linux operating system - that runs most of the

servers delivering content on the Internet today. Likewise, makers of enterprise software, such as

SAP and Oracle, may suffer similar market tensions with the advent of cloud computing. Up until

now, they have made billions by selling very expensive software solutions, demanding hefty sums

for their installation and then charging annual maintenance fees for upgrades and technical support.

But his lucrative business, says Michael Cusumano, professor at the Massachusetts Institute of

Technology (MICT), in (Siegel 2008, p.10), has come under increasing pressure. The corporate

world has become less and less willing to buy software for large sums of money, which explains

why large independent software vendors (ISVs) have steadily increased their maintenance and other

service fees over the years, and will continue to do so to maintain their revenues.

The biggest challenge for those firms will be to become providers of cloud-based online services

themselves. So far, they have moved slowly, offering software-as-a-service (SaaS) solutions partly

because their customers were not ready for a bigger move, but more importantly, because software

firms are still fixated on their old business models, which call for upfront payment as opposed to a

deferred pay-as-you-go revenue model (Siegel 2008, pp.10-11). This positioning became obvious

through several discussions I recently had on cloud computing with engineering managers1 at

Oracle. It appears that traditional ISVs, like Oracle, may not have the right resources, processes and

values2 to compete effectively on the cloud- computing market battle-field.

1 I refer in particular to a conversation I had with a VP of engineering for the Fusion Middleware line of products,

who admitted to Oracle's restrains to embracing a full-fledge cloud computing strategy because of its disruptive

nature.

2 Clayton Christensen (2004, p.32) discusses the resource, process, value (RPV) theory that in essence argues that a

firm's strength and weaknesses are defined by its resources, processes and values, and that to compete effectively on

the disruptive innovation battle-field, a firm must have the right ones. Assessing a firm's RPV requires answering

three questions:

• Does a firm have or can it marshal its resources required to attack an opportunity?



Similarly, in a special report of The Economist on Corporate IT, Siegel Ludwig (2008) argues

that, at the beginning, the biggest winners of the cloud computing market share battle are likely to

be the hardware manufacturers. In the longer term, however, there will be relative winners and

losers. “The hardware business could actually find itself in the losing group; its margins could get

squeezed as this industry matures because there will be fewer customers with more bargaining

power” (Siegel 2008, p.10), and that “in the long term, hardware manufacturers may be torn

between supplying cloud providers or becoming providers themselves. Being both will not be easy

because the firms may find themselves competing with their biggest customers” (Siegel 2008, p.10).

On the green IT front, a 2006 report by Sir Nicholas Stern, former head of the UK Government's

Economic Services and Chief Economist of the World Bank, also known as the Stern Review,

predicts that climate change will have a serious impact on economic growth without mitigation. The

report suggests that failure to invest at least 1% to 2% of the UK gross domestic product (GDP) to

mitigate the effects of climate change incurs the chance of a recession worth up to twenty percent of

the global GDP (Bowen et al. n.d., p.VII) and (Van der Perre et al. 2009, p.14). Furthermore, the

Intergovernmental Panel on Climate Change (IPCC) concluded that most of the observed global

warming since the middle of the 20th century has been caused by increasing concentrations of

greenhouse gases resulting from human activity such as fossil fuel burning and deforestation (Alley

et al. n.d., p.1). Greenhouse gas emissions are forecast to grow by 130% between now and 2050,

unless we learn to generate and use energy more efficiently. The International Energy Agency (IEA)

calculates that to reduce CO2 emissions to half of today’s levels would require investing $45 trillion

in the development and deployment of carbon emissions reduction technologies over this period, the

equivalent of 1.1% of the average annual global GDP (Kanter 2008).

• Do the firm's processes effectively and efficiently facilitate it doing what needs to be done?

• Do the firm's values allow it to prioritize one opportunity over other options on its plate?

Oracle certainly has the resources, but it arguably possess the processes and values to compete effectively and

effectively on the cloud computing competitive ground. Oracle's processes are deliberately targeted toward

delivering high-margin performance products to its most demanding customer base, entailing a high cost structure,

whereas most of today's cloud-based services buyers are small to medium businesses (SMBs) with limited

purchasing power and knowledge of high-end enterprise software solutions. With respect to values, Larry Ellison,

CEO of Oracle Corporation, said himself in an interview (2008) that cloud computing has been defined as

"everything that we already do" and that it will have no effect except to "change the wording on some of our ads",

then denying the very basic business values of this emerging phenomenon.



In “Green IT Going for Green”, VMWare and Intel (Speedfire et al. n.d.) throw a compilation of

striking figures providing evidence that the ICT industry bears responsibility in the current global

warming situation, and showing, for example, the dreadful impact of our seemingly benign attitude

when consuming electronic goods on the Internet. For instance, downloading the electronic version

of our daily newspaper uses the same amount of electricity as a laundry cycle in our clothes washer,

according to the IZT research institute. Also, according to Gartner in (Speedfire et al. n.d.) the ICT

industry is responsible for as much greenhouse gas as the world's airline industry, which contributes

from 2% to 5% of the worldwide CO2 emissions3. This worrisome number draws from the fact that,

in most developed and emerging nations, fossil fuel is used to produce electricity. Among these

nations stand the US who, operating some of the largest datacenters in the world, produces 50% of

its electricity out of burning coal. McKinsey & Co. in (GreenIT n.d.) forecasts that the ICT sector’s

CO2 emissions will triple during the period from 2002 to 2020, and that in office buildings ICT

typically accounts for more than 20% of the energy used, and in some offices up to 70%.

While there is little academic research to support the claim that cloud computing has a positive

environmental impact, there is reason to believe that it could help the ICT industry reduce its carbon

footprint. The basic reasons for this are:

1. Cloud computing provides a much higher utilization rate of ICT resources than conventional

datacenters.

2. Cloud providers' best interests are to optimize their energy consumption because this latter

directly affects their profit margins.

3. The industrialization and maturity of the cloud computing market will draw further

consolidations and improve ICT efficiency, leading to a reduction in energy requirements.

1.3 Approach

A CBA will produce reasonably accurate results only as long as it is used in the right way. As

such, this dissertation employs a multi-stepped approach to CBA proposed by (Baker et al. n.d.)

and by (Hanley & Spash 1995). (Baker et al. n.d.) proposes the case of a CBA methodology for IT

3 Available online after registration at http://go2.wordpress.com/?

id=725X1342&site=itgreening.wordpress.com&url=http%3A%2F%2Fwww.speedfire.com%2Foptin

%2Fwpgreenus.



related projects. Hanley and Spash in (1995) provides an introduction to CBA—and the closely

related technique of cost-effectiveness analysis (CEA)—for people interested in its application to

environmental management. In this book, they have tried to represent most sides of the arguments

concerning contentious issues around the correct approach to appraising projects that may impact

positively or negatively the environment. Both works propose a CBA structure that is roughly

outlined in six essential steps.

1. Determine the objective and scope of the project. This step establishes an understanding

of the business problem the ICT project is trying to solve. It is important to keep in mind

that businesses do not implement technologies for their own sake. In other words, businesses

will implement a new solution to solve a particular problem, improve a certain process,

become more efficient, and so forth. This step will document the business drivers that make

the case for the ICT project under consideration, and list the deliverables that support the

business drivers. In this step, I will also provide the baseline cost of business, meaning what

the company spends today in the domain under which the ICT project will operate.

Recalling the business drivers mentioned above, the baseline cost of business should

highlight how much the company currently spends on maintaining the information system

that support its business processes. Determining the baseline is important to effectively

calculate the magnitude of the benefits of the new ICT project.

2. Determine which costs and benefits are economically relevant. This step involves a

review of all the costs and benefits that are economically relevant to the project. From an

environmental point of view, the economically relevant impacts—what to count—that can

be accounted for in a CBA are necessary so long as they are measurable within the scope of

this study, even though the theory says that “the environmental impacts count so long as

they either: 1) cause at least one person in the relevant population to become more or less

happy; 2) change the level or quality of output of some positivity valued commodity”

(Hanley & Spash 1995, pp.8-20). For example, the positive impact of Google's Dalles

datacenter that uses the cold waters of the Columbia river for its cooling system may save on

carbon-dioxide (CO2) emissions, but may adversely deteriorate the landscape. In theory, this

negative effect is relevant to an environmental CBA as at least one person will most likely

dislike the landscape change because the absence of a market for landscape quality is



irrelevant here. In fact, many environmental effects fail to be recorded by market price

movements. Nevertheless, unpriced impacts—referred to as externalities—are the most

important feature of environmental CBA according to (Hanley & Spash 1995, p.10). The

central message here is that, while most environmental impacts are likely to be relevant in

the context of a full-fledge environmental CBA, they will not necessarily be carried out in

that study for obvious lack of measurability reasons. On the other hand, they may induce

additional project choice incentives in the conclusion.

3. Determine the project costs (monetary valuation of the negative effects). Once I have

identified which costs are relevant to the project, I will need to calculate their ex ante values

in monetary terms. There are two important things to keep in mind according to (Baker et al.

n.d., p.2):

• Ensure that the analysis includes all the cost categories.

• Express how much it will cost to maintain the system after implementation. This

is know as the full life-cycle cost.

4. Determine the project benefits (monetary valuation of the positive effects). Similarly to

determining the project costs, I will need to provide a list of the benefits that the project will

gain from the solution. For each of these benefits, I will need to provide a quantification

method to be able to calculate their ex-ante values in monetary terms.

5. Discounting of cost and benefit flows. The project's net cash stream will contain a dollar

value for each year starting with the first year of the project and ending after 3 years. The

cash-stream schedule indicates the timing of the costs and benefits which result in a positive

or negative net cash flow. Once all relevant cost and benefit flows have been so expressed,

it is necessary to convert them into present value (PV) terms. This step stems from the need

to appraise cost and benefit flows into their present value (PV) terms to take into account the

time value of money. The project cash-stream is the cornerstone of the financial analysis.

From it, I will be able to calculate the Return on Investment (ROI), the payback and Net

Present Value (NPV).

6. Applying the Net Present Value Test. The main purpose of the CBA is to help select

projects and policies that will be the most efficient in terms of their use of resources. The



criterion applied is the Net Present Value (NPV) test. This test simply asks whether the sum

of the discounted gain exceeds the sum of the discounted losses. If so, the project can be

said to present an efficient shift in resource allocation, given the data used in the CBA.

In combination with the multi-stepped approach to CBA, I will apply Forrester's Total

Economic Impact™ (TEI) methodology described in (Gliedman 2008). I have chosen to use this

methodology as a guideline throughout the dissertation because it helps provide a complete picture

of the total economic impact of an ICT investment project by measuring not only costs and benefits,

but also by weighing the enabling value of technology. A more complete description of the

methodology applied is discussed in Chapter 5 “Total Economic Impact Methodology”.



2 Project Description

The cost-benefit analysis (CBA) is done for the ICT team of the Engineering Center of A.C.M.E

Corp. The project under consideration consists in evaluating the business benefits of using the

Amazon Web Services (AWS) public cloud4 for the software development, test and quality

assurance (QA) customary tasks of GEC. As observed by a number of field practitioners, software

development and test/QA ICT service delivery functions constitute an interesting use case for cloud

computing, because it allows software R&D organizations to develop and test applications without

having to build and maintain a large datacenter infrastructure.

According to (Syntec 2010, p.7), software development, test and quality assurance (QA) is one

of those application types that can best leverage the benefits of the cloud and is easiest to deliver, as

shown in the following illustration.

4 Popularized in 2006, Amazon Web Services (AWS) started offering virtual machines for $0.10 an hour with the EC2

program, which first popularized the infrastructure-as-aservice (IaaS) utility computing model, which became

closely tied to the concept of cloud computing.



CIO.com online magazine in (Golden 2009b) and in (Golden 2009a) argues that development

and test in the cloud makes sense for a number of reasons that are outlined below.

• Companies devote the highest percentage of their ICT budget to keeping vital business

applications up and running. As a result, development and test procurement of

computing resources is often underfunded, leading to poor R&D efficiency and

effectiveness.

• Development and test (including QA use of resources) is spiky in nature, leading IBM

Research in (Rosenberg 2010) to observe that the average enterprise ICT department

may devote up to 50 percent of its datacenter infrastructure to development and test, and

that up to 90 percent of the available test infrastructure may remain idle at certain points

in time. By its very nature, development and test is spiky because a developer will write

code, test it out, and will move to other tasks such as design reviews, whiteboard

discussions, and so on. Similarly, QA teams make a non-linear use of computing


Illustration 1: Cloud-enabled applications quadrant (source: Syntec)

Ease of delivery ascloud computing style

Gain achieved fromCloud Computing

SMBsERP/SCM/CRM

WebServer

MessagingCollaborationConferencing

Development&Test

NumberCrunching

Small amount of data

DataWarehouse

VirtualDesktop

File Management& Printing

NumberCrunching

Large amount of data

Large EnterprisesERP/SCM/CRM

Large EnterprisesTransactional App. Web-based architecture

Virtualization oriented architecture

Data-base oriented architecture

Analytics

SystemsManagement


resources for reliability, performance and scalability testing that surges when an alpha

release is made available. At times like these, it seems that there are never enough

servers to go around.

• Development and test teams are often hindered in their attempts to access a production-

like quality environment because it is too costly to replicate and maintain a production-

like infrastructure setup and topology. This issue is particularly acute when testing out

how well an application responds to load and stress tests. This means that assessing how

well an application behaves in production (i.e., performance, reliability, request latency

and throughput) is difficult or impossible to evaluate in constrained environments. Also,

many of the most important bugs only surface under heavy load conditions, meaning that

these bugs aren't found during development and testing, and that they will surface when

deployed in production where it is most costly to fix them.

• The datacenter's operations staff doesn't want development and testing to affect

production systems. Putting development and testing into the production infrastructure,

even if quarantined via VLANs, holds the potential of affecting production applications

throughput, an anathema to operations groups. Consequently, development and test

groups are often hindered in their attempts to access a production-like environment.

2.1 Challenges

The following is a summary of an interview with the ICT team who manages the datacenter of

A.C.M.E corp. that counts for 135 employes among which 105 engineers are working in software

R&D. The ICT staff is in charge of maintaining the operations of about 1,000 machines, scattered in

9 different labs of around 50m2 each, and a central computer room of 160m2. The datacenter is a

horrendous collection of different computers with various hardware configurations ranging from

recycled personal workstations, inefficient old servers (e.g. Ultra1 and Ultra2 boxes) stocked in

racks, to more recent medium-to-high-end multi-core servers interconnected with large storage

systems. The central computer room is primarily used to host the file and backup servers of the labs.

It is interconnected with the 9 labs through fast 10 Gb ethernet links. The heterogeneity and aging

of the machines populating the datacenter make it difficult to cost-effectively manage the datacenter

because it is time consuming to maintain such a diverse ICT environment and requires a significant



inventory of spare parts to replace faulty components.

Among the 1,000 or so machines, it is estimated that a small proportion cannot be easily

externalized to the cloud because they are either used extensively—with, for example, release

engineering that runs non-regression tests on a daily basis using large amounts of data that would be

too costly and impractical to migrate to the cloud—or require control over the hardware

specifications of the machines—like a SPARC versus an x86 processor architecture—to conduct

performance benchmarks, product qualification, and other similar platform certification tasks.

Conversely, it is estimated that the vast majority of the x86-based servers could be externalized to

the cloud for general development and testing purposes on the condition that the solution can ensure

both excellent access performance and security. Amazon's Virtual Private Cloud, as we will see

below, and CohesiveFT were cited as companies who can tackle the issue of bridging the datacenter

with public cloud services in a secure manner to ensure that data at rest and data in-flight are not

compromised. CloudSwitch5 was also cited as a well regarded startup that pushes the concept of

hybrid cloud computing further by offering a service that takes care of all the networking, isolation,

management, security and storage concerns related to moving VMWare-based virtual machines to

Amazon EC2.

The primary motivation for GEC to outsource parts of its development and test infrastructure to

the cloud arises from the actual inefficiency in the use of the lab resources. Secondly, the

organization is seeking to reduce its hardware procurement and maintenance costs as well as its

electricity bills for cooling and running the systems, hence inducing a greener IT positioning by

reducing CO2 emissions from the sprawling of servers and storage farms. Thirdly, they are seeking

to become more agile and productive by providing developers and testers with a unified and more

effective computing environment.

Currently, GEC is facing several challenges that hamper its ability to reach these objectives.

• There are many contention issues among lab users between developers and testers to get

access to available machines of the right configuration type. QA teams are generally

scrambling to get access to machines when an alpha release is delivered. They rely

heavily on the engineering teams to free up boxes from development.

• Developers tend to be very inefficient in their use of lab resources because they step in

5 http://www.cloudswitch.com/



and out randomly with various engineering tasks. Meanwhile, they retain control of the

machines they own for weeks and even months because they are reluctant to release

them as doing so would mean that they would have to reinstall and reconfigure the

different pieces of the working environment every time they get a machine back from the

pool. It is common to see developers keep a dozen or so machines up their sleeve while

they are in actual fact only using one or two at a time. Similarly, QA has to reinstall the

entire software stack every time they get an available machine. This is because part of a

real testing cycle is to ensure that the application mirrors a production environment,

which means the machine needs to have a clean install with appropriate versions of all

the software components. It can take days to manually set-up a proper QA environment,

from the time the alpha version is released to QA and the time that QA actually gets an

environment set-up properly to begin the test.

• The demand for resources is overall very spiky over the course of a year. The ICT staff

does not keep an exact track of this, but their “gut feeling” is that , in general, machines

are most of the time in an idle state, which leads to a utilization rate below 10%. For

example, QA activities are linked to the product's life-cycles, which concentrate a stiff

peak of load twice a year for a couple of months at most, thus involving the allocation of

a large amount of resources, yet ephemeral, to be able to release the product on time. The

worst thing that could happen is to delay the release of a product as a result of a lack of

available resources.

To prevent such impediments , the policy, so far, has been to largely over-provision the capacity

of the datacenter. But in times of cost cutting, budgetary constraints and energy efficiency pressures,

this approach is no longer viable. So far, the ICT staff has been able to contain this precarious

situation, while reducing the number of physical servers, through an aggressive server consolidation

process started a couple of years ago, by using the virtualization technology of VMWare. But, for

historical and customary reasons, not all servers are yet virtualized. In addition, ICT staff is seeing

today a demand surge for larger server configurations—typically fast eight-core CPU machines

with 16 GB of memory and more—that the current virtualization layout cannot easily fulfill because

most of the underlying physical servers in the datacenter are small-to-medium size machines.

In addition to the points outlined above, the interview uncovered some other relevant goals that



the project should address.

• Allow administrators to centrally manage the labs' infrastructure and configuration,

including policies, which determine what resources can be allocated and consumed by

whom (i.e. groups of users or individuals).

• Allow testers to safely reserve virtual machines without conflict and provision

automated test configuration scenarios on a scheduled or on-demand basis with no

manual intervention.

• Allow testers to reliably request and securely access test configurations 24/7 through a

standard browser that enables R&D personnel—in any location worldwide—to deploy

test configurations via a self-service portal without requiring access to the physical hosts.

2.2 Opportunities

The ICT team of GEC believes that cloud computing can help evaluate, plan, design and

implement a dynamically scalable and virtualized software engineering environment. As such,

cloud computing is perceived as an opportunity to build a more flexible and cost-effective

development and test computing environment for the engineers working at GEC. It should allow the

organization to accelerate the product life-cycle and reduce costs by centralizing and automating the

deployment, configuration and tearing-down of this complex environment. Through this strategic

decision, GEC expects to obtain the following benefits.

• Reduce capital expenses while at the same time offering the elastic scalability to handle

fluctuating business needs.

• Reduce dependency by allowing engineers to allocate and manage computing resources

themselves with minimal intervention from the ICT staff.

• Reduce operating and labor costs of managing, deploying and supporting software test

configurations while improving productivity by eliminating the time spent on the setting

and tear-down of the lab environment.

• Facilitate innovation and improve time-to-market by reducing development and testing

setup times from weeks to minutes.



• Improve quality by eliminating critical software issues prior to deployment through

testing in environments that more closely match production environments.

• Maximize efficiency from a centralized lab supporting multiple and/or remote teams.

2.3 Action Plan

The action plan for the migration of the development and test computing environment to the

cloud is broken down as follows.

• The analysis will be based on the Amazon Web Services (AWS) EC2 and EBS services,

which cater for a world-class infrastructure-as-a-service cloud computing platform.

• Sourcing of the development and test environment to the AWS cloud will rely on the

Amazon Virtual Private Cloud (Amazon VPC) architecture. Amazon VPC enables

enterprises to connect their existing infrastructure to a set of isolated AWS computing

resources via a Virtual Private Network (VPN) connection, and to extend their existing

management capabilities such as security services, firewalls and intrusion detection

systems to include their AWS resources in a hybrid architecture that takes full advantage

of the benefits of the AWS cloud in a secured and dedicated area as shown in illustration

2.

• The ICT staff will create a set Amazon Machine Image (AMI) templates—for the

Solaris, Windows and Linux operating systems—containing the software necessary


Illustration 2: Amazon Virtual Private Cloud Architecture (Graphic Courtesy of Amazon)


(middleware, libraries, data and associated configuration settings) for engineers to

perform their customary tasks as if they were working locally.

• One small AWS EC2 reserved instance will be permanently allocated per engineer. In

addition, engineers will be able to allocate additional EC2 instances on demand through

a modified version of the Virtual Instance Reservation Portal (VIRP) application. On

average, engineers will be able to allocate two more small instances on demand when

development and test tasks require to run software on multiple nodes.

• Development and test data will be hosted on Amazon Elastic Bloc Storage (EBS)

volumes. Amazon EBS provides block-level storage volumes for use with Amazon EC2

instances. Amazon EBS volumes are off-instance storage that persist independently from

the life of an instance. Amazon EBS provides highly available, highly reliable storage

volumes that are particularly suited for applications that require a database, file system,

or access to raw block-level storage. Amazon EBS also provides the ability to create

point-in-time snapshots of volumes. These snapshots can be used as the starting point for

new Amazon EBS volumes, and protect data for long-term durability. The same snapshot

can be used to instantiate as many volumes as is needed. As such, engineers will be able

to maintain multiple versions of the software components under development as well as

multiple configurations and testing scenario setups without needing to hold a complete

virtual machine for such purposes.

• Developers will be allowed to create new EBS volumes of various sizes, ranging from as

little as a few giga bytes to hundreds of giga bytes of storage, through the VIRP

application and mount them onto their EC2 AMI instances dynamically. It is estimated

that engineers will need on average no more than three EBS volumes ranging from 70 to

250 GB in size.

• The number of managed servers—which for the most part are inefficient legacy

machines whose use and purpose are not always clear to the IT team — and as such the

virtual machines equivalent, will be drastically reduced. By transferring its computing

assets to the cloud, GEC will also be able to “pull the plug” on those unattended or

quiescent systems and avoid the effect of server sprawling in the future.



3 Cloud Computing

3.1 Overview

According to a recent post by Brian Finnerty in Network World, many analysts and practitioners

covering the ICT industry predict that 2010 will be the year of the cloud with the endowment of

Salesforce.com reaching the $1 billion revenue mark, thus showing that organizations are

accelerating adoption of the cloud (Finnerty 2010).

But as observed by Gartner in (Cearley, & Smith 2010, pp.3-4), cloud computing should not be

viewed as an entirely new paradigm that is divorced from previous Internet and ICT innovations.

Cloud computing today emerges from the synergistic intersection of the commoditization and

standardization of the Internet as a global computing platform that was first envisioned by John

Burdette Gage from Sun Microsystems in the phrase “The network is the computer”. The Web 2.0

combined several technologies that made the Internet an emerging enterprise ICT platform. On the

server side, the widespread adoption of the Web services technology allowing easy publishing,

access and integration of computing and infrastructure management services from diverse

organizations was a determinant enabler. On the client side, Rich Internet Applications (RIAs)

based on AJAX, Flash/Flex, or JavaFX programming languages permitted desktop-like applications

within a browser, including local persistence for offline use, enriched graphics processing, and

integration with local devices. Also, the massive development of broadband network access that

boosted the “dot com” era enabled the actual processing of applications to take place on the next

building or on the other side of the world without making much difference to the end-user. Without

the technologies supporting RIA applications and broadband networking, cloud-based services such

as Salesforce.com and Gmail would not have been possible. In addition, other advances such as

hardware virtualization, multi-tenant architecture, parallelization engines and grid architecture were

essential technologies favoring the emergence of cloud computing.

Putting them together brought a new style of computing and an industry phenomena that is

driving market disruption and creating new opportunities for enterprises to exploit information

technology. The discontinuity offered by cloud computing implies that the ability to deliver

specialized services in ICT can be paired with the ability to deliver those services in an



industrialized and pervasive way (Smith, Cearley et al. 2009, pp.3-8). The reality of this implication

is that users of ICT-related services can now focus on what the services provide to them, rather than

on how the services are implemented or hosted, similar to the way utility companies sell power to

subscribers. The following illustration from Gartner shows the s-curve technology innovations map

that led to the concept of cloud computing (Cearley & Smith 2010, p.4).

As such, cloud computing heralds the promises of an alternate delivery and acquisition model of

ICT-related services that will change the way purchasers of ICT products and services contract with

vendors, and the way those vendors deliver their offerings (Smith, Cearley et al. 2009, p.4). By

shifting the ICT products and services procurement process from a license-based and on-premises

buying model (which has dominated the ICT industry for so long) to a service-contracting buying

model, cloud computing offers a new game-changing alternative.

Cloud computing represents a new tipping point for the value of network computing. It is

perceived in many ways as a broader metaphor for the Internet as it allows consumers and

businesses to use remote applications from any computer plugged on the Internet without prior

software installation. It's main promises are to deliver cost-effective computing efficiency, massive

scalability, faster and easier software deployment. It is also associated with new programming

models, new ICT infrastructures, and the enabling of new business models. A new generation of


Illustration 3: Cloud Computing Evolution and Revolution (Graphic Courtesy of Gartner)


ICT services delivery —built on the advent of the Web 2.0, utility computing, virtualization, and

automated provisioning breakthrough technologies—are considered by many ICT practitioners and

analysts as disruptive innovations that will deeply transform the way the various actors of this

industry do business on both sides of the fence—the established technology vendors, but also

crucially, the technology users themselves.

In a memorandum of October 2005, Bill Gates set the tone in an allocution to Microsoft's top

executives: “The next sea change is upon us” (Gates 2005), alerting the company that the rise of

cloud computing is a serious threat to Microsoft's software business and that these revolutionary

“services designed to scale to tens or hundreds of millions [of users] will dramatically change the

nature and cost of solutions deliverable to enterprises or small businesses” (Gates 2005). In effect,

Microsoft's dominance on the desktop market may fade in importance as people may no longer need

to install software on their PC, but get an equivalent at a fraction of the price from online SaaS

offerings like Google Apps6.

“Specifically, internet software complements the power of the personal computer by

integrating traditional desktop software with applications and services that reside on

remote computers accessed over the internet. Driven by consumer demand for

ubiquitous access to applications and information, for extensive integration between

technologies, and for interoperability between complementary products and services,

this market is slated to reach $US100 billion by 2011.” (Iansiti et al. 2009, p.4)

Another prominent promise set forth by cloud computing is the economy of scale that can be

achieved through the commoditizing of computing resources such as processor, memory and

storage. A metaphor called utility computing—that can be interchangeably used for cloud

computing—is compared to that of the industrialization of electricity power plants that sprung up

throughout the US and Europe by the end of the 19th century, which provided cheap and plentiful

electricity that submerged and shaped the world we live in today in just a few decades according to

Nicholas Carr7, a well-known technology writer and blogger.

“Access to ubiquitous information processing should flow through the sockets in our

walls like electricity does [...] Capitalizing on the advances in the power of the micro-

6 http://www.google.com/apps/intl/en/business/index.html#utm_medium=et&utm_source=catch_all

7 Author of two books "The Big Switch" and "Does IT Matter?"



processors and the capacity of storage systems, firms like Amazon and Google are

beginning to build massive and massively efficient information processing plants that

are using the broadband Internet to reach customers” (Carr 2009b, p.101)

Looking at the metaphor of electricity is useful in understanding the technology needed to

industrialize datacenters. It was only after the widespread deployment of the “rotary converter”, a

device that transforms one kind of current into another, that different power plants and generators

could be assembled into an electricity grid. Similarly, virtualization allows physical computing

resources to be assembled into a computing grid platform. As mentioned previously, the term cloud

computing is quite recent, but the technological components underpinning the concept have been

around for years. For instance, distributed computing, cluster computing, grid computing, virtual

machine technologies are not new. Each of these technological advances are distinct in some ways,

even though there is a great deal of overlap between them. But what makes cloud computing

different is the maturation of the Internet as a global ICT platform. A key catalyst for this innovation

has been the commercial success of major Internet companies like Google, Amazon and Microsoft.

It is Amazon, the online book retailer giant we know, who pioneered the first commercial utility

computing offering called Amazon Web Services (AWS) in 2006. Since then, anybody with a credit

card can rent a virtual machine on Amazon's vast datacenter facilities by the hour and run almost

any kind of application on it. That is a paradigm shift.

3.1.1 Hype versus Reality

Hence, cloud computing has become an exceptionally hyped concept. Nowadays, computer and

software makers are all exited about cloud computing that Gartner positioned at the Peak of Inflated

Expectations on the Hype Cycle for Emerging Technologies in 20098.

In the report Hype Cycle Special Report for 2009, Gartner evaluates the maturity of 1,650

technologies and trends in 79 technology, topic and industry areas, which include cloud computing

and green IT9.

By looking at real benefits for those technologies, as opposed to hyped expectations, Gartner in

(Fenn et al. 2009) sees a number of potentially transformational technologies happening in 2009

8 See Appendix II: Gartner's Hype Cycle Explained

9 See Appendix III: Gartner's Hype Cycle for Emerging Technologies in 2009



that will hit mainstream markets in less than five years, which include cloud computing and green

IT, as the increase in high-density ICT equipment (server, storage and communication), the growing

cost and scarcity of power, and the move toward a greener environment are requiring new

technologies to meet the enterprise's growing needs. As shown in Gartner's Hype Cycle for

Emerging Technologies in 200910, cloud computing is just at the tipping point of the Peak of

Inflated Expectations curve, heading for the Trough of Disillusionment, meaning that, as time

passes, impatience for meaningful and real value will begin to replace the original excitement about

cloud computing.

In another report entitled Hype Cycle For cloud Computing, 200911, Gartner argues in (Smith,

Kenney et al. 2009, p.4) that cloud computing is the latest superhyped concept in ICT, for which

everyone has a perspective and an opinion, but the confusion is rampant and misconceptions

abound, particularly with regard to cost cutting. This viewpoint is supported by another study

conducted by Version One in June 2009, which found that 41% of senior ICT professionals actually

don't know what cloud computing is and two-thirds of senior finance professionals are confused by

the concept, highlighting the young nature of the technologies involved (Ebbrell 2009).

The Hype Cycle For cloud Computing, 2009 shows that many cloud computing technologies

and concepts will see mainstream adoption in two to five years, with the exception of those that

have been in use for some time, including grid computing, Web hosting, virtualization and SaaS

offerings such as Salesforce.com for sales force automation that have reached the Slope of

Enlightenment.

Nonetheless, an overwhelming number of cloud-related technology triggers are positioned at

pre-peak. This is not so surprising according to (Smith, Kenney et al. 2009, pp.7-34) since many

applications and technologies constitutive of the cloud computing phenomenon are new. Newer

concepts, such as private cloud computing, elasticity, cloud-bursting and application platform-as-a-

service (PaaS) are ramping up the Peak of Inflated Expectations in a myriad of innovative ways.

Some other items will take five to ten years, including cloud-bursting and platform-as-a-service, for

mainstream adoption to occur.

10 See Appendix III: Gartner's Hype Cycle for Emerging Technologies in 2009

11 See Appendix IV: Gartner's Hype Cycle for Cloud Computing in 2009



Also, in the same report, the Gartner Priority Matrix12 shows that some of the most impactful

items include PaaS, virtualization, elasticity and private cloud computing. Companies that are

conservative in their technology adoption (Type C organizations) may limit their focus to orange

areas, whereas companies that are more aggressive technology adopters (Type A and Type B

organizations) are most likely already using cloud computing technologies that will mature in less

than two years, and may consider investing other cloud computing technologies and concepts first

in yellow areas and second in gray areas13.

Of particular interest to this study is the “Cloud Computing for the Enterprise” technology

trigger that Phifer in (Smith, Kenney et al. 2009, pp.47-51) estimates will be adopted in between

five to ten years, with a current penetration rate in the target audience of 1% to 5% 14. The business

impact is potentially significant to an enterprise, including reduced total cost of ownership (TCO),

accelerated time-to-market and reduced costs for real estate, heating, ventilation and air-

conditioning (HVAC), as well as support personnel. Some enterprises already use some software-as-

a-service (SaaS) offerings like those from Salesforce.com. Fewer enterprises are using

infrastructure-as-a-service (IaaS) offerings like storage as a service, and application platform-as-a-

service (PaaS), which are frequently used on an ad-hoc departmental basis, without the knowledge

or even consent of the ICT organization. Phifer also outlines a number of outstanding issues among

which security, data ownership and reliability are of primary concern to enterprises reluctant to

using cloud-based services. Today, only Type A enterprises willing to take on risk, will be willing

to play in the cloud. However, even though these issues don't yet have adequate answers, cloud

computing providers are rapidly addressing them; therefore, cloud computing should soon pose an

acceptable risk for Type B enterprises (Smith, Kenney et al. 2009, p.19).

The adoption of cloud-based services has been spotty until 2009, but will accelerate in 2010 as

technologies and offerings of cloud computing mature. This trend should grow bold in the future,

with small businesses and enterprises in developing nations taking great advantage of cloud-based

services. Large enterprises will adopt the cloud at different paces, depending on their risk profiles.

These observations are consistent with another in-depth research held by the Burton Group in

12 See Appendix V: Gartner's Priority Matrix for Cloud Computing, 2009

13 See Exhibit II: Gartner's Hype Cycle Explained

14 Focuses primarily on the enterprise adoption of cloud computing services from the public cloud. It does not focus

on the adoption of private cloud by enterprises, as this is on a different trajectory.



(Reeves 2009)

“This situation is not a cause for alarm, but rather the natural course of ICT and

business. Burton Group has—on several occasions—made the case that ICT resources

are more than just tools to accomplish a business goal. They are, in fact, the medium by

which business is done. [...] Make no mistake, cloud computing is coming. It represents

the next step in ICT transformation whereby internal and external ICT assets become

inextricably intertwined with the business.”(Reeves 2009, p.9)

3.1.2 Market Forecast

The analyst firm IDC believes the depressed economic climate will drive more enterprises to

consider and adopt cloud-based services. They predict that spending on cloud services will hit

$US42 billion by 2012 (Maitland 2009). Similarly, IDC estimates in (Syntec 2010, p.8) that the

market of cloud-based services amounted to 5% of 2009's worldwide ICT spending, representing

$17 billion. Following a double-digit growth of 25% a year, this market could reach $44 billion in

2013, representing 10% of ICT spending worldwide. In Europe, the analyst firm PAC,

commissioned by the European Commission (EC), estimates in (Syntec 2010, p.8) that the cloud-

based services market amounted to 4 billion euros in 2009, representing 1.5% of the total software

and ICT consulting industry. This number could grow to 13% by 2015.



In France, the research firm Markess International estimates the total cloud computing and

server hosting services market to represent over 2.3 billion euros in 2009, and expects a steady

growth in the SaaS and IaaS segments (Syntec 2010, p.9).

3.2 A View of the Cloud from the Ground

While there exists an overwhelming plethora of business- and technical-related articles, analyses

and reports about cloud computing, there is surprisingly little academic studies devoted to this

phenomenon. As stated earlier, confusion among ICT practitioners still abounds when cloud

computing is discussed. For example, many people believe that there will be thousands of public

clouds, while Gartner, for instance, recognizes that there is only one cloud that they call “The World

Wide Computer”. Certainly, there are many cloud platforms, such as Amazon's EC2, and many

cloud services from companies ranging from Google to Zoho; all of them will inject their services

into the one public cloud. Some believe that SaaS and cloud computing are exactly the same, while

others do not. In its controversial report “Clearing the air on cloud computing”, McKinsey & Co.

argues that the lack of sharing a common understanding and rigorous definition of cloud computing

prevents managers from being analytical in their decision-making and from making more informed


Illustration 4: Cloud Computing Market Forecast in Million € for the EU's 27 Member States

(Graphic Courtesy of Syntec - ©PAC )


investment decisions. A clearer definition of cloud computing would allow service providers to

build more meaningful products, marketing and sales strategies that would translate into real value

for their customers.

Cloud computing is one of those terms whose definition and interpretation vary the most

between groups of people who have different perspectives and varying degrees of understanding

and exposure. The variety of technologies and the hype surrounding the concept, as stated earlier,

reinforces the overall confusion about the paradigm and its capacities, turning the cloud into an

excessively used general term that includes almost any solution that allows the outsourcing of all

kinds of ICT services. Another source of confusion highlighted by Vaquero et al. in (2008) lies in

the relation of cloud computing o grid computing. “The distinctions are not clear, maybe because

clouds and grids share similar visions: reduce computing costs and increase flexibility and

reliability by using third-party operated hardware.” (Vaquero et al. 2008, p.51). However, cloud,

grid and utility computing differ in subtle ways. Cloud computing includes attributes previously

associated with utility and grid computing models. Grid computing refers to the ability to harness

large collections of independent and heterogeneous computing resources to perform large tasks, and

utility is a metered consumption of ICT services. Cloud computing is taking these attributes

together, creating a more exiting ICT service delivery value proposition, says Kristof Kloeckner, the

cloud computing software chief at IBM in (Brodkin 2009).

Smith et al. in (2009) also believe that it is important to dig beyond the general cloud computing

concept to separate the hype from the actual ideas and technology benefits. To understand them it is

necessary to tear apart the hype surrounding cloud computing by focusing on more granular topics

which are part of the cloud phenomenon.

The following Section explores the concept in greater detail as confusion and conflicting

definitions may lead to the risk of overlooking what business benefits can be obtained from this

paradigm shift. This exploration includes an analysis of the business and technological advances

underpinning the concept in an attempt to delineate the scope of our research and focus primarily on

the utility dimension of cloud computing and on how it partakes with green IT to achieve the

expected business benefits of our project.

3.2.1 Defining the Concept



This Section presents some notable attempts at rigorously defining cloud computing and

capturing its key characteristics. It is important to keep in mind that the concept is in constant

evolution, so any definition is worth only for how cloud computing is perceived today. Its key

characteristics, deployment models, risks and benefits will be refined over time as the technology

and business models mature, and knowing also that the ICT industry represents a large ecosystem of

business models, vendors and market niches.

Nonetheless, any definition should attempt to encompass all of the various cloud approaches.

Vaquero et al. in (2008, p.50) report that there are over 20 different definitions of cloud

computing in literature today. They argue that these definitions seem to merely focus on certain

aspects of the technology and that none of them put forward a completely integrative definition.

Thus, they have tried to reach a definition that encompasses all of them by extracting relevant

cloud features and combining them to form both an integrative and basic cloud definition containing

the essential characteristics of the technology. Among these definitions, Vaquero et al. claim that the

one proposed by Jeremy Geelan in “Twenty one experts define cloud computing” (Geelan 2009) is

particularly interesting because it gathers definitions proposed by many experts, even though his

definition lacks a more global analysis of those proposals to reach a more comprehensive definition.

A minimalist definition of cloud computing by Vaquero et al. stands as a“Pay-per-use utility model

and virtualization” (Vaquero et al. 2008, p.51).

A minimalist definition such as the above, looking for a minimum common denominator, brings

little value. A more useful, yet unified and comprehensive, definition of cloud computing by

Vaquero et al. is substantially more thorough.

“Clouds are a large pool of easily usable and accessible virtualized resources (such as

hardware, development platforms and/or services). These resources can be dynamically

reconfigured to adjust to a variable load (scale), allowing also for an optimum resource

utilization. This pool of resources is typically exploited by a pay-per-use model in which

guarantees are offered by the Infrastructure Provider by means of customized SLAs”

(Vaquero et al. 2008, p.51)

This definition does not explicitly include software and platform services, nor does it emphasize

the economic values of cloud computing.



Armbrust et al. (2009) from the RADS Laboratory of UC Berkeley take a slightly different

approach to defining cloud computing in “Above the clouds: A Berkeley view of cloud computing”,

a well-regarded academic paper on the subject. They argue that the term cloud computing refers to

both:

• Applications delivered as services over the Internet.

• The hardware and systems software datacenters that carry these services' runtime.

These two bullet points can be contracted as “self-service ICT delivered through automation”.

They also claim that a cloud is different from a conventional datacenter in several ways. From a

hardware point of view, at least two aspects are new with the cloud.

• Cloud platforms bring the illusion of infinite computing resources available on demand,

thereby eliminating the need to plan capacity growth ahead of time.

• Cloud platforms eliminate upfront investments as companies of all sizes can provision ICT

resources on demand, with no upfront expenses or long-term commitments, and pay only for

the resources they use. The ability to pay for resources on a short-term basis as needed saves

energy by freeing machines and storage devices when they are no longer useful.

When a cloud is made available to the public in a pay-as-you-go manner, it is referred to as a

public cloud, and the service being offered as utility computing. Classic examples of public clouds

include Amazon Web Services (AWS), Google AppEngine, and Microsoft Azure.

As such, (Armbrust et al. 2009, p.1) formally define cloud computing as follows:

“Cloud computing refers to both the applications delivered as services over the Internet

and the hardware and systems software in the datacenters that provide those services…

The datacenter hardware and software is what we will call a cloud…Cloud computing

has the following characteristics:

- The illusion of infinite computing resources

- The elimination of an up-front commitment by cloud users

- The ability to pay for use as needed…”



However, this definition does not emphasize the idea of resource abstraction or infrastructure

virtualization explicitly. McKinsey & Co. provides yet another definition proposal.

“Clouds are hardware-based services offering compute, network and storage capacity

where:

- Hardware management is highly abstracted from the buyer

- Buyers incur infrastructure costs as variable OPEX

- Infrastructure capacity is highly elastic (up or down)” (McKinsey&Company 2009,

p.12)

McKinsey & Co. argues that cloud services known as SaaS may be confused with true clouds

when they comply only with one or two of the above key requirements, but not all three. Typically,

McKinsey & Co. would not qualify Gmail and Salesforce.com services as cloud-based services

because they do not comply with the infrastructure cost as variable OPEX requirements.

Finally, I will conclude with the definition of cloud computing by the National Institute of

Standards and Technology (NIST), which is the American equivalent of the Association Française

de Normalisation (AFNOR), the French national organization for standardization.

“Cloud computing is a model for enabling 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. This cloud model promotes

availability and is composed of five essential characteristics, three service models, and

four deployment models” (Mell & Grance 2009, p.2)

In reality, it is unclear whether there is such a thing as a canonical, standard and commonly

agreed definition of cloud computing because it is a complex and continuously evolving concept. To

fully understand it, we need to describe it as an overall paradigm shift within which emerge some

essential and unique characteristics, as well as the service and deployment models that underpin the

multiple ways cloud computing is being brought to market. The following Section describes the

three most common service models known to cloud computing today, and provides a description of



its commonly agreed fundamental characteristics and deployment models.

3.2.2 Cloud Computing Service Models

Clouds platforms are mainly distinguished by the level of abstraction and level of management

of the virtual resources they provide, which are categorized into three distinct classes of services (or

service models) .

1. Cloud Infrastructure-as-a-Service (or IaaS): the capability offered to the customer is to

provision processing, storage, networks and other fundamental computing resources where

the customer is able to deploy and run arbitrary software, which can include operating

systems and applications. The customer does not manage, control or own the underlying

cloud infrastructure, but has control over the operating systems, storage, deployed

applications, and may have limited control over select networking components (e.g., host

firewall). Cloud IaaS providers delegate control over the cloud infrastructure via

management interfaces and application programming interfaces (API) to enable resource

provisioning and configuration automation from external programs. These APIs are often

proprietary but tend to be standard under customer pressure. Cloud IaaS providers manage

very large amounts of computing and storage capacity. Through virtualization, they are able

to split, assign and dynamically resize these resources to build ad-hoc systems as requested

by customers.

2. Cloud Platform-as-a-Service (or PaaS): the capability offered to the consumer is to deploy

acquired or developed applications onto a virtualized middleware infrastructure using

programming languages and tools supported by the provider. The consumer does not

manage or control the underlying cloud infrastructure ( network, servers, operating systems,

or storage), but has control over the deployed applications and possibly the application

hosting environment configuration. In addition, cloud PaaS can provide service continuity

capabilities such as auto-scaling and automatic failover that are transparent to the end-user.

3. Cloud Software-as-a-Service (or SaaS): the capability offered to the consumer is to use the

provider’s applications running in a cloud infrastructure. The applications are accessible

from various client devices through a thin client interface such as a web browser (e.g. web-

based email). The consumer does not manage or control the underlying cloud infrastructure



such as the network, servers, operating systems, storage, or even individual application

capabilities, with the possible exception of limited user-specific application configuration

settings. Cloud SaaS offers an alternative to locally run applications.

It is important to understand that each service model category serves a different purpose and

offers different classes of services for businesses and individuals around the world. In addition, a

SaaS cloud may use the north-bound services PaaS cloud, and a PaaS cloud may use the north-

bound services of an IaaS cloud as shown in illustration 5.

An IaaS cloud, such as Amazon EC2, is generally viewed at one end of the resource abstraction

spectrum because the virtual resources it provides look much like physical hardware (i.e. raw CPU

cycles, block-device storage, IP-level networking) whereby a user can control the entire software

stack in a virtual server instance from the kernel and upwards. The virtual infrastructure

management interface in a typical IaaS cloud is limited to a few dozen application programing


Illustration 5: IaaS, Paas, SaaS: Who is maintaining what?

TraditionalIn-house ICT

TraditionalIn-house ICT

Network

Storage

Physical Machine

Virtualization

Virtual Machine

Operation System

Middle-Ware

Business App.

IaaS ModelIaaS Model

Network

Storage

Physical Machine

Virtualization

Virtual Machine

Operation System

Middle-Ware

Business App.

PaaS ModelPaaS Model

Network

Storage

Physical Machine

Virtualization

Virtual Machine

Operation System

Middle-Ware

Business App.

SaaS ModelSaaS Model

Network

Storage

Physical Machine

Virtualization

Virtual Machine

Operation System

Middle-Ware

Business App.

Service or product maintained by the cloud provider

Service or product maintained by the enterprise


interface (API) calls used to allocate and configure the virtualized hardware. It is also the type of

cloud that provides the greatest flexibility because there is no a-priori limit to the kind of

applications that can be hosted in this infrastructure. But its low level of abstraction makes it

inherently difficult for users to get transparent scalability and failover service capabilities.

Customers must implement the machinery themselves or subcontract the task to third-party value-

added resellers (VAR).

Higher-level managed services are offered by PaaS clouds, such as Google AppEngine,

Force.com—the salesforce business software development platform—and Microsoft's Azure.

Google AppEngine is a domain-specific platform targeted exclusively at web application design.

Google AppEngine provides impressive automatic scaling and high-availability capabilities out-of-

the-box, but imposes on the application developer a programming model and framework that

enforces a clean separation between a stateless computation tier and a stateful storage tier.

Furthermore, AppEngine applications are expected to be request-response based, and are severely

restricted on the amount of CPU time they can use to serve requests.

Similarly to Google AppEngine, Force.com is designed to support business applications that run

with the Salesforce.com database and nothing else. Contrarily to Amazon EC2, Google AppEngine

and Force.com do not seem suitable for general-purpose computing.

As an intermediate alternative, applications for Microsoft's Azure are written using .NET

libraries, and compile to the Common Language Runtime, a language-independent managed

environment for virtual machines. The system supports general-purpose computing rather than a

single category of applications. Users get a choice of language but cannot control the underlying

operating system or runtime. Thus, Azure is an intermediate solution between application

frameworks like AppEngine or Force.com and virtual machine and storage environments like

Amazon EC2.

In addition, it is argued in (Smith, Cearley et al. 2009) and (Armbrust et al. 2009) that the level

of abstraction by which virtual resources can be used and managed by the customer is an important

differentiator that determines the extent to which scalability and failover can be performed

automatically by the cloud infrastructure in a way that is independent from the application design.

AppEngine, Force.com and Azure each offer proprietary features or large and complex APIs such as

Azure's .NET libraries that have no free implementation. Therefore, applications developed for



AppEngine, Force.com and even Azure cannot be migrated easily from one cloud to another, nor

can they be re-hosted to self-hosted servers. Thus, choosing a PaaS solution today could be a lock-

in decision—due to the proprietary nature of the programming model—until standards are set.

3.2.2.1 Examples of SaaS Providers

As of today, the SaaS segment of cloud computing is the only segment that has proven

successful as a business model with SalesForce.com in the CRM industry. By running business

applications over the internet from centralized servers rather than from on-site servers, companies

can save money by avoiding maintenance licensing and hardware equipment costs. Furthermore,

with SaaS, companies are able to run applications much more efficiently from a computing

standpoint as SaaS greatly simplifies the software lifecycle management. Consumers of SaaS can

access their services anytime, anywhere, share data and collaborate more easily, and keep their data

stored safely in the infrastructure.

A myriad of vendors are offering SaaS cloud computing today. The companies listed below are

already well-established SaaS vendors. They usually charge a subscription fee.

• Salesforce.com

• Goggle Apps

• NetSuite

• Taleo

• Concur Technologies

3.2.2.2 Examples of PaaS Providers

The PaaS segment of cloud computing refers to products that are used to develop and deploy

applications using the integrated development environment of the platform. The following

companies have developed platforms that allow end users to access applications from centralized

servers using the internet. Next to each company is the name of their platform.

• Google (GOOG) - Apps Engine

• Microsoft (MSFT) - Azure

• Terremark Worldwide (TMRK) - The Enterprise cloud



• Salesforce.com (CRM) - Force.com

• NetSuite (N) - Suiteflex

• Mosso - Mosso, a division of Rackspace

• Metrisoft - Metrisoft SaaS Platform

3.2.2.3 Example of IaaS Providers

The final segment in cloud computing, known as IaaS, constitutes the underpinning basis of the

utility computing concept. Cloud-based infrastructure vendors provide the physical storage space

and processing capabilities.

Below are major IaaS that provide infrastructure services.

• International Business Machines (IBM) - managed hosting

• SAVVIS (SVVS) - managed hosting

• Terremark Worldwide (TMRK) - managed hosting

• Amazon.com (AMZN) – EC2 and S3

• GoGrid Cloud Hosting

• Rackspace Cloud Hosting

3.2.3 Fundamental Characteristics of Cloud Computing

According to the NIST (Mell & Grance 2009), there are six fundamental characteristics that

make cloud computing different from other more conventional ICT outsourcing facilities. Many

academics and industry analysts including (Armbrust et al. 2009), (Vaquero et al. 2008)

(McKinsey&Company 2009) and (Daryl C. Plummer et al. 2009), have refined these characteristics

and argue that to be considered a cloud service, a solution should adhere to some combination of

these attributes.

1. On-demand self-service: a customer can unilaterally provision computing resources as

needed, such as server time and network storage, without requiring any human intervention

in the process and by using just a credit card. It is not even necessary to be registered as a

legal commercial entity. Plummer et al. in “Five Refining Attributes of Public and private



cloud Computing” further contend the importance of the service-based characteristics of

cloud computing through the claim that consumer concerns should be abstracted from

provider concerns in that the interfaces should hide the implementation details and enable a

completely automated and ready-to-use response to the consumer of the service. The

articulation of the service feature is based on service levels and ICT concerns such as

availability, response time, performance versus price, and clear and predefined operational

processes, rather than technology and its capabilities. In other words, “what the service

needs to do is more important than how the technologies are used to implement the solution”

(Daryl C. Plummer et al. 2009, p.5)

2. Pay-per-use: customers pay services on a pay-per-use (or pay-as-you-go) basis. This

characteristic addresses the utility dimension of cloud computing, but utility computing must

not be confused with cloud computing as a whole because it relates rather to the pay-per-use

business model of the cloud rather than being a substitutive term. Furthermore, the pay-per-

use transferability does not make the difference between buying compute power out of one

virtual machine for 1000 hours and buying compute power out of 1000 virtual machines for

one hour. The cost is the same. Altogether, the pay-per-use characteristic implies that

enterprises incur no infrastructure capital costs, just operational thanks to the pay-per-use

business model with no contractual obligations.

3. Scalable and elastic: elasticity is a key characteristic of cloud computing, which resides in

the ability to quickly increase or decrease ICT resources (i.e. compute power, storage and

network capacity on demand). Elasticity15 is a trait of shared pools of resources. It allows

provisioning any number of server instances and disk storage programatically in an

economical way, that is, on a fine-grained basis with a lead time of minutes rather than

weeks. To a customer, capabilities available for provisioning ICT resources often appear to

be unlimited and can be purchased in any quantity at any time which allows matching

resource allocation with the current workload. Scalability is a feature of the underlying

infrastructure and software platforms. For example, a scalable service called Auto Scaling in

AWS removes the need for customers to plan far ahead of time for more capacity. For

example, AWS Auto Scaling allows to automatically scale Amazon EC2 capacity up or

15 A term first coined by Amazon



down according to the conditions set by the customer. Amazon claims that with Auto

Scaling, customers can ensure that the number of Amazon EC2 instances they are using

scales up seamlessly during demand spikes to maintain performance, and scales down

automatically during demand lulls to minimize costs. Complementary to the concept of

elasticity are the concepts of over-drafting or cloud-bursting. Cloud-bursting pushes

elasticity even further by allowing to automatically get more capacity from external clouds

when the primary cloud infrastructure is overloaded. The concept of over-drafting also

supports the idea of a cloud Market Exchange whereby providers could trade compute

capacity at a spot price when supply and demand fluctuate over time. Cloud-bursting, in

turn, allows to migrate application workloads out of the in-house datacenter to the cloud

when the local infrastructure is overloaded.

4. Shared resource pooling: ICT resources are pooled to serve multiple consumers in a multi-

tenant (shared) fashion to leverage economies of scale and achieve maximum efficiency.

Amrbrust et al. (2009) coined the term statistical multiplexing to describe how providers bet

on the likelihood that, at any point in time, the demand never exceed the capacity of the

infrastructure by shuffling virtual resources across datacenters. This scheme works on the

principle that the more applications and the more users using the cloud, the more dispersed

are the load profiles, whereby applications running at peak load can “borrow” resources

from idle applications or applications running in a quiet state. To be efficient, resource

pooling requires a high degree of virtualization, automated operations and state-of-the-art

resource provisioning capabilities. There is a sense of location independence and

transparency in that the customer generally has no control or knowledge over the exact

location of the provided resources but may be able to specify location at a higher level of

abstraction (for example, country, state or datacenter).

5. Uses Internet technologies: cloud services are delivered using Internet technologies

including Universal Resource Identifiers (URI), data formats and protocols, such as XML,

HTTP, and representational state transfer (Restful) Web services. In other words, cloud

computing is Internet-centric, allowing ubiquitous access over the network through standard

mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile

phones, laptops and PDAs). In that, it offers a computing software model that is multi-



platform, multi-network and global.

6. Metered by use: cloud services are tracked with usage metrics to enable multiple payment

models. The service provider has a usage accounting model for measuring the use of the

services, which could then be used to create different pricing plans and models. These may

include pay-as-you go plans, subscriptions, fixed plans and even free plans. The implied

payment plans will be based on usage, not on the cost of the equipment. These plans are

based on the amount of service used by the consumers, which may be in terms of hours,

data transfers or other use-based attributes delivered providing transparency for both the

provider and the consumer.

3.2.4 Cloud Computing Deployment Models

A common misperception about cloud computing is that, eventually, there will be only a handful

of cloud platforms, all of which public. This is highly unlikely given the complex ICT needs in

large organizations, according to the consulting firm Accenture in its special report “What the

Enterprise Needs to Know about cloud computing” (Accenture n.d., p.14).

While some general-purpose public clouds will exist, two other types of cloud are likely to

emerge. One type, community or speciality clouds, will cater to the particular needs of a select

group of organizations, an industry or even a country. The healthcare industry is a good example

since the inherent nature of medical records underscores the need for clouds to be non-public so as

to ensure data security, while levering mutualized infrastructures to lower ICT costs. Likewise,

some large multinationals may opt to build and operate their own private clouds or internal clouds

while continuing to tap into external cloud sources. In this way, they can achieve both elasticity and

control over service quality, security, data ownership and integrity, and other important regulatory

issues. Furthermore, there are applications that simply don't run well in a pure multi-tenant

environment. Databases, for example, perform better on dedicated hardware where they don't have

to compete for server input/output (I/O) resources. Plus, some businesses prefer to run databases on

dedicated hardware for PCI compliance16 reasons or because they do not want sensitive data to

reside on a shared platform, even if the environment is highly secure.

Other applications, such as web servers, run well in the cloud because they can use the elasticity

16 PCI compliance is covered in the risks sections below.



of the cloud to scale rapidly. For example, GoGrid Hybrid Hosting gives businesses the option and

flexibility of building a secure, high-performance scalable server network for hosting web

applications, using a combination of cloud and dedicated server hosting interconnected via a private

network link.

Overall, the NIST as well as other practitioners and academics agree to identify four common

cloud computing deployment models.

3.2.4.1 Public Cloud

In simple terms, public cloud services are characterized as being available to clients from a

third-party service provider via the Internet. The term “public” does not always mean free, even

though it can be free or fairly inexpensive to use. A public cloud does not mean that a user's data is

publicly visible; public cloud vendors like Amazon typically provide an access control mechanism

for their users. Public clouds provide an elastic, cost-effective means to deploy solutions.

3.2.4.2 Private Cloud

Private cloud computing—sometimes called Enterprise or Internal cloud computing—is a style

of computing where scalable and elastic ICT-enabled capabilities are delivered as a service to

internal customers using Internet technologies. This definition is very similar to the definition of

public cloud. Hence, the distinction between private cloud and public cloud relates to who can

access or use the services in question and who owns or coordinates the resources used to deliver the

services (Daryl C. Plummer et al. 2009, p.5). In other words, a private cloud is a cloud that

implements the cloud computing model in a private facility where only a single organization has

access to the resources that are used to implement the cloud. Therefore, it is a cloud that an

organization implements using its own physical resources such as machines, networks, storage, and

overall data center infrastructure(Wolsky 2010). A private cloud intends to offer many of the

benefits of a public cloud computing environment, such as being elastic and service-based, but

differs from a public cloud in that in a private cloud-based service environment, data and processes

are managed within the organization for an exclusive set of consumers without the restrictions of

network bandwidth, security exposures and legal requirements that public cloud services may entail.

In addition, private cloud services are supposed to offer providers and users greater control over the



infrastructure, improve security and service resilience because its access is restricted to designated

parties. Nonetheless, a private cloud is not necessarily managed and hosted by the organization that

uses it as it can be managed by a third party and be physically located off premises, built atop of a

public cloud infrastructure or built as a hybrid cloud. In principle, a private cloud assumes a

dedicated hardware environment of pooled hardware resources with a virtualization layer running

on top of it, allowing an enterprise to create and manage multiple virtual servers within a set of

physical servers and charge the organization's business units per usage. According to Gartner in

(Bittman 2009, p2), it is envisioned that private clouds may prevail in the first phases of the cloud

computing era whereby many large companies will offload their ICT operations from running their

own data and enterprise applications to secure offsite clouds linked to the company's offices through

virtual private networks (VPN) over the Internet. There is some amount of controversy whether a

private cloud should be considered as a genuine cloud-based computing environment. For instance,

(Armbrust et al. 2009) argues that except for extremely large infrastructures of hundreds of

thousands of machines, such as those operated by Google or Microsoft, private clouds exhibit only

a subset of the potential benefits and characteristics of public clouds, as indicated in Table 1

There are inherent limitations to consider with private clouds when it comes to elasticity and

scaling because the number of virtual machines that can be provisioned is limited by the physical

hardware infrastructure. An enterprise can of course add more machines to expand the infrastructure

compute power, but this cannot be done as fast and seamlessly as with public clouds. Thus,

(Armbrust et al. 2009, p.13) argues not to appoint private clouds as full-fledge cloud computing

platforms as this would lead to exaggerated claims. However, they acknowledge that private clouds

could get most of the cloud-based computing benefits when interconnected with public clouds

through a hybrid cloud-based computing model. The Table below summarizes the key differences

between public clouds and private clouds.

Table 1: Key Characteristics of Public and Private Clouds

Characteristics Public cloud Private cloud

On-demand self-service Ability to self-provision

computing resources on-demand

is provided out-of-the-box by

public cloud providers although

Ability to self-provision computing

resources on-demand is a feature to

build in an in-house private cloud

or acquire from a private cloud




this feature may be supported

through proprietary APIs.

hosting vendor.

Pay-per-use Ability to pay-per-use on a

variable basis without any upfront

investments excepted those

required to migrate or adapt

existing applications to a public

cloud.

Upfront cost to build the private

cloud infrastructure in-house or

acquire it from a private cloud

hosting vendor. The cost is either

fixed and payed upfront or payed

on a monthly basis.

Scalable and elastic Out-of-the-box illusion of infinite

computing resources available that

can scale up and down on demand

as the load and need for more

capacity vary over time. This

feature is generally supported in a

semi or completely automated

manner.

The illusion of infinite computing

resources is rarely available in a

private cloud environment because

hardware resources are limited. In

addition, in-house-built private

clouds can hardly compete with the

expertise and technical innovation

made by cloud specialists to

seamlessly automate this process.

Shared resource pooling Economies of scale can be

achieved through shared resource

pooling due to the very large size

of public cloud datacenters, and

obtain a high level of efficiency

through the multiplexing of

workloads coming from different

organizations.

Only global and very large firms

can achieve some level of

efficiency and economies of scale

through shared resource pooling

across multiple regions, yet at a

level that is most likely inferior to

that of public cloud vendors.

Uses Internet technologies Use of Internet technologies to

deliver cloud-based services is

part of the PaaS and SaaS

solutions' DNA.

Use of Internet technologies to

deliver cloud-based services in an

in-house private cloud is a feature

to build or acquire from a private

cloud hosting vendor.

Metered by use Cloud-based services metered by Cloud-based services metered by




use for customer chargeback is a

feature provided out-of-the-box by

public cloud vendors.

use for customer chargeback is a

feature to build for an in-house

private cloud or acquire from a

private cloud hosting vendor.

3.2.4.3 Community Cloud

A community cloud is controlled and used by a group of organizations that have shared

interests, such as specific security requirements or a common mission. The members of the

community share access to the data and applications in the cloud.

3.2.4.4 Hybrid Cloud

A hybrid cloud is a combination of a public and private cloud that interoperate. In this model,

users typically outsource nonbusiness-critical information and processing to the public cloud, while

keeping business-critical services and data in their control. The embodiment of hybrid clouds is

sometimes found in what is called a Virtual Private Cloud (VPC) whereby a portion of a public

cloud is isolated to be dedicated for use by a single entity or group of related entities such as

multiple departments within a company. In its simplest form, access to VPC services will be limited

to a single consumer and will deliver a service consumption experience that is virtually identical to

the public cloud services. VPC services are an emerging phenomenon driven by consumers that are

interested in the potential of cloud computing, but who do not want to concede too much control, or

share their computing environment with other customers. When combined with a hybrid cloud

computing model (for example, using internal resources and external cloud computing services)

(Wood et al. n.d.), VPC services have the potential to bridge the gap between public and private

cloud models. By providing additional control, management and security beyond that of public

cloud services, the VPC approach reduces risks and makes it feasible to deploy a wider range of

enterprise applications (Smith et al. 2009, pp.15-16).

Cloud bursting is a technique used along with hybrid clouds to provide additional resources to

private clouds on an as-needed basis. If the private cloud has the processing power to handle its



workloads, the hybrid cloud will not be used. When workloads exceed the private cloud’s capacity,

the hybrid cloud will automatically allocate additional resources to the private cloud.

All three main cloud providers examined for this study (GoGrid, Amazon and Rackspace)

provide some form of hybrid cloud computing services.

3.2.5 Conclusion

Cloud computing is a style of computing whereby, to qualify as a cloud, the offered services

should adhere to a combination of attributes, not just one. Stronger examples of cloud services will

adhere to more attributes of cloud computing than will weaker ones. Consumers and providers of

cloud services must examine these attributes to determine whether the services will deliver the

expected outcome.

The greatest differences between private and public clouds reside in the level of support

available, upfront costs, the extent to which the infrastructure can be isolated and secured from

external threats, and the ability to customize the service delivery and comply with regulatory

compliance as we will see in greater detail below. As public clouds are built atop of shared and

virtualized infrastructures, there are generally more limited customization possibilities than with

private clouds. Private clouds built atop of dedicated servers, storage and networks can more easily

meet the enterprise's security policies, governance and best practice requirements.

But private cloud computing should be viewed as a continuous evolution trend towards a

rationalization of the datacenter and improved operational efficiency rather than as a discontinuous

innovation. This trend is not new in ICT as it already started with the consolidation of the business

applications through virtualization. Thus, private cloud computing is pushing these rationalization

and efficiency objectives one step further by enabling a service-based delivery approach for the

firm's ICT resources and charge consumers (i.e. business units) on a per usage basis. However,

private cloud computing does not bring several of the key business benefits of cloud computing

itself, namely the elimination of an upfront commitment, the transformation of capital expenses into

operational expenses, and the availability of an unlimited amount of ICT resources at a snap of a

finger. Small and medium businesses that do not have a critical mass of compute, storage and

network resources to share in a pool, as well as the human capital and expertise to build and

maintain a cloud-based service delivery model, will not be able to get the expected economies of



scale and operational efficiency promises of cloud computing.

As a general rule of thumb, it is wise to avoid endless discussions about what is and what is not

cloud computing and focus on examining how much a given provider can deliver of the value

proposition of cloud computing through the support of its fundamental characteristics.

3.3 Cloud Computing Benefits

The main economic appeal of cloud computing relies on its usage-based pricing model, often

described as “converting capital expenses (CAPEX) to operating expenses (OPEX). Usage-based

pricing is different from renting in that renting a resource involves paying a negotiated fee to have

the resource available over a period of time, whether or not the resource is actually used. Usage-

based pricing or pay-as-you-go pricing involves metering usage and charging fees based on a fine-

grained usage basis, independently of the time period over which the usage occurs. With Amazon

EC2 for example, it is possible to buy computing resources by the hour and storage by the GB. In

addition, hours purchased can be consumed non-uniformly in that 100 server-hours purchased can

be used fully on the same day of purchase, the day after or at some later time.

Given the economics of cloud computing and the new business models emerging around the

delivery of cloud-based services, new applications can be created and delivered at a radically lower

cost compared to conventional approaches. As such, industry analysts and ICT practitioners have

agreed upon several major benefit forces that should drive the adoption of the cloud.

3.3.1 Transference of Provisioning Risks

Starting a new ICT project is less risky with cloud computing since it is possible to stop at any

point in time without incurring investment losses. The fine-grained economic models enabled by the

elasticity and transference of risks of cloud computing make investment decision tradeoffs more

fluid. Armbrust et al. (2009, p.3) see three particular compelling use cases that favor the utility

dimension of cloud computing.

1. A first case is when demand for a service varies with time. For example, provisioning a

datacenter for the peak load it must sustain a few days per month leads to under-utilization

at other times. Since few firms deliberately provision for less than the expected peak,



resources are idle at non-peak times. The more pronounced the variation, the more the

waste.

2. A second case is when demand is unknown in advance. For example, a web startup will need

to support a spike in demand when it becomes popular, followed potentially by a reduction

once some visitors turn away.

3. Finally, organizations that perform batch analytics can use the “cost associativity” of cloud

computing to finish computations faster: using 1000 EC2 machines for 1 hour costs the

same as using 1 machine for 1000 hours.

In addition, Armbrust et al. (2009, p.3) posit that even if utility computing pay-as-you-go pricing

is more expensive than buying and depreciating a comparable server over the same period, the cost

is outweighed by the extremely important economic benefits gained from the dual risk of wasting

resources—a consequence of over-provisioning capacity—and the risk of losing revenues and

customers due to poor service—as a consequence of under-provisioning capacity. These dual

transference of risk scenarios are illustrated in the two illustrations below.

Even if an increasing demand can be anticipated in planned capacity upgrades, without elasticity


Illustration 6: Traditional datacenter: Capacity vs. Usage (Graphic Courtesy of Amazon)


the enterprise wastes resources during non-peak times and loses revenues from users not served

during peak times. In addition, some users may desert the site permanently after experiencing poor

service; this attrition and possible negative press may result in a permanent loss of revenues for the

organization operating a conventional datacenter.

Conversely, Armbrust et al. (2009, pp.3-4) suggest that thanks to elasticity and fine-grained

scalable services of cloud computing, there is an almost perfect match between the demand curve

(actual usage) and the capacity curve (on-demand capacity) over time, yielding to an increased

revenue stream and a higher level of customer satisfaction as shown in illustration 7.

To get one step further in this analysis, (Armbrust et al. 2009, pp.3-4) further suggests the no

less, but harder to measure, negative effects of turning away users as a consequence of under-

provisioning resources. As stated rightfully, not only do rejected users generate zero revenues, they

may never come back due to poor service.

Another key observation is that “cloud computing’s ability to add or remove resources at a fine

grain level (e.g., one server at a time with Amazon EC2) and with a lead time of minutes rather than

weeks, allows matching resources to workload much more closely” (Armbrust et al. 2009, p.10).


Illustration 7: Amazon Web Services: Capacity vs. Usage (Graphic Courtesy of Amazon)


This point of view is reinforced by (Rangan et al 2008) and (Siegel 2008) cited in (Armbrust et al.

2009, p.3), who estimate that server utilization rates in traditional datacenters around the world

range from 5% to 20% on average, which is consistent with the observation that for many services

the peak workload exceeds the average workload by factors of 2 to 10 as few organizations would

deliberately provision capacity for less than the expected peak workload. Consequently, computing

resources are mostly in an idle state in conventional datacenters around the world. And, the more

pronounced the variations, the more pronounced the waste.

But do such pronounced peak variations scenarios really occur in the real world? It happens that the

born and bred mythology of cloud computing recounts many emblematic stories illustrating the

case.

“At the end of August [2008], as Hurricane Gustav threatened the coast of Texas, the

Obama campaign called the Red Cross to say it would be routing donations to it via the

Red Cross home page. Get your servers ready—our guys can be pretty nuts, Team

Obama said. Sure, sure, whatever, the Red Cross responded. We’ve been through 9/11,

Katrina, we can handle it. The surge of Obama dollars crashed the Red Cross website

in less than 15 minutes.” (Thomas 2008)

A New-York-based tech start-up, Animoto, lets users create professional-quality, MTV-style

videos using their own images and music. The company averaged 5,000 users a day until it

suddenly received a burst of new users who discovered the service through Facebook. The site

traffic surged to 750,000 visitors in three days. At one point, 25,000 simultaneous users were using

Animoto in one hour. But, thanks to Amazon Web Services, Animoto's application didn't crash

because their source of computing power came from Amazon Web Services (James 2008).

Stories of the Red Cross and Animoto neatly sum up the contrast between the former economy

and the emerging cloud computing economy says (Lasica 2009, pp.3-4). The “beauty” of the

stories, is that at the time of this event, Animoto—a tiny ten-employee startup— jumped from 50 to

3,500 servers in less than three days, and in the end, they paid only for what they used. Armbrust et

al. (2009) commented on that story too and stated that:

“No one could have foreseen that resource needs would suddenly double every 12 hours

for 3 days. After the peak subsided, traffic fell to a lower level. So in this real world



example, scale-up elasticity was not a cost optimization but an operational requirement,

and scale-down elasticity allowed the steady-state expenditure to more closely match

the steady-state workload” (Armbrust et al. 2009, p.4)

This technical prowess would have been unconceivable before what Google CEO Eric Schmidt

has called “The Cloud Computing Age.”

3.3.2 Economies of Scale Benefits

Cloud computing providers make their customers profit from the economies of scale they can

achieve because they buy hardware in bulks of tens of thousands of server and storage devices,

bearing strong bargaining power over computer manufacturers like IBM, Sun or HP. Some

providers like Google even produce their own machines (Sholler & Scott 2008, pp.2-4). The

illustration below represents the quadrant of economies of scale and skill that can be achieved

through cloud computing compared to other ICT outsourcing solutions.

Extremely large-scale, commodity-computer datacenters at low-cost locations has been the key

necessary enabler of cloud computing, for they have uncovered decrease factors of 5 to 7 in the

cost of electricity, network bandwidth, operations, software and hardware. Therefore, carefully


Illustration 8: Economies of scale and skill drive cloud computing (Source: Forrester Research,

Inc.)


choosing the location of a cloud computing datacenter is an extremely important decision as

electricity, cooling labor and property purchase costs, as well as taxes, vary greatly from one

country or region to another. These factors, combined with statistical multiplexing to increase

utilization rates compared to private clouds mean that cloud computing could offer services below

the costs of a medium-sized datacenter and yet still make a good profit. Another necessary, but not

sufficient, condition to become a cloud computing provider is to have existing investments not only

in very large datacenters, but also in large-scale virtual management infrastructures and operational

expertise required to run them and protect them against potential physical and electronic attacks.

Furthermore, the availability of inexpensive electrical power, inexpensive network bandwidth,

and a staggering amount of cash are needed because the up-front fixed costs for these types of

datacenters is enormous. Hundreds of millions of dollars are spent before the first byte can be

consumed. Few companies can afford to start a cloud computing provider business venture

(Armbrust et al. 2009, p.10). But there is also an economies of scope dimension of the cost-savings

argument. Economies of scope are an economic theory stating that the average total cost of

production decreases as a result of increasing the number of different goods produced. A popular

example is the fast-food restaurant which profits more from selling hamburgers and French fries

than from selling hamburgers alone. The reason for this effect is that the restaurant can use its

distribution infrastructure for selling different (related) products more efficiently. Similarly, cloud

computing is an attractive field of economies of scope for companies with a well built ICT

infrastructure.

Amazon, who is the leader in cloud computing infrastructures, is a perfect example of how

economies of scale and scope play in the economics of cloud computing. The company started the

Amazon Web Services business by renting out its idle servers during non-holiday shopping seasons.

In describing the new AWS Spot Instances plan, AWS Chief Werner Vogels demonstrates how the

tremendous growth of the AWS Infrastructure Services takes advantage of economies of scale.

“The broad Amazon EC2 customer base brings such diversity in workload and

utilization patterns that it allows us to operate Amazon EC2 with extreme efficiency.

True to the Amazon philosophy, we let our customers benefit from the economies of

scale they help us create by lowering our prices when we achieve lower cost structures.

Consistently we have lowered compute, storage and bandwidth prices based on such



cost savings.” (Vogels 2009)

But perhaps more importantly, Amazon indicates in “The Economics of the AWS Cloud vs.

Owned ICT Infrastructure” that customers they are in contact with report post-virtualization

utilization rates of a deceptive 20%-25% as the highest utilization ratio they can achieve with

virtualization technology. Whereas with AWS, “customers can achieve a greater overall utilization

of its hardware assets because of its large and heterogeneous customer population. Within that

population exist thousands of workloads, with non-correlating peaks and valleys. As an example, a

financial services firm with peaks at the beginning and end of each trading day will obtain their

utilization offset from an e-commerce firm with a shopping peak in the middle of the day and from

a pharmaceutical company data analysis job running overnight.” (Amazon 2009, p.2)

This refers to a unique characteristic of cloud computing already mentioned in this document

that Armbrust et al. (2009) define as statistical multiplexing. And Amazon to add: “Moreover,

Amazon Elastic Compute Cloud (Amazon EC2) features such as Auto Scaling and Elastic Load

Balancing enable businesses to automatically grow or shrink their usage of AWS based on the actual

performance of their application. In so doing, AWS' customers can minimize their waste of AWS

resources and achieve a utilization rate that truly does approach 100%” (Amazon 2009, p.2).

In a blog post of December 14, 2009, Nicholas Carr posits that Amazon AWS is emerging as the

Chicago Edison, in reference to the electric utility revolution metaphor developed in his book “The

Big Switch” (Carr 2009b). He explains that “the key to running a successful large-scale utility is to

match capacity (i.e, capital) to demand, and the key to matching capacity to demand is to

manipulate demand through pricing. The worst thing for a utility, particularly in the early stages of

its growth, is to have unused capacity.” (Carr 2009a)

“Amazon has left traditional ICT vendors in the dust”, he says, when the company announced in

mid-December 2009 its new EC2 Spot Instances pricing model. With this program, buyers can bid

for unused compute cycles in what is essentially a spot market for virtual computers. When their bid

is higher than the market spot price, their virtual machines start running at the spot price, which is

discounted compared to the normal price. When their bid falls below the spot price, their machines

stop running, and the capacity is reallocated to those customers with higher bids.

“Amazon's spot market promises to significantly reduce the cost of computing tasks that



don't have immediate deadlines, such as large data-mining or other analytical efforts.

And it promises to further increase Amazon's capacity utilization, which will in turn

allow Amazon to continue to reduce its prices, attract more customers, further smooth

demand, and avoid wasted capital.”(Carr 2009a)

As shown in Amazon EC2 pricing, “the expectation of improving performance at lower costs is

illustrated by the cost of reserved instances for Amazon EC2, which offers over 50% savings from

on-demand (hourly) prices. Reserved instances can also be turned off at any time they’re not being

used to avoid usage charges (e.g., to cover costs of cooling, power, etc.). These are costs that

enterprises cannot avoid if they are running datacenters themselves” (Amazon 2009, p.2). Thanks to

the economies of scale that can be achieved with the cloud, Amazon's AWS customers say that they

reap the benefits of decreasing costs, increasing performance, and enhanced functionality over

time .

3.3.3 Collaboration and Community Computing Benefits

As the globalization trend continues, distributed work has become an everyday reality in large

organizations. Many existing on-premises applications were originally designed to support

employees in same-time, same-place working styles. By contrast, cloud-based productivity tools

(for example, Google Apps, Microsoft Office Live Workspace, Intuit’s QuickBase, Facebook) are

inherently collaborative and accessible anywhere, including from home (Accenture n.d., p.8).

Community computing and collaboration in the cloud brings benefits that are not easily attainable

with local computing, such as the detection of distributed denial of service attacks (DdoS) or spams,

as cloud platforms that have a wide visibility on the Internet traffic would detect the onset of an

attack more quickly and accurately than any local threat detector (Wang 2009, p.2).

3.3.4 Environmental Benefits

Rising energy prices and widespread concerns about climate change and traffic congestion are

also major forces driving the adoption of the cloud. According to Gartner, the global ICT industry

generates as much greenhouse gas as the world's airline traffic, which accounts for 2% of global

CO2 emissions, and this number will grow. IDC reports that datacenter power and cooling costs



will rise by 400% by the year 2011 to reach a global spending of over $US30 billion17. Because of

its elasticity and statistical multiplexing properties, the cloud should reduce the global amount of

ICT resources (server, storage, network), and thus reduce the amount of energy needed to run and

cool them. Amazon AWS, for instance, provides business incentives for those enterprises wishing to

take advantage of cheap computing through the EC2 Spot Instances program. This program allows

customers to bid on unused Amazon EC2 capacity and run those instances at a discounted rate for

as long as their bid exceeds the current Spot Price. EC2 Spot Instances is an important innovation

with regard to energy savings because it offers financial incentives to re-architect applications so

that they can effectively be turned on and off. Matthew Wheeland emphasizes that “the simple

business imperative of maximizing profit and minimizing costs is sure to drive cloud providers

toward the most efficient computing practices possible, and the side benefits of energy efficient

computing in a world of carbon limits and climate legislation makes green IT a necessity from a

compliance standpoint as much as an operations standpoint” (Wheeland 2009)

3.3.5 Financial Benefits

Buying ICT capacity or an application (SaaS) delivered as a service from a cloud-based service

provider is an operating expense (OPEX) that can be scaled up and down or even turned off to meet

business need variations over time. The same solution hosted in the corporate datacenter is a sunk

cost that includes a capital expenditure (CAPEX) that must be carried on the balance sheet as an

asset that loses value as it depreciates. Turning capital expenditures into operational expenditures

make financing decision easier as stated also by The Economist in its special report on corporate IT

entitled “Let it Rise”.

“Instead of having to shell out a lot of money for, say a server to test an application

and, even with luck, wait a few weeks for it to be up and running, managers just have to

whip out a credit card, open an account at Amazon Web Services (AWS) and fire up a

virtual machine for a few dollars” (Siegel 2008, p.12)

Another important financial benefit as reported by Amazon is that, in a capital-constrained

context, many enterprises simply don’t have the budget required to either finance new ICT projects,

or extend or replace an aging ICT infrastructure. As a result, many enterprises are simply foregoing

17 See illustration Datacenters World Wide Spending (Source IDC).



important projects due to lack of capital (Amazon 2009, p.4). Finally, in (Schadler 2008, p.4),

Forrester highlights the key financial benefits between CAPEX and OPEX that are discussed below

and summarized in Table 2.

Table 2: Key financial benefits of on-premises ICT versus cloud computing (Source: Forrester

Research, Inc.)

Factor On-premises Cloud Computing

Expenditure Type Capital expenditure (CAPEX)

Operating expense (OPEX)

Operating expenses only

Cash flow 0 Payments are made as the

service is provided.

Financial risk Entire financial risk is taken upfront,

with uncertain return.

Financial risk is taken monthly

and is matched to return.

Income statement Maintenance and depreciated capital

expense

Maintenance expense only

Balance Sheet Software and hardware are carried as

a long-term capital asset.

Nothing appears on the balance

sheet.

3.3.5.1 Cash-flow Friendly

The high cost of running large datacenters in today’s economic downturn continue to put

pressure on overall business spending, drives ongoing ICT rationalization, datacenter consolidation

and outsourcing. The cloud-based pay-as-you-go model for organizations eager to delay, reduce or

eliminate capital spending, especially on one-off or marginal projects, provides an attractive option

because it avoids taking on debt and it “keeps cash in the bank longer” (Schadler 2008, p.4).

Increasingly, companies are also turning to software-as-a-service (SaaS) solutions as a way to

counter the high licensing costs of enterprise software. (Accenture n.d., p.8) and (Lasica 2009, p.50)

3.3.5.2 Lower Financial Risk

A cloud-based solution means that you pay only for what you use, and that you can terminate

the contract. In contrast, on-premises solutions mean spending money upfront for hardware and

software with an uncertain payoff, which means more financial risk if the expected return on



investment does not materialize.

3.3.5.3 Greater Financial Visibility

Buying cloud-based services such as email and calendaring from a SaaS provider is often more

cost-predictable and transparent than getting the same services from corporate ICT. Instead of

paying upfront for hardware, software and consultants to set-up and run office productivity

applications, you pay the SaaS provider per user and per month. This visibility is a comfort to a

CFO who must keep track of where the money is going. Ideally, basic ICT service settings should

deliver this same kind of financial transparency, but more often than not, fail to act in accordance

with CFOs' cost-control expectations because of the hindrance of hidden and indirect costs .

3.3.5.4 Healthier Return on Assets

One of the advantages of cloud computing’s pay-as-you-go pricing model is that the cost is

incurred in the same period as the one in which the value is delivered18. For CFOs, this means that

the balance sheet does not carry an ever-depreciating capital asset of hardware and software that

lowers the important financial metric of Return On Assets (ROA)19.

3.3.6 Increased Business Agility Benefits

Amongst the many ways in which the cloud will affect businesses and change the economy,

businesses agility emerges as perhaps the strongest adoption driver. Information technology

complexity has increased dramatically as applications become more integrated, requiring significant

planning, profiling and testing when making even the smallest changes in the business processes

across the organization. This complexity increases costs and reduces agility in compounding ways

(Lasica 2009, p12). Such an inconvenience contrasts with the observed trend that businesses must

become more adaptable, more interwoven and more specialized (Siegel 2008, p.11). This theme is

18 Costs matching to revenues in the same financial period is a GAAP accounting principle that govern how a company

tracks and reports on its financial health.

19 Return on asset (ROA) is an indicator of how profitable a company is relative to its total assets. ROA gives an idea

as to how efficiently management is at converting its investment into net income. The higher the ROA number, the

better, because the company is earning more money on less investment. The formula for ROA is: Net Income / Total

Assets. The assets of the company are comprised of both debt and equity.



largely developed in a master thesis entitled “Enhancing the Agility Promoting Benefits of Service-

Oriented With Utility Computing” by Adrian Sobotta of the IT University of Copenhagen. In his

thesis, Adrian Sobotta states that cloud computing, in conjunction with a service-oriented

architecture (SOA), improves business agility (Sobotta 2008, pp.47-74). He posits that the

introduction in the 1990s of software called enterprise-resource planning (ERP) lent to the promises

of making companies more agile. But once this massive software packages were in place, after

sometimes many years of struggle to align the actual business processes with the software, it

appeared exceedingly difficult for enterprises to change them as Ludwig Siegel jokes :

“Implementing SAP, the market leader in ERP, is like pouring concrete in your company” (Siegel

2008, p.12). This explains the divide that exists in many firms between the ICT department and the

business units, whose tensions have worsened in recent years. Also, to keep up with an ever-

changing market environment, many enterprises have accumulated a huge backlog of new projects

that the ICT department simply cannot deliver on time because of their inability to react quickly.

(Siegel 2008, p.12)

Because of the increased business agility benefits mentioned above, cloud-based services have

been extremely successful among start-ups, as the Animoto story tells us, which can enjoy quality

infrastructures of the same size as large companies. In fact, AWS is probably the main reason why

there are so many start-ups offering all kinds of “Web 2.0” services. Their usefulness may

sometimes be questionable, but it is a sign of a lively dynamism, an explosion of combinatorial

innovations made possible by the cloud because entrepreneurs can try new combinations of

technologies for very little investments, says Google's Mr Varian in (Siegel 2008, p.12).

Increased business agility drawn from the combinatorial innovations allowed by the cloud

should also be made easier by the fact that the cloud will host a huge collection of electronic

services based on service-oriented architecture standards such as HTTP, XML and SOAP embedded

in Web Services technologies. This should help free the enterprise's business processes from the

harness of the ERP system to be more easily adapted to the task at hand. It will become easier to

outsource business processes, or at least those processes that are not critical to foster competitive

advantages, and focus more on those that are core. This should foster further specialization as firms

will rely more on services provided by others. As a result, firms will increasingly form “process

networks”, a term for loosely connected groupings of specialized firms coined by John Hagel, a



business strategist at the auditing firm Deloitte & Touche in (Siegel 2008, p.12). Countries like

India will certainly take advantage of the global market for cloud services. For instance, Indian

hospitals are already offering specialized healthcare services using Zoho's technology, a popular

suite of web-based applications developed by the Indian company AdventNet. As another example,

the insurance branch of an Indian bank has used Zoho's technology to offer differentiated services

such as a personalized insurance for diabetes, where premiums are adjusted depending on how well

policy holders stick to their fitness programs (Siegel 2008, p.13). All this suggests that the economic

impact of the cloud may be felt not only within the ICT industry itself, but everywhere cloud

services can be substituted to inefficient industries and businesses.

Two collateral aspects of an increased business agility reside in an improved speed of execution

and focus. Cloud-based services accelerate a project rollout because users can start their project

with a prebuilt foundation. Similarly, using cloud-based services can free up ICT staff from the

daily duty of maintaing operations to focus on more gratifying core competencies that drive the

business.

3.3.7 Operational Benefits

A cloud-based infrastructure, with its robust, massively redundant infrastructure, can often

provide better uptime and availability than traditional datacenters. Also, thanks to their

infrastructure management services, cloud platforms provide in general good support for easy

provisioning of resources, automatization of maintenance tasks and consistent upgrades. Using

cloud management services helps launch new ICT projects more quickly and speed up innovation.

3.4 Cloud Computing Costs

The previous chapters outlined the business drivers that push enterprises to move ICT services

to the cloud. In this Section we will focus on total cost of ownership (TCO) differentiators between

using a cloud computing hosted environment versus running an in-house datacenter.

Doing like-for-like comparisons between cloud computing and in-house datacenters to run an

enterprise business application is a difficult task because it is easy to neglect many of the indirect

and hidden costs incurred by operating a datacenter. In fact, there are many arguments and counter-

arguments surrounding the total cost of ownership (TCO) of hosting in-house compared with using



cloud-based services. This is because each organization has its own capital and operational cost

structures and its own break-even point, but IDC in (Fleischer & Eibisch 2007, p.6) argue that most

companies, with relatively standard ICT and Web deployments, will achieve lower TCO by using a

managed hosting service than by hosting in self-owned and managed facilities. However, a simple

comparison of costs for self-owned versus hosted facilities is typically not possible, even for small

companies, due to the large number of indirect and hidden costs20 affecting in-house operations that

are overlooked.

In support of this statement, IDC argues that “too many companies inappropriately compare the

headline costs of in-house operations and managed services when they evaluate the two side-by-

side, such as the capital cost of servers versus monthly recurring fees. The range of costs necessary

to run a decent-quality hosting operation in-house is wider than many companies appreciate, and in-

house cost cutting can be illusory, creating more in risk than it saves in cost.” (Fleischer & Eibisch

2007, p.10)

To help out with this issue, Amazon developed in the “Economics of the AWS cloud vs. Owned

ICT Infrastructure” a comparative analysis of several direct and indirect costs entailed by owning

the facility versus using the AWS cloud that will be used hereafter. In this Section, I will strive to

sum up all the direct and indirect costs that apply to operating a self-owned datacenter and how they

compare to using cloud-based managed services. This outline will be used hereafter as a calculation

basis for TCO of the reference use case.

Operating a self-owned datacenter incurs a number of tangible asset's capital or lease costs and

other landlord fees, as well as personnel costs that broadly divide into three categories:

1. Datacenter facility costs that include: building maintenance and upkeep, fit-out costs,

technical space maintenance and refurbishment, two or more fiber ducts and fiber services to

the building, power plant, backup power generators, fuel storage, chillers, physical security

systems (access control, CCTV, security presence, etc.), fire suppression, racks, cabling, and

so on. To be included are business continuity redundancy for most of these components, and

20 A particular cost may be direct or indirect, depending on the cost object. For example, an IT organization's manager

is an indirect cost for operating a customer relationship management (CRM) service, but it is a direct cost for the IT

department itself. In the first case, the cost object is the CRM service. In the second case, the cost object is the entire

IT depatment.



insurance for all of them.

2. Computing equipments costs that include: depreciation, planned life-cycle replacement,

unplanned replacement, backup/hot swap, spare parts inventory (onsite or with supplier),

power and cooling costs, software licenses, system monitoring, system security (IDS, email

security, DDoS mitigation, etc.)

3. Personnel costs that include: salaries and related overheads of facilities and security staff to

operate the physical datacenter as well as of ICT staff to manage the technical environment;

cover for staff absence; attrition costs; training; staff facilities.

This is only a subset of the costs a company necessarily incurs in operating its own hosting

operations. While many companies, depending on the scale of their operations, make do without

some of these components, they are typically incurring risk in return for the cost saving (for

example, by cutting back on redundancy, or not deploying a DDoS capability, or under-resourcing

the operation in staff terms). A company that uses a managed hosting service will still pay these

costs, but the maim assumption about cloud computing's cost-saving opportunities discussed so far

is that these costs are shared across all customers of the service provider and, through the

economies of scale the hosting provider can achieve, the customer will pay only a fraction of the

amount for the in-house operations equivalent.

3.4.1 Hardware Equipment Costs

In typical enterprises, datacenters represent multi-million dollar investments in ICT

infrastructure. For most, initial large capital outlays as well as the expenses to maintain the

infrastructure with the newest servers, storage and networking technology has become prohibitively

expensive. These costs are typically amortized over a period of three to five years and remain fixed

irrespective of the firm's current activity, and so does the performance of the systems during that

time. Amazon argues that thanks to the economies of scale, AWS customers reap the benefits of

decreasing costs, increasing performance, and enhanced functionality.

Amazon in (Amazon 2009, p.2) also argues that the utilization of hardware equipment is one

key area where customers can benefit from using the cloud. This is because in traditional self-

owned datacenters, the server utilization rate commonly averages around 5% to 20% when

measured annually, which is consistent with McKinsey & Co.'s observations that, on average, server



utilization in a datacenter is 10%. (McKinsey&Company 2009, p.22)

Comparatively, AWS customers, for example, can effectively achieve a utilization rate close to

100% because they are charged only for the resources they actually consume as stated below. Also,

AWS instances can also be completely turned off at any time when they’re not used to avoid other

fixed costs that include cooling and power charges that enterprises cannot avoid when operating

their own datacenter.

3.4.2 Power and Cooling Costs

Inefficiency in datacenters severely limits the enterprise's ability to support increasing power

and cooling requirements. This in turn, inhibits the ability to deploy new applications, and hence to

drive business growth. The datacenter inefficiency issue combined with the increasing cost of

power and the shift in responsibility of ICT power expense towards the ICT manager, is creating

serious incentives for companies to either invest heavily in ongoing efficiency efforts to decrease

their power usage effectiveness (PUE)21 ratio or look at alternative ways of hosting and managing

their ICT infrastructure. Amazon argues that serious energy-efficiency efforts require dedicating

ICT and engineering resources, using the most efficient equipment, and adhering to industry best

practices, which often is not feasible for enterprises because to justify such investments they need

to operate on large scale with a large number of servers across multiple datacenters. In summary,

reducing the amount of hardware components needed to run applications on the company's

datacenter and replacing them with cloud-based computing systems reduces the constraints

associated with the energy needed to run and cool the machines.

3.4.3 Reliability and Availability Costs

Alongside cost, IDC reports in (Fleischer & Eibisch 2007, p.7) that the biggest issue they

repeatedly see from surveys, focus groups and conversations with end-user organizations relates to

service continuity issues, namely reliability and uptime of network and hosted services. Businesses

looking for highly reliable and available ICT services need not only to maintain reliable storage and

backup devices, but also to operate a reliable network with redundant routes between datacenters

and office facilities. In addition, they should also have tested a working solution for disaster

21 See “Energy Efficiency Measurement Metrics” Section.



recovery. This includes deploying data and applications across multiple remote datacenters and to

provision for extra capacity in case of disaster recovery failover. Therefore, enterprises need to

account for both the cost and complexity of such highly reliable ICT infrastructure deployments,

which generally happen to be a most expensive endeavor. Companies often report surprisingly low

levels of reliability for both self-managed, in-house services and even services that they buy from a

hosting provider. Organizations that run their own operation on a shoestring budget or that buy a

service from the cheapest provider (with the cheapest infrastructure and fewest resources) are

usually those that experience reliability problems. Some large, well-funded providers have

surprisingly poor reliability records too, hence the need to choose a service provider carefully. In

contrast, cloud computing solutions such as Amazon's AWS include all these capabilities in its

simple usage charges, allowing customers to deploy servers in multiple independent zones, which

cannot fail simultaneously for the same physical causes such as power or cooling failures, fire and

so forth. For example, data stored in amazon S3, Amazon SimpleDB, or Amazon Elastic Block

Store is redundantly stored in multiple physical locations as part of normal operation of its hosted

services. Furthermore, high-end cloud computing solutions such as AWS, Google AppEngine and

others, carry out failover capabilities that can be leveraged at no charge or for an optional fee.

3.4.4 Data Security and Privacy Costs

Enforcing data security and privacy is another important source of cost for enterprises hosting

Internet applications. This entails ensuring the confidentiality and integrity of sensitive data and and

user privacy. Examples of security costs for enterprises include capital expenditures for network

security devices (i.e. firewalls), security software licenses, staffing of security specialists, physical

security devices such as smart cards for access control, and so on. Amazon argues in (Amazon

2009) that AWS customers take advantage of Amazon's long experience and best practices to secure

end-to-end transactions and data privacy in a secure global computing infrastructure at no additional

cost to the customer. It is indeed true that AWS customers benefit from the physical security of

Amazon's datacenters, such as video surveillance, intrusion detection systems, two-factor

authentication procedures, and nondescript locations of those facilities; it is however still the

responsibility of the customer to handle its data security and privacy requirements at the operating

system and applications levels. Another interesting point of view raised by Forrester in (Wang 2009,

p.2) stems from the fact that collaboration and community computing, which is what cloud



computing does best, brings security benefits that are not attainable with local computation only. An

example is cloud-based threat services, such as distributed denial of service attacks (DDoS) or spam

detection. A cloud service provider that has a wide visibility of the Internet traffic would see the

onset of an attack more quickly and accurately than any local threat detector.

3.4.5 Regulatory Compliance Costs

Some industries, notably finance and manufacturing, have faced a steep hike in their regulatory

burden in recent years, driven by reasons as diverse as corporate corruption and fraud, terrorism,

environmental issues and consumer protection. Examples include SOX, HIPPA, FISMA, Basel II,

IFRS, WEEE and RoHS. Each new directive brings its own challenge, hence costs for ICT

departments and infrastructure, typically centered around storage, redundancy, supply chain and, in

the case of the environment, ICT efficiency as we have seen above, where the EU, for example, is

currently discussing regulating power consumption of a number of equipment categories, including

initial power supplies, cooling equipment and PCs.

3.4.6 Supply Chain Management Costs

Supply chain is another area where cloud customers can save on costs. It is fairly common for

enterprises to build a cushion inventory of hardware for the datacenter. This is due to the fact that a

typical lead for hardware procurement, such as a server, can take several months. Such long lead

times necessitate having excess capacity that spreads throughout the pipeline and increases costs.

Amazon in (Amazon 2009, p.3) argues that AWS minimizes this excess capacity by devoting

significant resources to managing its supply chain more efficiently than any traditional enterprise's

datacenter could ever achieve.

3.4.7 Maintenance and Administration Costs

IDC shows in (Fleischer & Eibisch 2007, pp8-10) that despite consolidation and virtualization

efforts that started a few years ago, the rapid growth of the server's installed base required to

support application growth within the organization has resulted in highly complex and difficult to

manage datacenters. Still, according to IDC's power and cooling research study in 2006, the cost of

simple maintenance and administration now represents 67% of total datacenter operational



expenditure as this ratio is directly related to the number of physical servers installed. Furthermore,

as the installed base of servers increases, the proportion of ICT budget consumed by simply keeping

systems up and running will continue to increase.

As a result, we can safely assume that cloud computing should facilitate savings on

maintenance and administration costs because of the sizable ICT department team that is needed to

handle such diverse tasks as managing heterogeneous hardware and procurement, staying up-to-date

on datacenter design, negotiating contracts, dealing with legacy software, operating datacenters,

moving facilities, scaling and managing physical growth, and so forth, to make the best of the

infrastructure costs. Cloud computing cuts on these costs, even though they are included in the

simple pay-as-you-go charges, because they are amortized over a much larger customer base and

hardware base that even large enterprises do not have.

3.4.8 Opportunity Costs

Perhaps the biggest incentive gained from using the AWS cloud, as stated by Amazon, for

enterprises of all sizes, comes out of the opportunity cost of owning, operating and maintaining an

on-premises datacenter. It is well-known that running large-scale, high-availability infrastructures

requires large funding, the effort of many talented staff members and the dedicated attention of

upper-level management. The opportunity cost of a datacenter represents the value of the next best

benefits forgone if the enterprise had focused and innovated directly in its core businesses rather

than investing in an on-premises ICT infrastructure.

However, to fully get the economies of scale effects, cloud computing providers must buy

hardware in bulks of thousands of machines, storage and networking equipment, creating a negative

cash flow condition that must be discounted for the opportunity cost of capital and the risk involved

that customers shall pay back one way or another.

3.4.9 Conclusion

I think that for most organizations, outsourcing to the cloud should reduce risks and hence costs.

The cost elements outlined above all present risk as well as cost to an organization in terms of

service disruption resulting in lost orders. Many companies operate internal ICT SLAs, but in the

face of a major disruption affecting operations, internal SLAs are effectively worthless. An SLA



from an external service provider would typically not cover the cost of lost business or customer

dissatisfaction, but can go some way to mitigating the financial impact. More significantly, if

stringent enough, the disruption should act as a major incentive for the service provider to fix

problems quickly and well. A strong SLA does not nullify risks, but will reduce the financial impact

by ensuring quick problem resolution and a level of loss buffering.

3.5 Cloud Computing Risks

William Coleman, founder of Cassatt Corporation, said in (Lasica 2009, p.32) that “for the cloud

to become all-pervasive, we must figure out how to overcome barriers around trust, reliability,

control and security”. In their current state, most (if not all) cloud platforms lack standardization

and face many challenges relating to data security, ownership and integrity issues, as well as

liability constraints, which are perceived as rapid adoption inhibitors (Smith, Cearley et al. 2009).

Gartner also reports in (Natis et al. 2009) and in (Robertson 2009) that most enterprises are looking

for ways to leverage cloud computing services, but many either fail to understand risks or over-

estimate the risks involved. Similarly, Forrester in (Staten 2008) estimates that cloud computing

does not yet meet enterprise requirements for similar reasons. The goal of this Section is to review

what those inhibitors are and to describe the extent to which they can impact our cost-benefits

analysis.

Security in the cloud and its associated risks is a hot topic which has received a broad coverage

in countless dedicated conferences and analyst reports. This Section will cover in detail what those

risks are and how they have been formally highlighted by Forrester and Gartner in several research

reports. For example, in 2009, Gartner conducted a virtual off-site conference, whose results are

consigned in (Robertson 2009), to get a consensus among analysts about what they think are the

top-five inhibitors to cloud computing adoption. I will also address the points -of-view of ICT

security experts and practitioners that have been put forward in conferences I had the opportunity to

attend. The conclusion will strive to objectively extract a rational position between those opinions

that are either over-emphasizing the risks entailed by externalizing ICT services to the cloud, or

underestimating the risks. Finally, I will propose a risk mitigation approach for the reference use

case that should be taken into account as bill of material items in the cost-benefits analysis.



3.5.1 Questioning the Cost-effectiveness of Cloud Computing

Before delving into a detailed description of the risks surrounding cloud computing, I should

report that the very cost benefit generally agreed upon about cloud computing is being challenged in

a direct way. In the controversial report “Clearing the air on cloud computing”,

(McKinsey&Company 2009) argues that cloud-based service offerings currently on the market are

most attractive to small and medium-sized businesses, but much less attractive to large enterprises

as there are significant adoption inhibitors, among which cost takes a forefront position. In the same

report, (McKinsey&Company 2009) also states that current cloud-based service offerings are not

cost-effective in the context of large firms because of the additional cost incurred by security and

reliability risk mitigation plans, the re-architecturing of applications to meet business continuity

requirements of large enterprises, and the adaptation of the ICT supply chain to function in a cloud-

centric environment. This Section focuses on the cost-effectiveness controversy about cloud

computing with simple yet concrete cost-of-material metrics, followed by a detailed description of

various cloud computing adoption inhibitors reported by a number of analysts, including Gartner,

that have contributed quite a lot to describing the business risks associated with cloud computing.

William Forrest, principal analyst at McKinsey and author of the report, argues that current

cloud computing services are generally not cost-effective for larger enterprises, which explains why

most cloud customers are small businesses. Has ensued a series of articles by Forbes in (Greenberg

2009), The Wall Street Journal, and The New York Times in (Lohr 2009), who wrote articles

quoting a McKinsey report relaying the idea that clouds are not cost-effective. The report cites an

example of one of McKinsey's financial services clients that migrated a Windows server farm to

Amazon Elastic Compute Cloud (EC2) and compares the total cost of ownership with that of a

regular datacenter. According to McKinsey, the total cost of the datacenter functions would cost

$US366 a month per unit of computing output, compared to $US150 a month for the conventional

datacenter. That is, more than twice as much as it would cost a large enterprise to own the same

computing resources in-house (McKinsey&Company 2009). Here is how the calculation goes.

At the time of the report writing, they estimated the typical CPU per month cost for 3 GHz dual-

core Xeon Windows-based servers to be $US150 ($US43 for non-labor-related costs and $US107

for labor-related ones). The report states that the cost of a comparable configuration in EC2 would

cost $US366 ($US270 for large standard Windows configurations and $US93 for non-labor-related



costs). The example assumes 10% labor savings by moving to a third-party cloud provider. The total

is $US216 per month more expensive to move a large Windows environment to EC2 versus running

it in-house. So, the financial firm in question would end-up paying more for all but a few Linux

servers, says William Forrest. In fact, when adding up the total information technology resources

actually used by the financial services company and multiplying the figure by the cost of those

resources in Amazon's EC2, Forrest found that the financial services company would be shelling out

nearly 150% more in a cloud computing model.

William Forrest contends that much of cloud computing's misplaced hype comes from the

assumption that businesses that make the switch to the cloud will be able to cut away the entire

personnel cost of the ICT department. As such, in his analysis of McKinsey's financial services

firm, William Forrest found that only around 15% of the company's 1,700 ICT employees had

hands-on access to hardware and software, while most employees worked in support or other

administrative tasks. Which meant that moving to Amazon's service would only cut about 200 full-

time workers, hardly the savings chief information officers might imagine. The labor savings from

moving to the cloud model have been greatly exaggerated, Forrest said. He concludes in (Lohr

2009) that owning the hardware is actually cost-effective for most corporations when the

depreciation write-offs for tax purposes are included..

In reaction to this report, a spokesperson for Amazon responded that the prospect of moving the

datacenters lock, stock and barrel to EC2 is ridiculous as AWS is not designed for that purpose. In

the same line of thought, James Staten, a principal analyst at Forrester Research, writes in (Maitland

2009) that “hosting a persistent service on a pay-per-use platform is not the best economic decision

that an enterprise big or small could make, [...] but adding virtual machines on the fly during

predictable peak traffic periods is a good use of a cloud computing platform”.

There is a problem, in my opinion, with the main thesis of McKinsey. First, the report assumes a

fairly static and constant server usage overtime. That is rarely the case in large real-world business

applications. Second, the report neglects to mention the reserved instance option of Amazon EC2,

which can greatly reduce costs when a predictable usage pattern can be planned ahead of time.

Third, it overlooks the numerous cost-effective benefits, described hereafter, that can be drawn from

the increased flexibility of scaling up and down servers on demand, support high availability and

disaster recovery scenarios at a fraction of the price it costs when doing it oneself. But assuming all



things being equal, in terms of labor costs and other more indirect costs, it is worthwhile to verify

the claims of McKinsey in a direct cost comparison between AWS EC222 and other forms of hosting

solutions such as dedicated server hosting23 and virtual private server24 (VPS) hosting. For the sake

of argument, I have taken into consideration only the cost of computing, storage and networking

between AWS EC2 and a handle of good reputation firms. Besides, there are a number of reasons

why one should exercise some caution when comparing AWS EC2 with pure-play VPS hosting

providers. Unlike AWS, EC2 providers who offer VPS hosting plans do not provide a clear

description of the performance characteristics of their hosting plans. For instance, an EC2 Compute

Unit provides the equivalent of 1.0-1.2 GHz 2007 Opteron processor capacity, whereas a VPS

instance would only guarantee an equal share of CPU usage across all the VPS instances running on

the same physical server. Obviously, this information is not very useful when comparing

performance unless you know how much VPS instances are typically running on one physical

server25. The same issue applies to disk and network I/O performance since all those resources are

also shared across all VPS instances. In addition, many hosting providers use commodity hardware

instead of enterprise-class hardware featuring overall low performance and little redundancy. It is

also common practice for VPS hosting providers to oversell; that is, creating as many VPS instance

onto the same physical hardware as possible, and praying that they do not all use their resources at

the same time. Finally, hardware configurations offered by AWS EC226 for standard, high-memory

22 Infrastructure as a Service services offered by cloud vendors like Amazon, RackSpace, GoGrid and others.

23 According to Wikipedia, a dedicated hosting service, dedicated server, or managed hosting service is a type of

Internet hosting in which the client leases an entire server not shared with anyone. Organizations have full control

over the server(s), including choice of operating system, hardware, etc. Server administration can usually be

provided by the hosting company as an add-on service. Dedicated servers are most often housed in datacenters,

similar to colocation facilities, providing redundant power sources and HVAC systems.

24 According to Wikipedia, a Virtual Private Server (VPS) sometimes referred to as a Virtual Dedicated Server (VDS)

is a method of partitioning a physical server machine into multiple servers that each have the appearance and

capabilities of running on their own dedicated machine. Each virtual server, or VPS host environment can run its

own full-fledged operating system, and each server can be independently rebooted. The most common virtualization

technics used in VPS are based on hypervisor technology, which main examples include Virtuozzo, Hyper-V,

VMWare and Xen. Depending on the virtualization product and licensing being used VPS pricing may vary.

25 This information is generally not communicated. Slicehost, a renown Linux VPS hosting provider, is a good

example of that ambiguity.

26 See Appendix I: Amazon Web Services: Amazon EC2 Instance Characteristics



and high-CPU plans are in the higher range compared to typical VPS configurations offered by

most hosting providers. For all these reasons, I have chosen to also include pricing for dedicated

hosting. A dedicated server plan is similar in some respects to a VPS plan. But for a monthly fee the

enterprise has complete control over the entire server instead of sharing it with numerous other

organizations. The full disk space, bandwidth and processor speed are yours for the taking, which is

also true with owning your own server. The difference is that you lease a dedicated server and all

the equipment remains at the host’s datacenter. This is also true to a certain extent with Amazon

EC2 which sells its hosting services on the notion of leasing virtualized servers with a guaranteed

amount of standard compute units, memory and disk space. Although EC2 instances are running in

a shared environment, the instance types purport to guarantee a certain level of compute

performance measured in virtual CPU units a.k.a Compute Units.

Table 3 proposes a quick cost comparison between AWS EC2 and other dedicated and VPS

outsourcing solutions from renown hosting providers.

Table 3: AWS EC2 Quick Costs Comparison

Vendor AWS EC2 Superb

Hosting

GoGrid RackSpace Colo

Crossing

Plan Standard Large Dual Processor

Servers

Standard

Dedicated

Server

Basic Two

Platform Xen VM Dedicated Dedicated Dedicated Dedicated

CPU core Quad Core 1-

1.2 2007

Opteron or

Xeon 64-bit

2 x Dual Core

Intel Xeon

5130 2.0 GHz

Quad Core Quad Core

AMD Opteron

Intel

Core2Quad

Q6600 2.4Ghz

RAM 7.5 GB 2 GB 8 GB 4 GB 4 GB

HDD 850 GB 320 GB 640 GB 500 GB 250 GB

Bandwidth 3500 GB 4000 GB unknown 2 TB 3500 GB

OS Windows

Server 2003 or

2008

Windows

Server 2003 or

2008 Standard

Windows

Server 2003 or

2008 Enterprise

Windows

Server 2003 or

2008

Windows

Server 2003 or

2008 Standard



Vendor AWS EC2 Superb

Hosting

GoGrid RackSpace Colo

Crossing

Including

support and

backup

Yes and no.

Some form of

support is

provided by the

community

yes yes yes yes

Additional

cost

100 GB EBS

storage27 fully

reliable

Annual Rate

(Monthly)

$US 71028 $US 259 $US 230

Requires

upfront

prepayment for

the year

$US 509 $US 222

When you add up all the costs of an Amazon EC2 Standard Large instance including EBS

storage and network traffic, the monthly bill that Amazon will send to its customer is around

$US710. A hefty sum when compared to Rackspace's $US509 a month for the most expensive

dedicated server offering we have in Table 3. Besides, the Basic Two plan from Rackspace boasts

more network bandwidth, more disk storage and probably more CPU capacity than the equivalent

EC2 Standard Large virtual machine. Of course, this amount assumes that you use all the EBS

storage and network bandwidth. This is were EC2 pricing differs most from other hosting provider

27 Amazon Elastic Block Store (EBS) offers persistent storage for Amazon EC2 instances. With Amazon Elastic Block

Store, enterprises pay for what they use. Volume storage is charged by the amount allocated until it is released, and

is priced at a rate of $0.11 per allocated GB per month in Europe. Amazon EBS also charges $0.11 per 1 million I/O

requests you make to your volume. As an example, a medium sized website database might be 100 GB in size and

expect to average 100 I/Os per second over the course of a month. This would translate to $11 per month in storage

cost (100 GB x $0.11/month), and approximately $28 per month in I/O requests cost (~260 million requests/month *

$0.11 per million I/O).

28 Windows Reserved Large Instance / month = $251

3500 GB Internet traffic / month (3500 * $0.12 per GB in and out avg.) = $420

100 GB EBS storage including I/O requests / month = $39



offerings. With Amazon, the inbound and outbound Internet traffic is not included in the EC2

instance's basic pricing. It comes as an additional pay-per-use cost that must be added to the

instance cost, whereas in the case of dedicated servers or VPS offerings it is generally included as

long as you stay within the plan's allowed maximum bandwidth. If you exceed that maximum then

you are charged a premium for each extra GB transfered. Pricing a Windows-based reserved

instance at $US710 a month brings EC2 way above the higher-end markup of the cost comparison

Table, tagging a 70% premium compared to the most expensive dedicated server hosting offering

from Rackspace priced at $US222 a month.

But doing a cost comparison of Amazon EC2 offerings with other hosting providers would not

be complete without running the numbers again against Slicehost. This US-based hosting company,

recently acquired by Rackspace, is a VPS hosting provider widely used within the developer

community for its attractive price, performance and ease of use. A Slicehost “slice” is their name for

a VPS instance. Like with EC2, a Slicehost virtual machine provides full access to the system that

allows a user to configure the Linux operating system and applications he/she wants on top of the

virtual machine. A user can subscribe from a number of slices. There are seven of them in total and

their names pertain to how much memory they have. For example, a 256 slice gets 256 MB, a 512

slice gets 512 MB, and so forth, up to the 15.5 GB slice that gets 15.5 GB of memory. Also, the

bigger the size of the slice, the more CPU, storage and network bandwidth it gets. More specifically,

the size of a slice represents the actual priority assigned to the virtual machine on the physical

server host under load. In other words, if you rent the biggest 15.5 GB slice, you are practically

assured to get exclusive access over the physical server resources29. If you rent an 8 GB slice, you

are splitting in half the resources of that machine with another 8 GB slice or more. If you rent a

1 GB slice, you get 1/16th of the machine resources under load and so on. Slicehost prices the 4 GB

slice at $US250 a month including 160 GB of storage and 2500 GB of network bandwidth. The

closest comparable EC2 offering to a 4 GB Slicehost slice is the Standard Reserved Large EC2

Instance running on the Linux operating system. Doing the same calculation as above, using

different storage and bandwidth variables to match the 4 GB slice hosting plan, we arrive at a price

29 A 15.5 GB slice should be hosted on height-core processor machine with 16GB of memory (500 MB reserved for

internal usage). Seems to be the way VPS are provisioned at Slicehost although this information is backed from

unofficial blogs and forum sources.



of $US538 a month for the AWS EC2 Large Instance30. That is, more than twice the price of a 4 GB

slice from Slicehost.

There are four observations we can make out of this direct cost-focused comparison.

1. Despite some recent consolidation, server hosting has been a highly fragmented market for

many years. This may be good for companies seeking the cheapest deal, but makes it

extremely difficult to compare a basket of offerings on a like-for-like basis in order to find a

high-quality hosting provider.

2. Performing a cost comparison in a like-for-like manner is very difficult, if not impossible,

due to the fact that each hosting solution is different with regard to the hardware

configuration and its resulting performances (i.e. CPU speed, RAM and HDD capacity). But

it is also due to the additional services that are bundled with the product offering. For

instance, most dedicated hosting products include some form of corporate support and

backup service, while in the case of AWS EC2, standard support is provided through a

community forum or as an optional add-on or Amazon's partners like RightScale.

3. Taken from a direct outsourcing cost point-of-view, and assuming a 24 hours a day, 7 days a

week, 365 days a year usage pattern, AWS EC2 is not cost-effective at all compared to other

hosting solutions such as VPS hosting and even dedicated server hosting. But this sort of

comparison based solely on direct costs leaves out many of the cloud computing economic

benefits, such as those supported by AWS, that revolve around the pay-per-use pricing

model and elasticity enablement.

4. Complex distributed applications imply a deployment cost that goes way beyond the sole

server hosting cost in that it requires a scalable and fault tolerant architecture that is

generally not supported by hosting providers out-of-the-box. Also, what one gets with cloud

computing that one does not get with classical hosting solutions is an infinite compute

power that you pay on a fine-grained usage basis (as opposed to renting a server for a month

or more), the ability to scale up and down automatically, and pre-built service continuity

features. For example, EBS provides RAID-level reliable persistent storage for EC2

30 Linux Server Reserved Large Instance / month = $193

2500 GB Internet traffic / month (2500 * $0.12 per GB in and out avg.) = $300

160 GB EBS storage including I/O requests / month (assuming an average 100 I/Os per second) = $45



instances that can be backed up to multiple datacenters. cloudFront (another AWS service)

even brings the ability to have static files served from 12 different locations in the world,

making your latency very small. If you need more EC2 instances of (almost) any size, you

simply need to wait a few minutes to get one to boot. If you need more bandwidth, it's

automatic. If you need more storage, S3 and EBS can always give you more so that you

could have terabyte after terabyte of storage potentially distributed across multiple

datacenters for performance and/or disaster-recovery purposes. EC2 provides tools to build

failure-resilient applications and isolate themselves from common failure scenarios like with

Elastic Load Balancing, which automatically detects unhealthy instances and distributes or

reroutes traffic to healthy instances until the unhealthy instances have been restored. These

types of features are simply not available in a classical hosted environment. It will cost a

large amount of money for facility, power, cooling and maintenance, and a hefty lump sum

to have it built in-house. And also, because it is a one-off project, the ICT department will

almost certainly subcontract the job to a system integrator, which brings another bucket of

consulting costs, risks and all sorts of troubles. All this has to be taken into account when

doing a total cost of ownership (TCO) calculation.

Therefore, there is no simple answer to the question about whether cloud computing is cost-

effective compared to on-premises ICT infrastructures without incorporating in the cost and benefit

analysis equation an evaluation of the positive business impact of the technology, the accrued

business flexibility and the potential associated risks given the enterprise's resources and processes

context in which a project is implemented.

3.5.2 Risk-testing Risks

This risk stands from the belief that it is hard to tell if a cloud service provider is doing all due

diligence to mitigating other key risks such as those listed below. This is difficult to evaluate

because of the lack of information made readily available by providers. Gartner put forward that

“more-mature cloud service providers are now improving their ability to show how they operate

behind the cloud to reassure customers — particularly about security risks.”. (Robertson 2009, p.2)

3.5.3 Data Location Risks



An issue arising from the storage resource abstraction paradigm in public clouds, is that you

may not know in which location your data is stored. It may be in any disk farm, datacenter or even

region or country. The problem with this storage resource abstraction is that many enterprises have

to comply with national or even supranational laws that require some level of control over where

your sensitive data resides. In Switzerland, for example, financial firms and banks are required by

law to retain financial data within the country. In the US, the Patriot Act may allow the U.S.

government access to any information stored within its borders, which may infringe data privacy

laws and scare non-resident citizens. For example, the way in which Google stores, uses and

transfers data across its massive mesh of interconnected datacenters means that users cannot know

(and that there are no guarantees) that their data that Google or any other cloud provider has, is

stored within its country of origin. That is putting the firm that uses Google's services into a

collision course with regulators and privacy advocates. This is especially true in Europe, where the

EU privacy directives31 do not allow companies to "process" any personal data outside their country

of origin or outside the EU. (Logan 2009, p.3)

3.5.4 Data and Code Portability Risks

Once data has been put into a system, particularly a full application system offered as a cloud

service or SaaS, it can be difficult to get it back out. Besides the data itself, it may be hard to get

the process (and the code that executes that process) out of the provider's system.

3.5.5 Data Loss Risks

Gartner argues that there have been some isolated cases of providers either suffering a complete

technical meltdown or going out of business. Both forms of failure may result in non-recoverable

data losses. (Robertson 2009, p.2)

3.5.6 Data Security and Privacy Risks

It is almost conventional wisdom to agree that security and privacy represent a strong barrier-to-

31 The main regulation is the EU Council Directive 95/46, which protects "personal data," that is any in which a person

is identifiable. The directive prohibits the "processing" of that data under all but the most necessary circumstances

and does not allow its free movement outside the EU. In the directive, processing is defined very broadly, and would

encompass anything and everything you might do with data, including move it to a cloud provider



entry that are as much road blocks for organizations considering adopting cloud services. Forrester

in (Wang 2009) synthesized interviews of a dozen vendors and ICT users about cloud computing

security issues, which reveal that data security and privacy, compliance, as well as legal and

contractual risks are top of mind. The pith of these risks are detailed in the following sections.

Concerns such as data protection, operational integrity, vulnerability management, business

continuity, disaster recovery, and identity access management (IAM), make up the list of security

issues for cloud computing (Wang 2009). For example, Gartner cites concerns in (Smith, Cearley et

al. 2009) and (Robertson 2009) that potential intruders could get access to an enterprise's data

more easily when it is hosted externally rather than internally. Hence the concern that externally

hosted data is more vulnerable to confidentiality and integrity attacks than that protected behind an

organization's firewall. Privacy is another key concern whereby data that the service collects about

the end-user (e.g. profile and preferences) gives the provider valuable marketing information, but

can also lead to misuse and violation of privacy (Wang 2009, p.3). Thus, the cloud model has been

criticized by privacy advocates for the greater ease in which the companies hosting cloud services

can control and monitor at will, lawfully or unlawfully, the communication and data transfered

between the end-user and the hosting provider (Robertson 2009, p.3). Again, the EU's security and

privacy laws have established regulations—that must be enforced by the member states—that

should deter such practices.32 From that respect, Google, like Microsoft before it, is a cloud vendor

most likely to attract the scrutiny of the regulators inside and outside the United States as Google's

main business is based on collecting personal information about what individuals do on the Web

and retain that information for commercial purposes in advertising through Google's AdSense33 and

AdWords34 programs. These practices may have serious legal implications—especially under the

EU's directive 95/4635—in countries who protect any personal identifiable data from secondary use.

32 The EU Council Directive 95/46 on data protection, which went into effect in October 1998, is an omnibus law

regulating the commercial use of personal information. The directive requires EU member states to enact their own

privacy laws based on the principles laid out in the EU's directive and to establish independent government agencies

to regulate privacy laws. There have been regulatory efforts between the US and the EU to harmonize their legal

environment about data security and privacy regulations through the Safe Harbor Program.

33 https://www.google.com/support/adsense/bin/answer.py?hl=en&answer=9712

34 http://adwords.google.com/support/aw/bin/answer.py?hl=en&answer=6084

35 The EU 95/46 directive prohibits the processing of personal data under all but the most necessary circumstances and

does not allow its free movement outside the EU. Under this directive, processing is defined very broadly, and



The problem when it comes to cloud computing is that one way for customers to evaluate a

provider’s security and privacy practices is through auditing, which can help to lend some visibility

into the vendor’s ability to cope with the security and privacy concerns of its customers. However,

auditing often goes against the very essence of cloud computing, which attempts to abstract away

the operational details by providing easy-to-use interfaces and APIs. A cloud provider would not

allow internal audits, and when they offer provisions for some form of external audits, in their

infrastructure and network, it is often delivered in an ad-hoc and proprietary manner, which does

not integrate well with the customer's auditing system (Wang 2009, p.3).

3.5.7 Compliance Risks

Users who have compliance requirements need to understand whether, and how, utilizing cloud

services might impact their compliance objectives. Data privacy and business continuity are two big

items for compliance. Privacy compliance applies to the enterprise's legal obligations towards data

location, data processing and data retention periods or requests for information from potential

litigants and regulators. However, there are other regulatory compliance risks that candidates to the

cloud should carefully examine. For example, HIPAA regulations prohibit the co-location of a

pharmaceutical company's data with data from another company on a shared server. Public

companies that outsource datacenter responsibilities still have to manage operations in order to

obtain compliance with regulations such as the Federal Information Security Management Act

(FISMA), the Health Insurance Portability and Accountability Act (HIPAA), the Sarbanes-Oxley

Act (SOX) in the US, the Federal Financial Institutions Examination Council (FFIEC), Basel II, and

the credit card industry's PCI Data Security Standard (PCI DSS). There are also a number of

specific regulations regarding the storage of electronic communications such as the SEC Rule 17a

3/4 that has a significant impact on how financial services companies save and store all electronic

communications. Regulations which pertain to electronically stored information (ESI) require that

the ESI be kept, that it be protected and that it be produced when and if a regulator or potential

litigator asks for it (Logan 2009, p.4). To make some compliance objectives easier to fulfill, some

providers propose community or hybrid cloud deployment models. Also, certain providers, such as

Amazon, Google and Microsoft, have obtained a SAS 7036 Type II certification but even those

would encompass anything and everything you might do with data, including move it to a cloud provider.

36 Sarbanes-Oxley Act (SOX) certification: The American Institute of Certified Public Accountants developed the



certifications have been criticized on the ground that the chosen set of goals and standards

determined by the auditor and the audited are often not disclosed and can vary widely from one

provider to another.

3.5.8 Legal and Contractual Risks

Liability and intellectual property are just a few of the legal risks that an enterprise candidate to

cloud computing should consider. Forrester argues in (Wang 2009) that liability is not always clear-

cut when it comes to cloud services. The same goes for intellectual property (IP). For some services,

the IP issue is well understood — the SaaS provider owns the infrastructure and the applications,

while the user owns the data and computational results. In other cases, the division is not quite so

clear. In PaaS, where the provider owns the infrastructure and the middleware applications, it can be

difficult to delineate who owns what and what rights the customer has over the provider. Forrester

concludes that “it is therefore imperative that liability and IP issues are settled before the service

commences. Other contractual issues include end-of-service support —when the provider-customer

relationship ends, customer data and applications should be packaged and delivered to the customer,

and any remaining copies of customer data should be erased from the provider’s infrastructure.”

(Wang 2009, p.4)

3.5.9 Vendor Viability Risks

Statement on Auditing Standards (SAS) No. 70. Organizations that successfully complete a SAS 70 audit have been

through an in-depth audit of their control activities, including controls over IT and related processes. SAS 70 allows

a company to provide a third-party certification of its internal controls to customers. To obtain that certification,

SAS 70 datacenters have to maintain prescribed levels of data security and redundancy, as well as personnel

controls. These requirements include reporting on the following:

• Firewall configuration and access

• Database access

• Data transmissions

• Data backup and recovery

• Application security

• Product development

In addition, datacenter staff cannot access servers or data without a specific procedure. All access and activity is logged.

And all physical access is highly controlled.



Finally, Gartner believes that vendor viability represents a significant level of risk. Of course,

this may not happen to an Amazon, Google or Salesforce.com, but the economic downturn may

prove fatal to the viability of smaller vendors. Consider that some vendors may fail and completely

go out of business as it has been the case in the days of the Internet bubble. Even large vendors

might decide to stop offering a particular cloud computing service. Note also that the procurement

chain of providers may hide some viability risk, as some service providers may leverage others that

in turn could go out of business (Robertson 2009, p.3).

3.5.10 Vendor Lock-in Risks

Even if the cloud is likely to transform the ICT industry, fundamental rationale will stay the

same. One is about the importance of vendor lock-in. All things being equal, companies and

developers will become more dependent on cloud platforms and online services than they are on

traditional software as ICT organizations will surrender parts of their prerogatives and

responsibilities to service vendors. For instance, firms wishing to migrate a SaaS application back

into an on-premises datacenter will no longer have the required expertise or may be unable to do so

because of the proprietary adherences of the application with the SaaS platform. Some are already

calling for a “cloud computing Consortium”, in the mould of the World Wide Web Consortium

(W3C), to set interoperability standards that allow applications to migrate easily from one platform

to another.

3.5.11 Conclusion

Cloud computing is still at an early stage. Therefore, to a significant extent, its technological and

business models are as yet unproven. Cloud computing is not necessarily for everyone, nor for any

type of application. It is probable though that data security and privacy compliance concerns will

prevent a rapid adoption of public cloud solutions in heavy regulated industries, and in many global

companies that operate in multiple jurisdictions as stated by Gartner in (Logan 2009). That is why,

a company considering moving applications to the cloud must be conscious of their security

policies and regulation compliance constraints. Beyond that, I believe that issues around data

security and privacy risks in the cloud have been overly emphasized. It is also the opinion of several



field experts, who talked at the Kuppinger Cole37 and Cloud Slam 201038 virtual conferences on

cloud computing and security I had the opportunity to attend.

There seems to be a consensus around the idea that cloud computing is not inherently insecure

or even less secure than traditional ICT. The cloud way may even be more secure than many poorly

managed information systems where traditional ICT is incapable of providing the same level of

expertise and control on their production systems for reasons as diverse as insufficient staff, limited

budget for training and hiring top-of-the-line security experts. As a matter of fact, internal ICT

teams can hardly compete with the budget and level of expertise carried out by the big cloud

computing vendors to effectively secure their infrastructure from a physical and logical standpoint.

In addition, it should be well understood that companies are always responsible —irrespective of

whether their data resides in the cloud or not—vis a vis their legal obligations. Therefore, what a

company needs to determine is whether or not it can protect, produce and consume sensitive data in

the cloud with the same level of security and regulatory compliance as it does internally. Companies

wishing to use cloud-based services should ascertain that their provider can meet their requirements

and, if so, at what costs if any. Meeting security and compliance requirements can be onerous and

expensive for both parties. Litigious relations are often a direct result of not properly addressing the

responsibilities of all parties in the contract. Therefore, any hosting business relationships should

clearly state what jurisdiction applies to the hosting contract. Cloud hosting providers should honor

the security and compliance requirements of their customers, and provide transparent answers to

inquiries around those questions.

It should be clearly stated that the responsibility to deal lawfully with corporate data, whether it

be in the cloud or not, is not the responsibility of the cloud provider. It is always the responsibility

of the company to protect the data it produces, no matter where data is located. In other words, the

processes used to deal with the legal complexity of managing data should not be different in the

cloud than in a self-owned datacenter. A company must know what it is doing in the cloud by first

creating its security and regulatory compliance processes internally, and then ensuring that they can

be carried equally by the provider or themselves to the cloud.

Finally, CEOs and CIOs need to understand that cloud computing requires new policies and new

37 http://www.kuppingercole.com/webinars

38 http://cloudslam10.com/



controls because it may give rise to new ICT risks that can have an operational and even strategic

impact on the enterprise's efficiency and effectiveness. Adopting cloud computing to externalize

computing resources poses the question of ascertaining opportunities versus operational and

strategic risks.



4 Cloud Computing Relationships with Green IT

In Chapter 3 “Cloud Computing”, I tried to describe what cloud computing is by going through

a definition of the concept and by describing its expected business benefits and the potential risks

entailed by making the strategic decision to move IT services to the cloud. In Section 4.4 “Green IT

and Cloud Computing”, I will try to shed more light on the environmental benefits of cloud

computing, already introduced in Section 3.3.4, by digging further into the greening attributes of the

cloud, and evaluate to what extent the technology can contribute to a reduction of CO2 emissions in

the ICT sector. Then, in Chapter 6 “Financial Analysis”, I will address the question of how the

environmental benefits of cloud computing apply to our case study and how they can be quantified

in terms of energy policy compliance risks and business operations benefits.

Perhaps, I should begin the discussion about cloud computing and green IT relationships with

the statement that connection between the two concepts is not well established, and as far as cloud

computing goes their is a lot “buzz”, confusion, and soon to come, disillusion. An article by Tom

Jowitt in NetworkWorld illustrates this situation with the findings of Rackspace's Green Survey of

2009 that discloses skepticism about the green benefits of cloud computing, and demonstrates that

cost savings and datacenter consolidation are current issues driving the green IT agenda (Jowitt

2009).

It is reported in the survey that cost savings are proving to be the biggest driver of decisions

made about environmentally responsible ICT managers and that companies are still concerned with

green initiatives, and are continuing on the track to sustaining and improving their environmentally

friendly policies (Rackspace 2009). But in the 2009 edition of the Green Survey39, Rackspace added

a new question about whether its customers view cloud computing as a greener alternative to

traditional computing infrastructure. The result was rather deceptive indicating that only 21%

percent agreed that cloud computing was a much greener alternative, against 35% percent that were

not convinced on its green benefits, as illustrated in the illustrations below.

39 A poll among 1,507 randomly selected Rackspace customers around the globe.



Furthermore, only 7% of Rackspace's customers think that cloud computing is critical to their

company to become greener, and 46% say that cloud computing is not part of their overall

environmental strategy.

Instead, the survey indicates that they are relying on more traditional green initiatives. Seventy-

one percent have undertaken or are focusing on recycling; 31% on datacenter consolidation; 29% on


Illustration 10: Rackspace customer views on how cloud computing fit into their environmental

initiatives (Graphic courtesy of Rackspace)

Illustration 9: Rackspace customer views on cloud computing as a greener alternative to traditional

computing infrastructures? (Graphic courtesy of Rackspace)


transportation (car pooling and travel restrictions); 10% on renewable energy; 10% on carbon

footprint; and 2% on LEED certification (Rackspace 2009).

In order to better understand why a sizable proportion of Rackspace's customers have the

perception that cloud computing is not critical for their company to become greener and that it is

not part of their environmental strategy, we need to enter with some detail into what is intended

behind the concept of green IT.

4.1 What Is Green IT?

Green IT is an umbrella term that Forrester defines in (Washburn & Mines 2009, p.3) as “IT

suppliers and their customers reducing the harmful environmental impacts of computing.”.

Forrester claims in (Washburn & Mines 2009, pp.3-4) that achieving green IT objectives in an

organization implies five types of core activities.

1. Energy efficiency and management. Energy efficiency and management involves

reducing energy expenses resulting from the sprawling of server and storage farms,

intensive datacenter cooling, and distributed IT assets such as PCs and printers. It is, for

most organizations, the major driving force towards Green IT. Energy efficiency can be

improved by provisioning more efficient brands of PCs, monitors, power supplies,

servers and cooling equipment. Environmental-aware procurement organizations are

leaning towards equipment that complies with energy-efficient standards like Energy

Star and Electronic, while Product Environment Assessment Tool (EPEAT) helps make

the right purchasing decisions. Energy management, in turn, leans towards energy

conservation by powering-down IT assets when not in use or by using renewable sources

of energy.

2. Equipment and resource reduction. Reducing the IT equipment footprint by

decommissioning and consolidating underutilized equipment reduces energy

consumption and proactively curb electronic waste, or e-waste.

3. Life-cycle and e-waste management. To limit the fast growing proliferation of

hazardous materials such as cadmium, lead, and mercury, CIOs should buy less ICT

equipment, use less, lengthen the life cycles of ICT assets, and ensure the responsible

reuse, recycling, and disposal of IT assets at their end of life.



4. Support for green corporate initiatives. The new corporate initiatives trend is

prompting CIOs to better understand how to provide ICT infrastructure, applications,

and expertise to improve the sustainability of business processes and operations outside

of ICT, such as support for telecommuting and teleconferencing, paperless billing,

building automation, and enterprise wide carbon and energy management.

5. Governance and reporting. Governance and reporting is viewed as an important

process element to green IT, including setting goals, documenting policies, capturing

best practices, and reporting progress.

4.1.1 Quick Overview

The green IT agenda is broadening beyond the datacenter, as the enterprise as a whole is

plagued by other significant energy consumption inefficiencies found in distributed assets such as

PCs, monitors, printers and phones, which together typically consume more energy than the

datacenter itself40. Also, while the greening of IT assets remains fundamental, executive

management will spur more ambitious and strategic objectives by addressing the sustainability of

the enterprise operations outside of IT. Ultimately, this ought to bring greater impact to the

environment. The “SMART 2020: Enabling the low carbon economy in the information age” report

from the Global e-Sustainability Initiative (GeSI)—further discussed in the Section entitled “Cloud

Computing as a Green IT Strategy”—was the foundational event that broadened the concept of

green IT for the enterprise (Washburn & Mines 2009, p.5). To emphasize the expansion of the

concept, the green IT community has used the Web metaphor of green IT 1.0 and green IT 2.0.

• Green IT 1.0. is referred to as “Green for IT”. It aims to reduce the environmental

impact of technologies within the datacenter, such as servers, storage, network, cooling

and power distribution, and of IT infrastructure outside the datacenter, such as PCs,

monitors, printers and phone systems.

• Green IT 2.0. is referred to as “IT for Green”. It aims to reduce the environmental

impact across the overall business value chain through the use of IT services and

expertise such as videoconferencing, building automation systems, supply-chain

40 According to a Forrester survey of more than 300 IT practitioners, the datacenter, on average, consumes 45% of total

IT energy consumption, while 55% is consumed outside of the datacenter (Washburn 2008b)(Washburn 2008b)



optimization and carbon and energy management software.

Forrester reports—from customer surveys conducted in 200941— that while 85% of the

respondent IT leaders said that environmental concerns are “very important” or “important” to them

in planning their company's IT operations, their journey to greener IT is just beginning. Forrester

argues that:

IT leaders are increasingly involved, pursuing energy efficiency and other green

initiatives in their IT infrastructure and across all IT operations and teams. But for most

companies, the journey toward a greener IT organization is not well-mapped. Many

CIOs are aware of the green imperatives for their organization and, in some cases,

aware of the benefits of green IT. However, most do not yet have a well-documented

baseline or plan for reducing environmental impacts and delivering on financial and

business objectives. (Washburn & Mines 2009, p.2)

In practice, Forrester observed that there is a gap between the recognition of the green

imperative and the pace at which plans are being executed to reduce environmental impacts.

According to corporate sustainability initiative observers, it is the economics of green IT that drive

enterprises to give consideration to the environment, meaning that green IT is stimulated primarily

by the concern of cutting costs (Washburn 2008a).

Forrester in (Mines 2009, pp.2-4) gives some insights from Global Green IT Online Surveys, as

to why the effective implementation of green IT action plans are lagging behind. To the question:

“Does your company have a comprehensive plan for implementing green IT practices and

technologies” only 28% of the respondents said that they were actually implementing it in

November 2009. The illustration 11 provides a historical graph of respondents' answers to the

above question in November 2009, April 2009, October 2008, April 2008 and October 2007,

showing a moderate, but nonetheless steady, progression of plan implementations since October

2007.

41 Forrester's April 2009 Global Green IT Online Survey received responses from more than 300 IT professionals.

Forrester's November 2009 survey of enterprise green practices received responses from more than 900 IT

professionals in leadership, operations and architecture design, which represented responses from 602 companies

based in Asia, US, Europe and the rest of the world and 9 different activity sectors.



The illustration 12 provides the respondents' answers to the question “if you do not have a

comprehensive plan for implementing green IT, why not?”

The lack of concrete green IT action plan is explained by other competing priorities that are

“top-of-the-mind” concerns followed by no clear ownership and uncertain ROI of green IT.


Illustration 11: More enterprises are implementing green IT action plans but actual implementation

is still lagging (Graphic courtesy of Forrester Research, Inc.)

Illustration 12: Barriers to Green IT Adoption (Graphic courtesy of Forrester Research, Inc)


Nonetheless, executive managers leading green IT initiatives see payoff benefits in several

ways, from reducing costs to complying with regulation to generating revenue growth with greener

products and services.

4.1.2 Green IT Motivations

Forrester's Global Green IT Online Survey for Q4 2009 presented in (Mines 2009) outlines the

organization's top motivations for pursuing greener IT operations from November 2009, April 2009,

October 2008, April 2008, and October 2007. This is detailed in illustration 13.



It is apparent from (Washburn & Mines 2009, p.4) that the top motivation for pursuing greener

IT is financial, in particular, to reduce the energy-related operating expenses of IT (69% of

respondents cite this as their primary driver). The second most popular motivator for green IT, cited

by 40% of companies, is to reduce other IT operating expenses, such as hardware maintenance

costs, software license fees and staffing. Other understated, although more strategic, rationales for

pursuing green IT include aligning with a corporate initiative (34%), improving the firm’s brand

image (26%), and complying with regulation (15%). The “Do the right thing for the environment”

motivation, cited by 50% of companies in October 2007, retreated to 34% in November 2009,

raising the question whether green IT actually exists as an environmental awareness driving force in

its own right in the manager's mind.


Illustration 13: Organizations’ top motivations for pursuing greener IT operations (Graphic

courtesy of Forrester Research, Inc)


4.1.3 Energy Policies and Implications

At the turn of the 21st century, the current political and social climate calls for limiting the

emission of greenhouse gases (GHG)—in particular carbon dioxide (CO2)—in the atmosphere to

mitigate the disastrous effects of global warming. The European Union (EU) has already

implemented an Emission Trading Scheme (ETS) that regulates the emission of greenhouse gases

for the energy sector, and several heavy energy consuming industries (EUC 2003). This scheme

regulates the emissions of greenhouse gases for EU industries during the period 2008-2012 thereby

contributing to reach the EU's reduction target of 8 pct by 2012 using 1990's levels as a baseline in

accordance with the Kyoto protocol. Furthermore, on January 2008, the European Commission put

forward a Climate and Energy proposal package, which aims to deliver on the European Union's

ambitious commitments to fight climate changes and set the target of increasing the share of

renewable energy in use to 20% by 2020 (EUC 2008).

As part of the Climate and Energy package it was suggested that non-ETS sectors, such as

household, transport, agriculture—and more importantly in our case—the service sectors, should

contribute to the reduction of greenhouse gas emissions. Every member state of the EU has been

given fair-share targets both for general emissions reductions and for emissions reductions in non-

ETS sectors [EUC, 2008a].

Most companies consumers of information technology fall into the service sector, and therefore,

should be induced to inflect growth of GHG attributable to the use of computer systems.

Industry analysts such as IDC in (Fleischer & Eibisch 2007) expect that green considerations in

the enterprise will be driven by legislation. Today, legislation is limited to materials (ROHS) and

recycling (WEEE) with little in place regarding the operational aspect of ICT. However, discussions

are ongoing both within the EU and non-governmental organizations (NGOs), and companies

should give consideration to this issue today to minimize future impacts. For example, many

countries in the EU and the rest of the world, as well as several US States, have enacted or are

considering environmental laws that apply to ICT products and services regulating power

consumption of a number of equipment categories, including initially power supplies, cooling

equipment and PCs. As such, six countries within the EU have met the 2007 deadline to comply

with the European Union's Energy-using Products Directive, which encourages manufacturers to

design products with their environmental impact and energy conservation in mind throughout their



entire life cycle. Similarly, California’s Global Warming Solutions Act of 2006 has set an official

timetable for reducing the effect of greenhouse gases emissions to year 2000 levels by year 2010.

Tomorrow, european firms of the service sector may be constrained by the regulatory body of

the EU to abide to CO2 emission restrictions by law as opposed to the actual self-regulatory policy.

For example, governments are starting to look into this with labels such as Energy Star, which is a

prerequisite for responding to a public sector request for proposal (RFP). In addition, non-

governmental organizations such as the WWF and Greenpeace are taking lead in this area as we will

see in the “Cloud Computing As A Green Strategy” section below. As stated by IDC, these

initiatives “will result in both industry standard metrics for measuring data centre efficiency and

potential heavy legislation to meter efficiency at both the server and data centre level.” (Fleischer &

Eibisch 2007, p.3)

Research undertaken for The Green Grid42 consortium has identified a wide range of current and

future energy policy responses that are of importance to the ICT industry sectors. “The Green Grid

Energy Policy Research for Data Centres” research paper by CB Richard Ellis (CBRE) summarizes

the key policies identified and applicable to France, Germany, the Netherlands and the United

Kingdom as of November 2, 2009 in (Van der Perre et al. 2009, p.10)43.

Table 4: Key Energy Policies Applicable To The Case Study (Source: The Green Grid)

Policies France Germany Holland UK

Energy Performance of Buildings Directive & Rev. ✓ ✓ ✓ ✓Energy Labeling Directive & Rev. ✓ ✓ ✓ ✓Eco-Design Directive & Expansion ✓ ✓ ✓ ✓Datacenter Environmental Certification ✓ ✓ ✓ ✓Energy and Environmental Product Labels ✓ ✓ ✓ ✓EU Code of Conduct: Energy Efficiency for Data Centres ✓ ✓ ✓ ✓The Fluorinated Greenhouse Gases (F Gas) ✓ ✓ ✓ ✓Feed-In Tariffs ✓ ✓ ✓ ✓Tax Reductions ✓ ✓Grants & Funding ✓ ✓

42 http://www.thegreengrid.org/

43 To be noted that CO2 emissions are particularly important in the UK, the Netherlands and Germany. They are less so

in France, where electricity supplies include higher proportions of nuclear power, approximately 39%, and

hydroelectricity, approximately 5%.



Policies France Germany Holland UK

Carbon Trading ✓Increasing Buildings Codes ✓ ✓ ✓

Evolving Planning Policies ✓

Voluntary Monitoring & Reporting Initiatives ✓ ✓ ✓ ✓

The following sections will examine in more depth the implications of energy policies that are

solely applicable to France and relevant to our case study.

4.1.3.1 Energy Performance of Buildings Directive & Revisions (EPBD)

The EU Energy Performance of Buildings Directive (2002/91/EC) introduced several legal

requirements for non-domestic buildings regarding energy efficiency. Since December 2006, an

Energy Performance Certificate (EPC) that shows a building's theoretical energy efficiency must be

issued at the completion of the building, sale or lease. To date, the EPBD has had minor

implications for the datacenter sector. But in November 2008, the European Commission (EC)

proposed a revision to the EPBD that proposals entails more serious implications for the datacenter

sector, according to CB Richard Ellis in (Van der Perre et al. 2009, p.10), including:

• potentially very significant impacts on the design of datacenters in terms of the recent

amendments regarding net zero carbon buildings by 2019. The extent to which this

policy will apply to datacenters (within buildings or standalone) and their CO2 emissions

is unclear and requires clarification in due course.

• more transparency in the real-estate market for tenants, investors and property owners.

• likely offering of financial incentives at the design and refurbishment stage, although no

details are as yet available.

4.1.3.2 The European Code Of Conduct On Datacenter Energy Efficiency

The European Commission’s (EC) Code of Conduct on Data Centres Energy Efficiency, version

1.0 released in October 2008, is often referred to as the new European Union Code of Conduct,

a.k.a the EU CoC. The aims and objectives of the EU CoC is to minimize the energy consumption

of datacenters to help reduce CO2 emissions as a way to mitigate the effects of global warming.



The European Code of Conduct is a “multipurpose” document, allowing different

stakeholders to commit to improve efficiency in their own areas of competence. The

primary target of this Code of Conduct is the data centre owner / operator, who is

encouraged to commit to undertake and implement energy efficient solutions in existing

or new data centres, whilst respecting the life cycle cost effectiveness and the

performance availability of the system (EUC 2008, p.2)

The EU CoC is destined primarily to datacenter owners and operators who may become

participants, and secondly to the supply chain and service providers who may become endorsers.

• A participant operates one or more datacenters or equipment within datacenters and

commits to energy reporting and implementation of certain practices from the Best

Practice Guide. In order to maintain the credibility of the EU CoC, some minimum

eligibility criteria are applied and these criteria will continue to be developed. Although

being a participant does not entail legally binding obligations, the status of the

participant requires strong commitment and a substantial contribution to the objectives

of the EU CoC.

• Other bodies involved with data centre operators may become endorsers by committing

to support the code and participants through the development of products, information,

services, education or other programs.

All participants have the obligation to continuously monitor energy consumption and adopt

energy management in order to look for continuous improvement in energy efficiency. One of the

key objectives of the EU CoC is that participants produce evidence of continuous improvement in

efficiency.

The EU CoC provides participants and endorsers with essential energy efficiency and technical

guidance. According to (Van der Perre et al. 2009) and (Bertoldi 2009), the key implications and

benefits of the EU CoC are:

• little additional time associated with employing and advocating the guide.

• costs incurred by implementing the accepted CoC Action Plan according to the agreed

timetable.



• opportunity to increase relationships with clients and therefore enhance reputation.

• opportunity for the industry to self-regulate and reduce the risk of future legislation.

• participants will receive public recognition for their efforts, through the Code of Conduct

promotion campaign, aimed at raising public awareness on energy issues.

• participants that score a low energy for the datacenter will be allowed to indicate that

they are CoC low energy champions and will be eligible for awards and other

promotional activities.

In France, Business&Decision, and its Grenoble-based subsidiary Eolas, are first leveraging the

potential business benefits of the EU CoC in the datacenter hosting sector with the Greenethiquette

label.44 Basically, the label entails a charter that commits both sides of a cloud hosting business

relationship to agree on a code of conduct, which covers among other things the obligation for the

service provider to share PUE data with its customers, and undertake improvement measures under

the EU CoC policy and Energy Star rating for datacenter infrastructure.

4.1.3.3 Carbon Trading

Carbon trading is about putting a price on carbon. The Green Grid reports in (Van der Perre et

al. 2009) that in January 2005, the European Union Greenhouse Gas Emission Trading Scheme (EU

ETS) commenced operation as the largest multi-country, multi-sector greenhouse gas emissions

trading scheme, which operates under the Kyoto protocol worldwide umbrella of GHG trading

schemes. To date, the ICT and datacenter sectors have not been included in the EU ETS as the

scheme covers only large combustion installations such as power generation grids, cement, glass,

ceramics, the steel industry, and so forth, with the aviation industry entering in 2013. Operators of

installations that are covered by the EU ETS are obliged to monitor and report emissions of

greenhouse gases from that installation and to surrender allowances equivalent to those GHG

emissions.

However, given that it is estimated that the ICT sector is responsible for as much CO2 emission

as the airline industry, managers would be wise to keep an eye on this scheme.

44 http://www.greenethiquette.com/25-about-greenethiquette.htm



4.1.3.4 The Grenelle of the Environment

The Grenelle of the Environment is the forum of French environmentalists, business

representatives and trade unions convened by French President Nicolas Sarkozy, whose aim is to

reach an agreement on ways of combating climate change. Its conclusions were published in

October 2007. In December 2007, President Nicolas Sarkozy spoke about the Grenelle of the

Environment agreement explaining that France will take a new approach to environmental

protection, thereby confirming the desire to reduce GHGs and increase renewable energy, but not at

the expense of business and commerce, through taxation.

Since 2007, a working group known as the “Développement Eco-responsable et TIC” (DETIC)

has been focusing on options and proposals for reducing energy consumption and reusing heat

produced by datacenters (Petit 2009). They delivered their report on September 8, 2009 to Christine

Lagarde—Minister of the Economy, Industry, and Labor—and Christian Estrosi—Minister in

charge of the Industry. The report proposes a certain number of regulatory measures aiming at

reinforcing the role of the ICT sectors in the area of sustainable development. The number and the

quality of the proposals to contribute to the DETIC effort led to the creation of three workshops,

among which, the second workshop—Architectures, Réseaux & Centres de Données (i.e.

datacenter)—focused specifically on the fast-growing datacenter sector, which when combined with

computer equipment represents 13% of the electricity consumption in France (and growing 10% a

year). The datacenter workshop issued 20 recommendations for the promotion and the development

of greener and innovative datacenters in France. They also raised the strategic imperative to achieve

a computing grid independence vis-a-vis the rest of the world at the national and european levels. I

will only cover here the recommendations that are of interest for our use case and which can

potentially apply to the TEI analysis.

• Tax incentives should be granted to organizations investing in eco-efficient datacenters

that are following a governmental-approved certification process with the involvement

of an accredited governmental office such as the Ademe, or which already comply with a

national or european eco-efficient certification process such as the EU CoC.

• Help through the granting of tax deductibles—similar to those granted to individuals

investing in renewable energies for home heating equipment—enterprise investments

aiming to improve the energy efficiency of the datacenter and, in the longer term, those



enterprises that have the lowest datacenter carbon footprint.

4.2 Energy Efficiency Measurement Metrics

According to the The Green Grid45 consortium, only 30% of a typical datacenter’s energy

consumption goes to powering its IT equipment, with the biggest share going to chillers (33%),

computer room air conditioners (CRACs) (9%), and humidifiers (3%) (Avelar et al. 2008). To unify

the efficiency measurement of the datacenter and improve its' performance per-watt, the Green Grid

proposes two specific metrics known as the Power Usage Effectiveness (PUE) and Datacenter

Infrastructure Efficiency (DciE) (Rawson et al. 2008)

• PUE = Total Facility Power/IT Equipment Power (1)

• DciE = 1/PUE = IT Equipment Power/Total Facility Power x 100% (2)

For equations 1 and 2, the Total Facility Power is defined as the power measured at the utility

meter that is dedicated solely to the datacenter power. The IT Equipment Power is defined as the

equipment that is used to manage, process, store or route data within the datacenter.

In other words, the PUE is a measure of how much energy entering the datacenter goes to

computing instead of non-computing elements like heating, ventilating and air conditioning

(HVAC) or lighting. For example, a PUE of 2.5—equivalent to a DciE of 40%—means that for

every Watt of power that is delivered to server, storage and network, 1.5 Watts is consumed in

overhead. As we will see below, both Microsoft's and Google's cloud infrastructures have extremely

efficient large-scale datacenters aiming at an industry-leading PUE of 1.12.

The DCiE metric and its reciprocal PUE are becoming a de facto standard for measuring the

efficiency of the power and cooling infrastructure in a datacenter. Governmental activities in the

U.S.A. and European Union suggest a high probability that they will also become the “official”

standards such as stated in reporting requirements of the EU CoC.

It should be noted that as of today, there are no normalized or standard metrics and processes to

measure and report the carbon footprint impact of the datacenter's use of power as well as the

carbon footprint impact derived from the manufacturing and recycling of the electronic equipment

45 The Green Grid is an association of IT professionals seeking to dramatically raise the energy efficiency of

datacenters through a series of short- and long-term proposals.



composing it.

4.3 Cloud Computing as an IT Efficiency Strategy

The Economist nails down, in a special report on corporate IT entitled “Let IT Rise”, the

ascertainment of the current inefficiency state of datacenters worldwide. The Economist claims that

7,000 home-grown designed datacenters in North America alone are notoriously known for their

inefficiency and, relays McKinsey and the Uptime Institute findings, that on average, only 6% of

server capacity is used. Of even more concern is the assumption that nearly 30% of the servers are

no longer in use at all in these datacenters, but no one bothers to remove them. It is claimed that

often nobody knows which application runs on which server, and so the method used to find out is

to “pull the plug and see how calls”(Siegel 2008, p.3). For years, ICT departments kept adding

machines when new applications were needed, which over the years led to a situation known as

server sprawl. The illustration below shows the worldwide spending of datacenters since 1996 with

a projected increase estimated to $250 billion by 2011.

Prior to the economic down-turn of 2009, adding servers was not too much of an issue because

entry-level servers were cheap and ever-rising electricity bills were generally charged to the

company's facilities budget rather than to the ICT department's budget. But as stated by IDC, this is

changing.


Illustration 14: Datacenter Worldwide Spending (Graphic Courtesy of IDC)


“Doubts over future energy security continue to drive up the cost of electricity, to the

point where companies can spend as much on powering a server over a three-and-a-

half-year life cycle as on purchasing the server in the first place, [...] and in an

increasing number of cases the cost of power is being transitioned to the IT manager

[...] In some cases, the cost of power could equal, or even exceed, an organisation's IT

budget.” (Fleischer & Eibisch 2007, p.3)

Cloud computing as an energy-efficient outsourcing solution deservers some attention. As such,

Fleischer and Eibisch (2007) with IDC discuss the business incentives for ICT outsourcing from an

increased datacenter efficiency perspective. They report that in 2007 around 50% of companies

were still hosting their Web sites and e-business infrastructure internally and that this trend has been

consistent over recent years. However, they believe that many companies that do in-house hosting

are underestimating the total costs involved in doing so, due mainly to the rising costs of power and

cooling and the gradual shift of costs such as power from facilities departments to ICT

organizations. This claim is substantiated by a survey conducted by IDC in 2006 showing that 13%

of companies' total datacenter operational expenditure went on electricity and that respondents

expected that proportion to increase to 20% within a year. They say:

“In most cases, these high proportions are caused by inefficient server deployments.

For example, the use of industry-standard servers deployed to handle peak traffic loads,

and often in pairs for redundancy, has resulted in system utilization rates as low as

10%. This inefficiency from servers sitting idle is creating unnecessary cost, as well as

avoidable power consumption and carbon emissions.” (Fleischer & Eibisch 2007, p.2)

VMWare, a major virtualization software vendor, makes similar observations when it says that

“a server on standby draws 30% of its peak power consumption for doing nothing!” (Speedfire et al.

n.d.).

In addition,

“Although energy costs typically comprise less than 10% of an overall ICT budget, in a

few years they could rise to more than 50% according to a 2006 Gartner report. Many

large organizations - such as Google - already claim that their annual energy costs

exceed their server costs.” (GreenIT n.d.)



In times of cost-cutting, where companies are striving to reduce fixed costs not directly related

to their core businesses, the concern of datacenter inefficiency becomes more stringent. For this

reason, IDC believes that many datacenters will be modernized and consolidated, but the cost of

modernizing and refitting existing facilities is extremely high and will have a major impact on

overall ICT budgets that is beyond the reach of many organizations, including primarily SMBs.

Because of that, many enterprises will need to consider fitting-out new datacenter facilities in the

near future. A new and more efficient datacenter that consumes less power is a greener datacenter

and even more so if further consideration is given to sourcing renewable power, geographical

location or reuse of the generated heat, as examples. Considering the source of power generation is

also very important as it is possible to reduce a datacenter's power consumption, while still seeing

an increase in carbon footprint, if the power source is switched from, say, nuclear to coal.

Numerous studies, including the one conducted by Greenspace46, an Illinois-based vendor of

green building supplies, support the claim that cloud customers can save billions of kW-hours in

energy consumption, and so, foster the idea that cloud computing is greener than traditional

datacenters because providers are able to squeeze the performance and efficiency of their

infrastructures at much higher levels of compute resource utilization than individual companies,

especially small firms with fewer ICT resources. But whether Cloud Computing is a green

technology or not is a totally different question, as we will see below.

4.4 Cloud Computing as a Green IT Strategy

Capitalizing on the advance in power of microprocessors and data storage capacity, firms like

Amazon and Google are beginning to build massive and highly efficient information processing

infrastructures that use the broadband Internet to reach customers. In 2008, Google was said to be

operating a global network of about three dozen datacenters around the world loaded with more

than 2 millions servers, although this information may be incomplete as Google is very secretive

about the location of its datacenters. According to Google’s earnings reports, the company has spent

$US1.9 billion on datacenters in 2006, and $US2.4 billion in 2007. Google unveiled four new

datacenter projects in 2007. Each has a cost estimate of $US600 million, which will include

46 http://www.greenbiz.com/news/2009/07/15/cloud-computing-highlighted-emissions-reduction-

strategy#ixzz0my89bcgf



everything from construction to equipment and computers.47 Both Microsoft and Google have

extremely efficient large-scale datacenters; both companies are aiming for an industry-leading PUE

of 1.12 in their computing centers (Wheeland 2009). Expanding the use of these services means

more incentive to concentrate ICT operations on top-of-the-line facilities, and will continue the

shift.

To exemplify the above, an article published in June 2006 by The New York Times (Markoff &

Hansell 2006), unveiled Google's project to build the largest and most sophisticated datacenter on

the planet near a small town on the banks of the Columbia River, named The Dalles, in North

Oregon. Today, the site features three 68,680 square foot windowless warehouses designed to host

hundreds of thousands of computers all working together as a single machine to deliver content

over the Internet. A kind of information-processing “dynamo” of unprecedented power, comparable

to a nuclear power plant for generating electricity, as stated in (Carr 2009b). Since then, The Dalles

has become a symbol for the datacenter industry’s growing need for massive amounts of electric

power. In its March issue, Harper magazine publishes in (Strand 2008) one Section of the official

blueprints of the site plan estimating roughly that once all three server buildings will be operational

in 2011, the plant can be expected to demand about 103 megawatts of electricity—enough to power

82,000 homes. The Web, the magazine says, "is no ethereal store of ideas, shimmering over our

heads like the aurora borealis. It is a new heavy industry, an energy glutton that is only growing

hungrier." (Strand 2008).

Google is not alone. Microsoft is also investing billions of dollars in very large computing grids,

such as its datacenter in Northlake, a suburb of Chicago, which covering 500,000 square feet

(46,000 square meters) and costing $US500 million, is one of the biggest, most expansive and

sophisticated datacenter on the planet. The entire first floor is designed to be crammed with 200 40-

foot (13 meter) each containers, loaded with up to 2,500 servers. To support Northlake's datacenter

electricity needs, Microsoft has created three electricity substations that can distribute up to 200

megawatts, that is, as much as a small aluminum melter. Other Internet giants like Yahoo! are also

busy building large server farms. In 2008, half a dozen were being built in Quincy in the middle of

the Washington state close to the Columbia River. Other massive datacenters are being built in the

UK too. For example, Rackspace has built a large datacenter on Slough Estates that will run on

renewable energy and will use low-power equipment such as AMD's Opteron processor and HP's c-

47 http://royal.pingdom.com/2008/04/11/map-of-all-google-data-center-locations/



Class blade servers. The company has partnered with organizations such as NativeEnergy and the

International Tree Foundation in the UK to enable carbon-neutral operations through offset

programs (Fleischer & Eibisch 2007, p.10)

Neither Amazon, Google and other major providers would officially comment on their

datacenters' efficiency levels. However, they argue that thanks to their large customer base, they can

make large investments in efficiency innovations, which smaller firms cannot achieve on their own,

leading to a continuous maximization of their infrastructure that ultimately benefits both parties. It

is commonly reported that a typical PUE for a cloud-based infrastructure is around 1.2 and below,

whereas the average datacenter PUE is 2.5 (Wheeland 2009). Furthermore, we see through

initiatives like the EC2 Spot Instances program that maximizing the utilization rate of the datacenter

is of primary concern since the worst thing for a cloud provider is having to maintain an inventory

of unused capacity.

Furthermore, cloud computing practices promote worker mobility, reducing the need for office

space, buying new furniture, disposing of old furniture, having the office cleaned with chemicals

and trash disposed of, and so on. They also reduce the need for driving to work and the resulting

carbon dioxide emissions.

But while the environmental energy efficiency benefits of cloud computing are generally not

contested, all the discussions about cloud computing being an effective strategy toward green IT

actually miss the point, according to an inflammatory report released by Greenpeace in March 2010.

This report, "Make IT Green: Cloud Computing and its Contribution to Climate Change," updates

and extends some of the research published in 2008 in the Smart 202048 report on how IT

contributes to climate change, and finds that the Year of the Cloud is only going to make things

worse (Wheeland 2010) and (Greenpeace 2010).

The concern Greenpeace expresses in this report is that despite an increasing focus on PUE, and

despite efforts to constantly make computing facilities more efficient, cloud computing is never

going to make enough of a dent in greenhouse gas emissions without the involvement of

48 The Climate Group and the Global e-Sustainability Initiative (GeSI) issued SMART 2020: enabling the low carbon

economy in the information age. The study highlighted the significant and rapidly growing footprint of the ICT

industry and predicted that because of the rapid economic expansion in places like India and China, among other

causes, demand for ICT services will quadruple by 2020.



constraining national and supranational regulations. This is because, despite the fact that some

providers are reaching extremely low PUEs and are also looking to build their datacenters in places

so as to maximize energy efficiency and harness renewable or clean energy49, “it is still a tiny slice

of the pie of both new and existing datacenters, and the ones that are not using renewable energy or

free cooling are the biggest part of the problem” (Wheeland 2010).

Greenpeace alleges in this report that while cloud computing companies are pursuing design and

enforcing strategies to reduce the energy consumption of their datacenters, their primary motivation

is cost containment, and that the environmental benefits of green datacenter design are generally of

secondary importance. Increasing the energy efficiency of its servers and reducing the energy

footprint of the infrastructure of datacenters are a must do, but efficiency by itself is not green if you

are simply working to maximize output from the cheapest and dirtiest energy source available says

Greenpeace in (2010). In this respect, Greenpeace lays out how dirty some of the most renown

cloud provider's biggest datacenters are.

49 It is reported in (Wheeland 2010) that Google and Microsoft are hitting PUEs of 1.1, and KC Mares, the founder of

MegaWatt Consulting and a veteran designer of large-scale datacenters, was able to achieve a PUE of 1.05 in a

recent project using currently available technology.



Google's Dalles facility does the best job, with 50.9 percent renewable energy from

hydroelectric power. Microsoft's Chicago facility does the worse job, with 1.1% of renewable

energy and 72.8% from coal-burning electricity.

But Greenpeace's concerns about cloud computing's negative environmental impact does not

stop here. They argue that with “The arrival of the iPad and growth in netbooks and other tablet

computers, the launch of Microsoft’s Azure cloud services for business, and the launch of the

Google phone and the proliferation of mobile cloud applications are compelling signs of a

movement towards cloud-based computing within the business sector and public consciousness in a

way never seen before.” (Greenpeace 2010)

So another burning question Greenpeace is posing about cloud computing is just how big the

cloud really is when it comes to electricity consumption and GHG emissions and how big will it

become given its rapid growth, and given that many major cloud brands refuse to disclose their

energy footprint.


Illustration 15: Comparison of significant cloud providers' datacenter fueling energy mix (Graphic

courtesy of Greenpeace International)


The Smart 2020 analysis has already forecasted that the global carbon footprint of the main

components of the cloud (datacenters and the telecommunications network) would see their

emissions grow, on average, 7% and 5% respectively each year between 2002 and 2020, with the

number of datacenter servers growing on average 9% each year during this period. The new report

brings adjustments to the Smart 2020 report forecast on the electricity demand of the global cloud,

highlighting the impact of the projected IT demand and importance of where and what sources of

electricity are being used to power Google, Amazon and other cloud-based computing platforms.

Table 5 is projection of growth in ICT electricity consumption and GHG emissions by 2020, using a

9% annual growth rate estimated in the Smart 2020 report for datacenters and recent estimate by

Gartner for growth in telecommunications of 9.5% a year.

Table 5: Projections of growth in ICT electricity and GHG emissions by 2020 (Source: Greenpeace

International)

Derived Electricity

Consumption

Forecast Electricity

Consumption

Derived Electricity

Emissions

Billion of kWh 2007 Billion of kWh 2020 MMTCO2Eq50 2020

Datacenters 330 1,012.02 533

Telecoms 293 951.72 501

Total Cloud 623 1,963.74 1034

Using the Environmental Protection Agency's Greenhouse Gas Equivalencies Calculator51, I

found that 1034 million metric tons of carbon dioxide equivalents (MMTCO2Eq) represents the

CO2 emissions from the electricity use of 125 million homes for one year!

Therefore, according to Greenpeace, cloud providers should build new datacenters in areas that

provide cleaner energy mixes for their grid, and push regulatory bodies, in the regions where their

existing datacenters are housed, to add more renewable energies to the grid.

4.5 Conclusion

The potential business benefits of Green IT along the lines of energy saving pressures should

50 million metric tons of carbon dioxide equivalents. See Glossary of terms for a definition.

51 http://www.epa.gov/RDEE/energy-resources/calculator.html



make ICT managers look at ways of increasing the efficiency of their operations. In the short term,

while these issues need to be addressed, they will remain highly complex. This dilemma should

accelerate the move towards the energy-efficiency value proposition of the cloud computing model

that presents itself as one of the viable options to reduce much of the risk associated with a

datacenter's inefficiency, especially for non-core applications such as Web applications. With regard

to the future legislative landscape, it is extremely important that ICT managers begin planning and

implementing a methodology to better understand their own carbon footprint and efficiency today to

ensure that operations are ready once legislation is approved by the EU and enforced by the member

states.

However, Greenpeace observes that the cloud phenomenon may aggravate the overall climate

change situation because the collective demand for more computing resources will increase

dramatically in the next few years. Even the most efficiently built datacenter with the highest

utilization rates will only mitigate, rather than eliminate, harmful CO2 emissions until regulatory

measures are taken by governments to incite the generation and use of renewable energy sources in

cloud computing infrastructures.



5 Total Economic Impact Methodology

In this dissertation I will apply The Total Economic Impact™ Methodology: A Foundation For

Sound Technology Investments by Forrester that is described in (Gliedman 2008) (Erickson &

Hughes 2004) and in (Leaver 2009). The Total Economic Impact (TEI) methodology is the product

of field practitioners and industry analysts' work with Forrester. The goal of this methodology is to

provide a practical and compelling framework that embraces all the critical components of

quantified—as opposed to fuzzy—risk and flexibility analysis of a business case template for ICT

investments.

“The TEI methodology quantifiably measures the business value of an ICT decision or

project. [...] The TEI is composed of four main elements with associated tools and

methodologies for quantification. Together, they provide holistic tool for assessing and

justifying ICT investments”(Gliedman 2008, p.3)

Given the increasing sophistication that enterprises have regarding cost analysis related to ICT

projects, Forrester's TEI methodology provides a complete picture of the total economic impact of

an ICT project by looking at four fundamental financing decision points with associated tools and

methodologies for quantification.

1. Benefits: the TEI methodology calculates the benefit of a technology investment

decision in a given use-case scenario. TEI quantifies both tangible and intangible

benefits and their dependencies over the period of analysis by identifying and

calculating their positive business impacts, such as efficiency or revenue gains over the

period of analysis.

2. Cost: TEI looks to determine the cost of investing in a new initiative, application, or

technology by analyzing the change to ICT and business operations caused by the new

technology investment compared with the cost of maintaining the current environment

over a given period that can include planning, implementation, maintenance, and the

associated internal efforts and resources.

3. Risk: to reduce the marginal error of the estimated benefit and cost, TEI quantifies the

impact of risk to establish a more realistic view of likely outcomes by tempering initial



benefit estimates to compensate for environmental and technical uncertainty. The result

is a risk-adjusted estimate that is most likely a more accurate predictor of the future.

4. Flexibility: to provide visibility into the investment life cycle, TEI values the future

options that are created by the investment decision and estimates the future likely impact

of ICT investments by monetizing values of future options created that often result from

infrastructure, application architectures, excess capacity and similar platform

investments.

The TEI quantification of benefits, cost, risk and flexibility is illustrated in the illustration

below.

5.1 Benefits Measure Future Positive Impacts of the Project

The TEI methodology applies a rigorous process and best practices to improve accuracy in

valuing technology benefits as described in (Gliedman 2008) and (Erickson & Hughes 2004), which

consist in:

1. Establishing categories of tangible benefits to quantify.

2. Establishing quantifiable metrics for each benefit.

3. Establishing current baselines and future projections for each metric.


Illustration 16: The Four Elements of TEI: Benefits, Cost, Risk and Flexibility for Financial

Analysis (Graphic courtesy of Forrester Research, Inc.)


4. Establishing an “exchange rate” for the metric.

The TEI methodology accounts for five categories of technology benefits, as indicated in the

Table 6.

Table 6: Five Categories Of Technology Benefits (Source: Forrester Research, Inc.)

Benefits categories Typical measure of benefit

Revenue • Incremental revenue increase, tempered by any additional costs of

the additional goods sold

User productivity • The increased output per worker

• The workers’ time saved

• Cost avoidance in additional staff

Capital efficiency • Increased output per capital investment

• Direct cost avoidance

Protection of assets • Cost avoidance of liability X probability

• Cost avoidance for the “insurance” that would transfer liability to

third party

Compliance • Cost avoidance of any penalties or fines

• Often assumed to be a business requirement

• Weighs costs of different approaches to reach required level

5.2 Cost Measures of the Negative Impact on ICT and the Business

The 'traditional' total cost of ownership (TCO) model aims to “capture all of the potential cost

areas, with the goal appearing to be the 'conclusive' determination of the total costs for performing

an ICT function. TEI, on the other hand, focuses on the changes to ICT and business spending that a

project under consideration will involve” (Gliedman 2008). That is essentially the investment

required to bring this new initiative, application, or technology online, as opposed to measuring the

cost of maintaining the current environment only. Therefore, I will define sequential phases for the

project under study for its periods of analysis and quantify the expected costs—like upfront

software and project management costs, cloud hosting costs, training, and ongoing support costs—

for each relevant phase and cost categories, as described in Table 7.



Table 7: Typical Cost Categories By Phase (Source: Forrester Research, Inc.)

Project Phase Typical Cost Categories

Planning and Design Planning end-solution evaluation resources

Legal and other internal and external resources

Pilot or proof-of-concept expenses

Implementation Software/hardware licensing fees

Consultants and other external resources

Development, testing and other internal resources

Operations Ongoing license/maintenance fees

Operations personnel

Systems administration and management

Maintenance and

modifications

Planned upgrade or enhancement costs

Other expected incremental expenses

5.3 Risk Quantifies the Impact of Future Uncertainties

Forrester states in (Gliedman 2008)that any number associated with project costs and benefits is

an estimate based on the available knowledge at a certain point in time. Future unknowns and or

changes will cause the costs and benefits of an investment to diverge from the original estimate. The

TEI methodology addresses this level of uncertainty by quantifying risk impacts on the initial

estimate, and projected return, to establish a risk-adjusted view of the financial figures.

The TEI methodology focuses on four core categories of investment risks:

• Implementation risks

• Impact risks

• Strategic risks

• Measurement risks

Implementation and impact risks are applied to individual cost and benefits estimates. The

impact of strategic and measurement risks is calculated in the investment level. The risk-adjusted

analysis for the project under study will start with the initial expected cost and benefit estimates. To



this initial estimate, I will apply four different risk scenarios as recommended by Gliedman in

(2008).

• Determine the worst case value for the estimate. On the cost side, I will determine

the worst case considering the organization's familiarity with the technology, the size

and duration of the project, previous experiences with similar projects if any, and

other relevant affecting criteria. On the benefits side, I will determine the worst case

using criteria such as the likelihood that the users will be able to achieve the

expected productivity benefits, and whether the organization will be able to capture

and leverage those benefits.

• Determine the best case for the estimate. Determining the best case estimates for

costs and benefits should ideally not be the same as the initial estimates.

• Determine an expected value for the projected range of cost and benefit

estimates. The three estimate numbers define a range of potential outcomes.

Therefore, calculating the arithmetic mean of these three estimates should

approximate the expected value for this range of outcomes.

• Recalculate return on investments based on risk-adjusted estimates. As the risk

adjusted estimates better represent the likely outcome of the project.

Table 8: Typical Risk Categories and Impact (Source: Forrester Research, Inc.)

Risk categories Typical characteristics

Implementation risk Cost or time estimates are incomplete or optimistic

Expected infrastructure proves to be inadequate as planned

Technical snags drive up project costs

Impact risk Project fails to deliver expected benefits

Users fail to change behavior and capture value

Marketplace fails to embrace new application

Strategic risk Poorly understood goals lead to strategic miss

Organization strategy shifts prior to project completion

Measurement risk Lack of post-project analysis breaks link between project costs and expected

benefits



Risk categories Typical characteristics

Poorly defined metrics reduce focus on key benefits

5.4 Future Options for Flexibility Values

Finally, Forrester introduces in (Gliedman 2008) the notion of investment choices that, in

addition to the direct benefits, create the option to accelerate other initiatives at a later date. These

investment choices or flexibility options may produce no immediate return but will create

opportunities for future growth and adaptation.

Table 9: Common Flexibility Options (Source: Forrester Research, Inc.)

Variable Description

Application suite or

platform

Ability to expand application into other areas with reduced costs and time

Exercise price 0

Excess capacity (e.g.,

server, network storage)

Ability to scale application to meet increased demand at a lower

incremental cost

Standardized

architecture

Ability to reduce time and cost for follow-on application projects

Application upgrades Ability to expand application functionality to meet additional business

requirements when desired

Training Ability to quickly ramp-up staff when business needs dictate new

technology or direction

The dissertation will strive to identify the potential value of options that a particular cloud

hosting solution can offer compared to the typical incremental cost of purchase that an on-premises

hosting equivalent would entail. The TEI methodology uses the Black-Scholes or the binomial

option pricing models to quantify the value of these flexibility options.

Table 10: Flexibility Options Pricing Models (Source: Forrester Research, Inc.)


Value of the future asset Expected business or asset value of the project once the option is

exercised

Exercise price 0




Length of the option Length of time that the option is available. Generally, if an option is not

used within two budget cycles, it will expire unused.

Risk-free rate of return Baseline return that the money invested in the option would have

produced

Market volatility Measure of future uncertainty. Generally, the more volatile the market in

which an organization resides, the more valuable the options.



6 Financial Analysis

6.1 Introduction

From the information provided in an in-depth interview with the ITC staff, I have constructed a

TEI framework to better assess the economic impact of the migration of the development and test

activities of GEC to the AWS cloud. The objective of the framework is to identify the cost, benefit,

flexibility and risk factors associated with the project that affect investment decisions. For

simplicity, most monetary values shown in this study are rounded to the nearest dollar. The US

Dollar (USD) currency has been chosen instead of the Euro because most of Amazon's AWS

services prices are expressed in USD.

6.2 Framework Assumptions

The cash flow tables used in the financial analysis start with an initial investment column that

reflects the costs incurred at time 0 or at the beginning of year 1. These costs are not discounted. All

other cash flows in years 1 through 3 are discounted using the discount rate shown in Table 11 at the

end of the year. Present value (PV) calculations are calculated for each total cost and benefit

estimate. Present value (PV) calculations are shown in summary tables and are the sum of the cash

inflows and outflows at a discounted rate in each year.

Table 11 lists the general financial assumptions metrics used throughout this Chapter.

Table 11: General Financial Assumptions and Metrics

Ref. Item Value Description

A1 Discount rate 10.00% Discount rate used to calculate the present value (PV) and

net present value (NPV). Organizations typically use a

discount rate between 8% and 16% based on their current

business environment and risk versus benefits appraisal.

A2 Length of analysis 3 years Time horizon used for the financial analysis.

A3 Datacenter average

PUE

2.5 No precise accounting was given to establish the actual

PUE of the actual datacenter. I have decided to use a rather

conservative value of 2.5, but the reality might be



Ref. Item Value Description

significantly worse.

A4 Amortization period 3 years Most companies amortize hardware equipment over a

period of 3 years (reflecting the equipment's “useful” life)

with a marginal end-of-life salvage value.

A5 Network cost ratio 20.00% No precise accounting was given to establish the network

cost ratio that is expressed as a percentage of the initial

purchase (or acquisition) price of the servers. I have used

an industry standard value of 20% for this metric.

A6 Annual hardware

maintenance cost

ratio

10.00% No precise number was given to establish the annual

hardware maintenance cost that is expressed as a

percentage of the initial hardware equipement purchase

price. I have used an industry standard value of 10% for

this metric.

A7 Work days per year 220 44 weeks of 5 days

A8 Number of users 105 The number of end users (engineers working on software

R&D) is relatively stable and has even decreased over the

last couple of years.

A9 Electricity Price per

Hour (kW)

$0.14 Average commercial value of electricity for enterprises in

France as of May 2010 with a Euro to USD conversion rate

of 1 Euro = 1.23102 USD

A10 Power Conversion

Factor

50% Power Conversion Factor is used to convert the nameplate

power rating (maximum) to average operating load.52

Table 12 provides rough estimates of the annual utilization ratio for the compute nodes of the

datacenter along the year. This ratio will be used to calculate the ongoing operational costs of using

AWS EC2 instances that are charged by the hour. A utilization ratio of 100% means that a server is

used 8,760 hours per year (24 hours x 7 days x 52 weeks). Some server types are non-shared,

meaning that their usage is solely dedicated to one user at a time. These are small machines. Shared

servers can be used by multiple users simultaneously. They are multi-core servers with a large

52 Server manufacturers indicate a power rating value on the nameplate of every server. Datacenter administrators,

however, know that this represents a worst-case scenario, and typical server power consumption never reaches the

rated nameplate value. Nameplate ratings are typically de-rated by 50% to 75%, according to (Raritan Inc. 2009).



amount of memory and disk space.

Table 12: Compute Nodes Usage Patterns

Ref Server Types Typical Tasks Typical Usage

Pattern

Number of

Hours per

Year

Annual

Utilization

Ratio

A11 Small Ongoing office, software

programming, debugging,

and documentation

writing tasks

8 hours x 5 days x 44

working weeks (non-

shared)

1 760 20%

A12 Small Multi-node debugging

tasks

8 hours x 44 working

weeks (non-shared)

352 4%

A13 Medium Compilation and other

integration testing tasks


weeks (shared)

2 080 24%

A14 Large Automated build, non-

regression testing, and

other CPU intensive tasks


weeks (shared)

1040 12%

A15 Extra large Integration, performance

and acceptance testing

tasks


weeks (2x2 months)

(shared)

480 5%

6.3 Baseline Cost of Business

The information obtained while interviewing the ICT staff for the elaboration of the project that

is discussed in Chapter 2 “Project Description”, allows evaluating the baseline cost of business of

the GEC's datacenter that is comprised of the support staff salaries for the ongoing administration of

the systems, and the datacenter operations costs that include the electricity bill and the hardware

cost.

6.3.1 Cost of Support Staff

When companies own and operate servers on-site, they need to hire full-time personnel to

procure, deploy, support and manage server assets. Server administrators perform a variety of



duties, including: server upgrades, restores, patches or moves, problem/incident management,

vendor management and monitoring. Table 13 describes the salary assumptions for the support staff

that is in charge of the ongoing management of the datacenter. Table 14 gives an estimate of the

fully-burdened annual salaries53 for this staff, which is derived from the above assumptions. The

support staff is comprised of the ICT team manager, two system administrators and nine lab

managers, whose lab-dedicated activities account for about 20% of their work time.

Table 13: Support Staff Salaries Estimates

Ref. Metric Base Increase per

Annum

B1 ICT manager salary $126 000 3%

B2 ICT staff salary $99 000 3%

B3 Lab manager salary $106 000 3%

Table 14: Annual Cost of Support Staff Salaries

6.3.2 On-Site Datacenter Costs

The datacenter is composed of the central computer room of 160m2 and the 9 engineering labs

of around 50m2 each. When not directly available, the costs listed below use industry data based on

AWS customer research provided by the Amazon EC2 Cost Comparison Calculator to quantify the

annual fully burdened cost of owning, operating and maintaining a datacenter infrastructure. I could

quantify only the most significant costs as many ancillary costs such as architecture and engineering

fees, cabling, real estate, ICT build-out, facilities management, datacenter security, the cost of

53 Fully-burdened salary includes all direct and indirect charges that equals raw salary x 1.42, with an increase of 3%

per annum.


Ref. Metric Cost Basis Number Discount RateB4 ICT manager salary 1 100%B5 ICT staff salary 2 100%B6 Lab manager salary 9 20%B7 Total

Salary Amount

$126 000 $126 000$99 000 $198 000

$106 000 $190 800$514 800


invested capital, debt service and so forth, are simply unknown or could not be obtained. As such,

owning and operating a datacenter infrastructure incurs additional costs beyond those provided

below, but I will assume that the proposed estimate is reliable enough to draw a reasonably accurate

baseline cost of business.

6.3.2.1 Server Hardware Cost

Four server configuration types are used in different contexts of development and test activities

ranging from the daily use of small servers to perform compilation and unitary testing of individual

software components, to medium and large servers to perform a full build and integration testing of

a complete software components stack, to extra larger servers to perform occasional heavy load

testing and performance testing tasks. To be able to perform a like-to-like cost comparison between

the on-site datacenter solution and the hybrid cloud computing solution, I have established that 1)

the Amazon EC2 virtual instances that will be provisioned have a comparable hardware

specification to that of the on-site physical computers, and 2) I use the same number of computers

for the calculation of the baseline cost of business as with the hybrid cloud solution.

Table 15 gives the number of computers under consideration for the calculation of the hardware

costs and also describes the hardware specification mapping54.

Table 15: Description of the Server Specifications Mapping and Quantity

Server Types Computer Hardware Specification AWS EC2 specification

equivalent

Number of

Computers

Small server 32-bit platform, one 2 GHz CPU,

2 GB of memory, 160 GB disk,

disk storage

Small Instance 1.7 GB of

memory, 1 EC2 Compute Unit (1

virtual core with 1 EC2 Compute

Unit), 160 GB of local instance

storage, 32-bit platform

450

Medium server 64-bit platform, 1 quad-core

2 GHz CPU, 8 GB of RAM,

500 GB disk storage

Large Instance 7.5 GB of

memory, 4 EC2 Compute Units (2

virtual cores with 2 EC2 Compute

108

54 One Amazon EC2 Compute Unit provides the equivalent CPU capacity of a 1.0-1.2 GHz 2007 Opteron or 2007

Xeon processor. See http://aws.amazon.com/ec2 for more information on Amazon EC2 instance types



Server Types Computer Hardware Specification AWS EC2 specification

equivalent

Number of

Computers

Units each), 850 GB of local

instance storage, 64-bit platform

Large server 64-bit platform, 2 quad-core

2 GHz CPUs, 15 GB of memory,

850 GB disk storage

Extra Large Instance 15 GB of

memory, 8 EC2 Compute Units (4

virtual cores with 2 EC2 Compute

Units each), 1690 GB of local

instance storage, 64-bit platform

54

Extra large

server

64-bit platform, 4 quad-core

2 GHz CPUs, 32 GB of memory,

1 TB disk storage

High-Memory Double Extra

Large Instance 34.2 GB of

memory, 13 EC2 Compute Units

(4 virtual cores with 3.25 EC2

Compute Units each), 850 GB of

local instance storage, 64-bit

platform

27

Total 639

The cost of hardware is based on the total cost of purchasing, operating and supporting server

equipment in the datacenter that includes server hardware, network hardware and hardware

maintenance. Software expenses including both operating system and application licenses, as well

as the datacenter's floor space renting expenses are not accounted for in the financial analysis as

these expenses are invariant costs regardless of whether parts of the computing assets are moved to

the AWS cloud or not.

When calculating the financial impact of a datacenter, one needs to take into consideration the

cost of computer replacement that is captured in the depreciation cost. As such, the initial

acquisition price of hardware is depreciated over the asset’s expected useful life, which in our case

is three years (A4). Therefore, using a straight-line depreciation, the annual server expense is the

initial purchase price minus the end-of-life salvage value, divided by the expected useful life. For

simplicity, I will assume here a zero salvage value. Thus, the annual cost of servers is calculated by:

Annual Cost of Server Hardware = (Total Number of Servers * Cost per Server) / A4



6.3.2.2 Network Hardware Cost

Network hardware allows servers to connect to one another and to the Internet. Network

hardware includes firewalls, routers, switches, intrusion detection systems and other equipment. I

have assumed the cost of network hardware as 20% (A5) of the initial server hardware cost with a

straight-line depreciation over the expected useful life of 3 years (A4). The annual cost of network

hardware is calculated by:

Annual Cost of Network Hardware = (Total Number of Servers * Cost per Server * A5) / A4

6.3.2.3 Hardware Maintenance Cost

Server and network hardware is commonly purchased with an annual maintenance contract to

repair the equipment in case of hardware failure. The annual maintenance cost is expressed as a

percentage of the initial purchase cost of server and network hardware that I have assumed to be

approximately 10% (A6), as suggested by the Amazon EC2 Cost Comparison Calculator. The

annual cost of maintenance for server and network hardware is calculated by:

• Annual Hardware Maintenance Cost = Annual Maintenance Cost of Servers + Annual

Maintenance Cost of Network Hardware, where:

• Annual Maintenance Cost of Servers = Total Number of Servers * Cost per Server

* A6

• Annual Maintenance Cost of Network Hardware = Total Number of Servers *

Cost per Server * A5 * A6

6.3.2.4 Server Operating Power and Server Cooling Power Cost

Datacenter servers not only consume electricity but also convert that electricity into heat, which

must be removed from the datacenter to avoid overheating the server equipment. The power

required to cool datacenter servers can equal or exceed the power used by the servers themselves as

explained extensively in the chapters above. The Green Grid consortium estimates that most

datacenters have a PUE ratio of between 1.3 and 3.0. I have assumed a PUE of 2.5 (see A3) as a

conservative value, but the reality might be significantly worse as very little energy-efficiency

measures have been taken to reduce the PUE of the datacenter at GEC. I have used the Amazon



EC2 Cost Comparison Calculator equation to calculate the total power usage (power and cooling)

by estimating the de-rated energy demand of servers multiplied by the PUE ratio. The total

electricity usage is then multiplied by the average cost of electricity per kW hour to estimate the

total annual power cost. The annual cost for server operating power and cooling is calculated by:

• Annual Cost of Power and Cooling = Total Number of Servers * Nameplate Power per

Server55 * A10 * Server Operating Hours per Year56 * A3 * (A9/1000)

Table 16 gives an estimate of the annual cost associated with owning and operating the

datacenter of GEC as of 2009.

Table 16: Annual Datacenter Cost Summary

6.3.3 Summary of Baseline Cost of Business

55 Nameplate Power per Server is the maximum amount of power (watts) used by servers, categorized by Amazon

EC2-equivalent server type based on guidance from major hardware vendors.

Standard Compute High-Memory High Compute

Small = 150W Double X-Large = 515W Medium = 289W

Large = 240W Quad X-Large = 630W X-Large = 309W

X-Large = 391W

56 The number of hours each year the server is turned on that is typically 8,736 hours (24x7x52)


Items Year 1 Year 2 Year 3Datacenter Servers Costs

C1 Small Servers $363 450C2 Medium Servers 108C3 Large Servers 54C4 Extra Large Servers 27

Datacenter Network Hardware CostC5 Network Hardware

Datacenter Hardware Maintenance CostC6 Hardware MaintenanceC7 Total Hardware Cost

Datacenter Power and Cooling CostC8 Power and cooling (kW) $0,14 1402554C9 Total Datacenter Cost

Ref.Cost per

ItemNumber of Items

Cost Basis

$163 350 $54 450 $54 450 $54 450$1 452 $156 816 $52 272 $52 272 $52 272$2 903 $156 762 $52 254 $52 254 $52 254$6 473 $174 771 $58 257 $58 257 $58 257

$130 340 $43 447 $43 447 $43 447

$78 204 $78 204 $78 204$782 039 $338 883 $338 883 $338 883

$196 358 $196 358 $196 358$535 241 $535 241 $535 241


By adding the annual cost of support staff salaries to the annual datacenter cost, we get an

estimate of the actual baseline cost of business for the on-site datacenter of GEC.

Table 17: Total Baseline Cost of Business

6.4 Quantification and Monetary Valuation of Relevant Impacts

6.4.1 Project Implementation Costs

This Section describes the overall cost to plan, design, implement and maintain the AWS-based

hybrid cloud solution for the software development, test and quality assurance (QA) activities of

GEC.

It is well understood that such a project cannot be achieved overnight. Several phases will be

required to complete a workable solution and mitigate risks. This project is divided into a planning

and design phase, followed by a roll-out period phase that is itself divided into nine sub-phases.

6.4.1.1 Planning and Design Cost

Some amount of time will be required to plan the project, understand the AWS technology, and

design the required changes, and in particular design the modifications that must be implemented

into the Virtual Instance Reservation Portal (VIRP) application. The VIRP application is an

internally developed Web-based application that was designed by the ICT team to facilitate the task

of provisioning virtual machines in the virtualized lab environment. This application needs to be

modified in order to enable the creation and deployment of new EC2 instances and EBS volumes in

the AWS cloud. Additional functionalities should be developed to support access rights controls,

and the enforcement of resource quotas, as well as the automatic reclaim and shutdown of unused

compute resources. No external professional services will required to assist with the design and


Items Year 1 Year 2 Year 3D1 Total Support Staff Cost (B7)D2 Total Datacenter Cost (C9)D3 Total Baseline Cost

Ref.$514 800 $530 244 $546 151$535 241 $535 241 $535 241

$1 050 041 $1 065 485 $1 081 392


implementation of the project. The engineers working at GEC are security and virtualization

experts, and therefore the ICT manager in charge of the project should have no problem finding the

right resources to design the solution properly . However, the project will require the part-time

effort of the ICT team, lab managers, and some amount of time from software engineers to fine-tune

the design of the solution. Overall, it is estimated that the effort to become familiar with the AWS

technology and design the solution will take around 30 days. Various individuals will be involved

during that phase that precedes the roll-out (or implementation) phase. The fully-burdened averaged

salary for the employees that will be involved in the plan and design phase is estimated at $501 per

day57.

Table 18: Planning and Design Phase Cost, Non-Risk-Adjusted

Ref. Metric Calculation Initial

E1 Days of effort 30

E2 Daily fully-burdened loaded cost ((B1+B2+B3)/3)/A7 $501

E3 Planning and Design Cost E1 * E2 $15 030

6.4.1.2 Project Implementation Cost

The project roll-out phase will be scheduled over nine months, which corresponds to the in-

sequence migration of the nine labs. The initial and subsequent sub-phases will require some

amount of time from each lab manager and engineering teams to recreate and/or migrate their

current local development and test environment to the AWS cloud using the modified VIRP

application. This migration effort is estimated at 10 days per lab, which sum up to 90 days of

overall effort for the nine labs. To that amount, one must add the cost of implementing and testing

the modification brought to the VIRP application, whose cost of design work is accounted in the

planning and design phase. This task is estimated at 60 days of effort. For the sake of simplicity, I

will use the same fully-burdened average salary cost of $501 per day as calculated above.

Table 19: Project Implementation Cost, Non-Risk-Adjusted

Ref. Metric Calculation Initial Year 1

F1 Days of effort 60 90

F2 Daily fully-burdened loaded ((B1+B2+B3)/3)/A $501 $501

57 The averaged fully-burdened salaries for the ICT manager, ICT staff and lab managers is $501 per day.



cost 7

F3 Implementation Costs F1 * F2 $30 060 $45 090

6.4.1.3 Hybrid Cloud Infrastructure Cost

In order to complete an accurate financial analysis, it is of primary importance to determine the

number of servers that will remain on site versus those that will migrate to the AWS cloud. Amazon

EC2 instances are billed according to actual hourly usage, rounded up to whole hours. The price per

instance hour varies by Amazon EC2 Instance type and geographic region. Multiple Amazon EC2

instance pricing options are available, including the option to:

• Pay on-demand instance hourly usage rates.

• Pay a one-time fee to reserve an instance for 1- or 3-year terms and pay reduced reserved

instance hourly usage rates

Therefore, defining the number of instances needed and the percentage of time that each

instance will be used is the first step in estimating the annual Amazon EC2 instance cost. To this

aim, it has been determined that there are two instance categories that will be provisioned to cost-

effectively satisfy the total demand of EC2 resources along the year.

• Baseline instances that are assumed to be used by engineers for a large percentage of the

year, which represent the minimum base level demand for computing resources. The

purchasing of baseline instances will be based on the Reserve Instance pricing model on

a 3-year term, which corresponds to both the amortization period and length of analysis.

• Peak instances that are assumed to be used for a smaller percentage of the year and

represent the additional computing resources needed to satisfy workload peaks due to the

seasonal fluctuations arising from the product's development and testing life-cycle needs,

as well as unexpected workload spikes that can arise from on-off projects. The

purchasing of peak instances will be based on the On-demand Instance pricing model.

The annual usage (hours) of baseline and peak EC2 instances is calculated by:

• Hours of Baseline Instance Usage = No. of Baseline Instances * Hours per Year *

Average Annual Usage of Baseline Instances



• Hours of Peak Instance Usage = No. of Peak Instances * Hours per Year58 * Average

Annual Usage of Peak Instances

Tables 20 and 22 summarize a usage forecast for the total number of AWS EC2 instances and

Elastic Bloc Storage (EBS) volumes that are deemed sufficient and necessary to maintain the

development and test engineering activities. The usage forecast constitutes the calculation basis for

the AWS computing and storage resources that will be charged on a pay-per-use basis.

Table 20: Hybrid Cloud Computer Utilization Ratio Estimates

Ref. Server Types Number of

Instances

Annual Utilization

Ratio

Hours of Instance

Usage per Month

Hours of Instance

Usage per Year

Physical Servers Deployed in the Datacenter

G1 Small 150 n/a n/a n/a

G2 Medium 36 n/a n/a n/a

G3 Large 18 n/a n/a n/a

G4 Extra large 9 n/a n/a n/a

AWS EC2 Baseline Instances

G5 Small 100 20% 14 560 174 720

G6 Medium 72 24% 12 580 150 958

G7 Large 0

G8 Extra large 0

AWS EC2 Peak Instances

G9 Small 200 4% 5 824 69 888

G10 Medium 0

G11 Large 36 12% 3 145 37 739

G12 Extra large 18 5% 655 7 862

G13 Total 639 36 764 441 167

A total of 639 computers—compared to the actual 1000 computers—is deemed necessary and

sufficient to maintain the development and test activities at GEC. Among these 639 computers, only

213 will remain on-site at the end of the project roll-out phase. The computers that are no longer

used will be decommissioned from the datacenter to be recycled or donated to local communities

(e.g, startups, universities, ...). Table 21 shows the amount of decommissioned computers and how

it affects the cost of depreciation over the length of the analysis.

58 There are 8 736 hours per year (24 hours x 7 days x 52 weeks)



Table 21: Datacenter Depreciation Cost Basis Adjustment

Amazon EBS volumes can be mounted simultaneously on multiple EC2 instances. The annual

utilization ratio for the EBS volumes is mapped to the annual utilization ratio of the EC2 virtual

instances. The EBS volume utilization ratio is used to approximate the dollar amount that will be

charged by Amazon, knowing that with Amazon EBS, enterprises pay for what they use. Volume

storage is charged by the amount allocated until it is released, and is priced at a rate of $0.11 per

allocated GB per month in Europe. Amazon EBS also charges $0.11 per 1 million I/O requests you

make to your volume. For the sake of clarity and because there are no known metered values, I will

assume an average of 100 I/Os per second (IOPS) when an EBS volume is being used.

Average Number of IOPS per Volume per Month = 100 IOPS * 2.6 million59 * Annual

Utilization Ratio

Table 22 provides estimates of the number of EBS volumes needed to support the development

and test activities as specified in the action plan of the project description of Section 2.3.

59 There are roughly 2.6 million seconds in a month.


Items Cost per Item Year 1 Year 2 Year 3Datacenter Server Cost

H1 Number of Small Servers $363 150H2 Number of Medium Servers 36H3 Number of Large Servers 18H4 Number Extra Large Servers 9

Datacenter Network Hardware CostH5 Network Hardware

Datacenter Hardware Maintenance CostH6 Hardware MaintenanceH7 Total Hardware Cost

Datacenter Power and CoolingH8 Power and cooling (kW) $0,14 467518H9 Total Datacenter Cost

Ref.

Number of Items After

Rollout Cost Basis Adjustment

$54 450 $18 150 $18 150 $18 150$1 452 $52 272 $17 424 $17 424 $17 424$2 903 $52 254 $17 418 $17 418 $17 418$6 473 $58 257 $19 419 $19 419 $19 419

$43 447 $14 482 $14 482 $14 482

$26 068 $26 068 $26 068$260 680 $112 961 $112 961 $112 961

$65 453 $65 453 $65 453$260 680 $178 414 $178 414 $178 414


Table 22: Total Number of Elastic Bloc Storage (EBS) Volumes

Ref. EBS Volume Size Number of

Volumes

Total EBS

Volume Size

Annual

Usage

Number of IOPs

per Month

Number of IOPs

per Year

I1 70 GB 200 14 000 GB 4% 10 400 000 124 800 000

I2 70 GB 100 70 000 GB 20% 52 000 000 624 000 000

I3 128 GB 72 9 216 GB 24% 62 400 000 748 800 000

I4 256 GB 36 9 216 GB 12% 31 200 000 374 400 000

I5 512 GB 18 9 216 GB 5% 13 000 000 156 000 000

I6 Total 426 111 648 GB 117 000 000 2 028 000 000

A total of 426 EBS volumes—one per virtual server instance—of varying sizes ranging from 70

GB to 512 GB is deemed necessary and sufficient to maintain multiple versions of software

components under development as well as multiple configurations and testing scenario setups that

can be executed on baseline and peak instances. The total amount of EBS volumes that can be used

represent a total amount of around 111 TB.

The Amazon Virtual Private Connection (VPC) is charged for each VPN Connection-hour in

which a private link is established between GEC's datacenter and the AWS cloud to securely

transfer data in and out of Amazon VPC across the Internet. Each partial VPN Connection-hour

consumed is billed as a full hour. For performance reasons, it is assumed that there will be one VPN

Connection-hour per active user that will be charged $0.05 an hour. In addition to the VPN

Connection-hour, the data transfer cost for Amazon VPC is based on the amount of data transferred

in and out of the AWS cloud each month, excluding traffic coming from or going to other AWS

services (e.g. Amazon S3), as there is no charge for data transfers within a region of the AWS cloud.

A data transfer in is charged $0.10 per GB a month, and a data transfer out is charged at a rate that

varies on volume60.

It is expected that on a daily basis the amount of data transfer in and out will be fairly limited

once the export of all the required data assets (i.e. applications, software source trees, build

processes, data, test scenarios, ...) to the AWS cloud is complete. That is because users and

applications will be working directly off the cloud, which therefore implies that data will move

around mainly from within the cloud infrastructure with only occasional significant data transfers

60 See Appendix I: Amazon Web Services (AWS):VPN Data Transfer Pricing



between the two facilities. The physical data export process will take place progressively during the

roll-out period of nine months across the existing dual Internet leased line of 20 Mbps. In any event,

the maximum amount of data transfered during this period cannot exceed the total EBS volumes

capacity of 111 TB. Therefore, I will conservatively assume that the data export will be of

111 TB maximum the first year, and after that, the ongoing amount of data transfer in and out

should not exceed 1 GB per month, per user, and in equal proportion.

The annual VPN Connection-hour is calculated by:

• Yearly Hours of VPN Connection = No. of users (A8) * Working Hours per Year (A7 * 8)

The ongoing monthly Amazon VPC data transfer (in GB) is calculated by:

• Monthly Data Transfer In = No. of users (A8) * 500MB

• Monthly Data Transfer Out = No. of users (A8) * 500MB



Table 23 summarizes the total cost of the hybrid cloud infrastructure.

Table 23: Hybrid Cloud Infrastructure Costs, Non-Risk-Adjusted

Note: For the first year (Year 1), the AWS baseline instance usage fee, and the AWS peak

instance usage fee calculation are adjusted by a discount factor of 0,5833 to take into account the

fact that there is a progression of the monthly fees charged by Amazon during the roll-out phase

over the nine-month period, whereby: 1 twelfth of the monthly fee will be charged the first month;

2 twelfths the second month; 3 twelfths the third month, and so forth, until the eighth month

included. The last 4 months of the year will be charged 12 twelfths (i.e. full amount) of the monthly

fee.


Items Initial cost Year 1 Year 2 Year 3

J1AWS Reserved Instance On-Time Fee (3-year term)

J2 Small Server $350 100 $0 $0 $0J3 Medium server 72 $0 $0 $0J4 Large Server 0 $0 $0 $0 $0J5 Extra Large Server 0 $0 $0 $0 $0

AWS Baseline Instance Usage Fee (hours per year)J6 Small Server $0,040 174720J7 Medium server $0,160 150958J8 Large Server $0,320 0 $0 $0 $0J9 Extra Large Server $0,560 0 $0 $0 $0

AWS Peak Instance Usage Fee (hours per year)J10 Small Server $0,095 69888J11 Medium server $0,380 0 $0 $0 $0J12 Large Server $0,760 37739J13 Extra Large Server $1,340 7862

AWS EBS Volumes (IOPS & volumes size in GB)J14 EBS Volumes (GB) $0,100 111648J15 EBS IOPS in Million $0,100 2028 $118 $203 $203

AWS VPC Usage (VPN connections hours & data transfer in and out)J16 VPN Connections (hours) $0,050 237600J17 VPN data in (GB) $0,100 810 $81 $81J18 VPN data out (GB) $0,150 810 $71 $122 $122J19 Total Hybrid Cloud Cost

Ref.Cost per

ItemNumber of Items

Total Datacenter Hardware Cost (H8) $178 414 $178 414 $178 414

$35 000$1 400 $100 800$2 800$4 900

$4 077 $6 989 $6 989$14 089 $24 153 $24 153

$3 873 $6 639 $6 639

$16 730 $28 682 $28 682$6 145 $10 535 $10 535

$6 512 $11 165 $11 165

$6 930 $11 880 $11 880$6 583

$135 800 $243 542 $278 862 $278 862


6.4.1.4 Support Staff Cost

Because there will be much fewer physical servers on-site (i.e. 213 instead of 639) to maintain

and because each engineer will be able to provision pre-configured EC2 server instances

automatically through the modified VIRP application, it is expected that the support staff workload

entailed by the ongoing management of the datacenter will be greatly reduced, especially for the

two system administrators and the nine lab managers. Table 23 gives a re-evaluated estimate of the

annual fully-burdened salaries61 of the support staff, based on the assumption that system

administrators and lab managers will spend fewer time on operations, and therefore, will free up

time to focus on higher value-added activities. To be compared with Table 13.

Table 24: Annual Support Staff Salaries' Cost, Non-risk-Adjusted

6.4.1.5 Summary of Hybrid Cloud Project Costs

Table 25 summarizes the total project costs (or cash outflows) associated with the design,

implementation, infrastructure deployment and roll-out phases, as well as the cost of support staff

salaries—with an increase of 3% per annum—to manage and maintain the hybrid cloud

infrastructure.

The Present Value (PV) is calculated by:

PV = Initial Cost + Year 1 Cost * .9091 + Year 2 Cost * .8264 + Year 3 Cost * .7513

61 Fully-burdened salary includes all direct and indirect charges that equals raw salary x 1.42, with an increase of 3%

per annum.


Ref. Metric Base Number Discount RateK1 ICT manager salary 1 100%K2 ICT staff salary 2 80%K3 Lab manager salary 9 10%K4 Total

Salary Amount

$126 000 $126 000$99 000 $158 400

$106 000 $95 400$379 800


Table 25: Total Project Costs, Non-Risk-Adjusted

6.4.2 Benefits and Savings Opportunities

Many of the benefits that can be realized in this project are not easily quantifiable in terms of

ROI. The hypothesis is that the benefits of cloud computing should substantially lower the TCO of

operating the hybrid cloud infrastructure that supports the software development and test activities

of GEC. The goal of this Section is to express those benefits in monetary terms so that we can

assign a monetary valuation of their relevant effects. Some calculations made at this stage will be

performed under varying degrees of uncertainty given that there are two types of benefits:

• Quantitative benefits that go directly into the ROI calculation.

• Qualitative benefits that are not directly included in the ROI calculation.

However, the qualitative benefits are potentially as valuable as the quantitative ones and should

be taken into consideration in the total CBA valuation of the project. For the organization, the

following assumptions regarding the benefit estimates were made following the TEI benefits

categorization approach.

6.4.2.1 Revenues Benefits

The engineering activities of GEC generate revenues from the sale of software products and

services. However, this organization is not a profit center, with its own profit and loss (P&L)

objectives, but operates a cost center. In other words, the engineering center of A.C.M.E Corp.,

which is a subsidiary within the larger organization of A.C.M.E. Corp. generates expenses with no

accountability for creating revenues in return. The objective of this project, therefore, is to lower

expenses whenever possible while staying within the allocated budget that is defined at corporate


Items Year 1 Year 2 Year 3 TotalL1 Planning & Design Cost (E3)L2 Implementation Cost (F3)L3 Support Staff Cost (L3)L4 Hybrid Cloud Infra. Cost (J19)L5 Total Project Cost

Ref.Initial Outlay

Present Value

$15 030 $15 030 $15 030$30 060 $45 090 $75 150 $71 051

$379 800 $391 194 $402 930 $1 173 924 $971 280$135 800 $243 542 $278 862 $278 862 $937 066 $797 165$180 890 $668 432 $670 056 $681 792 $2 201 170 $1 854 526


level.

So, what is of interest in the revenue category of benefits in the financial analysis is the

operational cost savings that can be realized out of the development and test's computing resources

externalization to the AWS cloud. As shown in Table 26 there are three areas in which significant

operational cost savings can be achieved in this project.

4. Hardware equipment cost savings as a result of externalizing the computers that have a

low annual utilization ratio to the AWS cloud.

5. Electricity consumption cost savings as result of externalizing a large chunk of the

computers and storage to the AWS cloud.

6. Support staff cost savings as a result of a better effectiveness of the solution.

These cost savings are summarized in Table 26.

Table 26: Total Operational Benefits, Non-Risk-Adjusted

6.4.2.2 User Productivity Benefits

Amazon AWS services carry a prebuilt infrastructure foundation that provides good support for

easy virtual machine provisioning and allows consistent virtual machine image upgrades, which can

impact positively both the productivity of the engineers as well as the system administrators and lab

managers. In addition, as stated above, each engineer will be able to provision pre-configured EC2

server instances automatically through the modified VIRP application. Therefore, I posit that there

will be benefits to the organization not only in terms of improved productivity for the support staff,

but also in terms of an overall improved effectiveness of the solution for the engineers working on

site as outlined below.

1. No contention for resources: development and QA can each get as much computing


Items Calculation Year 1 Year 2 Year 3 TotalM1 Hardware Cost Benefits C7-H7M2 Electricity Cost Benefits C8-H8M3 Support Staff Cost Benefits D1-L3M4 Total Operational Benefits

Ref.$225 922 $225 922 $225 922 $677 766$130 905 $130 905 $130 905 $392 715$135 000 $139 050 $143 221 $417 271$491 827 $495 877 $500 048 $1 487 752


resource as they need to do their job easily. This means that development and QA no

longer have to contend for a limited pool of compute nodes and storage.

2. Agile development and spiky usage is supported: Amazon's AWS lack of long-term

commitment for users of the cloud services means that engineers can get the computing

resources they need at one point in time, and then release the resources with no further

commitment. The short-duration, variable usage patterns typical of development and test

activities are well-aligned with this capability.

3. QA can be more productive: tedious test-harness installation and configuration tasks

become unnecessary. As a cloud computing environment is typically based on

virtualization, it is easy to clone virtual machines and recreate a test-harness

environment. Furthermore, development teams can hand over to QA a pre-installed and

pre-configured virtual machine (or even a collection of virtual machines) that have been

cloned and that QA can readily start using. This capability obviates numerous issues

highlighted in the development and tests challenges introduced in Section 2.1, such as a

missing component in the test-harness install recipe which may cause the test to fail or

produce undesirable results.

4. Scale and load can more easily be tested: Amazon's AWS infinite resources mean that

setting up a load test with hundreds or even thousands of simultaneous requests is more

easy to accomplish in the cloud than in an on-premises datacenter because as many

servers as necessary can be started to generate load without requiring physical asset

procurement. The issue of hardware provisioning for load and stress testing is the kind of

hassle that drives software development organizations crazy because there is never

enough compute resources available when needed. Then, after a load test is finished a

few hours later, there is a big tear-down effort as well. Instead, with development and

test in the cloud, it is easy to fire up a large number of Amazon EC2 instances, run them

for a few hours or a couple of days at a very small cost and then shut them down.

As a result of all the above, the solution should improve the productivity of the engineers

working on development and test activities as well as inducing higher quality products. Not having

to continuously stand the burden of competing for resources and making the process of provisioning

and configuring lab machines simpler can ultimately improve the way that end users to their job.



Based on the assumption that engineers will spend fewer time on these tasks, it is assumed that they

will free up time. A general policy of GEC is that under most circumstances, employees are

redeployed to new areas of greatest need when their workload is significantly reduced. In doing so,

GEC can avoid the cost of hiring additional engineers to work on new projects. Table 27 illustrates

the average productivity gain estimates and ensued salary savings obtained as part of these

estimates.

Table 27: Annual Engineering Salaries Benefits, Non-risk-Adjusted

With an annual salary savings of about $923,550 resulting from the expected engineering

productivity improvement, GEC could afford to redeploy about 10 engineers on new R&D projects

an avoid hiring new engineers to satisfy new staffing needs.

Below are other unquantified qualitative benefits that can be achieve with the development and

test in the hybrid cloud solution.

6.4.2.3 Capital Efficiency Benefits

The pay-as-you go model, instead of investing capital expenditures upfront, is cost-effective and

allows greater flexibility in cash flow. This means that GEC can scale gracefully with the demand

for computing resources and possibly fund new development and QA projects simultaneously, all

without having to budget capacity, investments and personnel ahead of time.

6.4.2.4 Compliance Benefits

The potential benefits that can be achieved from the point of view of regulatory compliance

concerns primarily the enactment of future environmental laws regulating the ICT sector. As

discussed in detail in Section 4.1.3 “Energy Policies and Implementations”, today's energy

efficiency legislations in the EU in this sector are inciting rather than constraining, but this may


Ref. MetricN1 Engineer Productivity Benefits 105 10%N2 Manager Productivity Benefits 9 3%N3

Base Salary

Number of Users

Average Productivity

GainSalary

Amount$85 000 $892 500

$115 000 $31 050Total Productivity Benefitss $923 550


change in the future. The Green Grid consortium has identified a wide range of current and future

energy policy responses that are of importance to our case among which the European Code of

Conduct (EU CoC) on Data Centres Energy Efficiency, which may bear some significant impacts

on the design and operations of datacenters. The key implications and benefits of the EU CoC for

GEC are that by significantly reducing the electricity consumption of its datacenter, the organization

concours to mitigating the risk of future legislations and could be illegible, as EU CoC low energy

champions, for awards, promotional activities and could enhance their reputation as first class EU

CoC citizen. Similarly, recommendations from the DETIC of the Grenelle of the Environment62

may warrant for tax incentives and/or tax deductibles in the future to those enterprises investing in

improving the energy efficiency of their datacenter.

6.4.2.5 Summary of Quantifiable Benefits

Table 28 summarizes the total benefits (or cash inflows) associated with the operational cost

benefits and the engineering engineering productivity benefits—with an increase of 3% per annum

—that can be achieved with a successful realization of the hybrid cloud solution.

The Present Value (PV) is calculated by:

PV = Initial Cost + Year 1 Cost * .9091 + Year 2 Cost * .8264 + Year 3 Cost * .7513

Table 28: Total Quantifiable Benefits, Non-Risk-Adjusted

6.4.3 Risk Quantification

Risk quantification is the third component within the TEI model. It is used as a filter to capture

the uncertainty surrounding different cost and benefit estimates. If a risk-adjusted ROI still

demonstrates a compelling business case, it raises confidence that the investment is likely to

62 See Section 4.1.3.4.


Items Year 1 Year 2 Year 3 TotalO1 Operational Costs Benefits (M4)O2 Engineer Salary Benefits (N1)O3 Manager Salary Benefits (N2)O4 Total Quantifiable Benefits

Ref.Present Value

$491 827 $495 877 $500 048 $1 487 752 $1 232 599$892 500 $919 275 $946 853 $2 758 628 $2 282 431

$31 050 $31 982 $32 941 $95 972 $79 406$1 384 327 $1 415 152 $1 446 901 $4 246 380 $3 515 030


succeed because the risks that threaten the project have been taken into consideration and

quantified. The risk-adjusted numbers should be taken as realistic expectations, since they represent

the expected values considering risk. In general, risks affect costs by raising the original estimates,

and they affect benefits by reducing the original estimates.

For the purpose of this analysis I have used a risk-adjusted cost and benefit estimates to better

reflect the level of uncertainty that exists for each estimate. Each benefit and cost is assigned either

a low, medium, high, or none risk rating.

The TEI model applies a probability density function known as the triangular distribution

method to calculate risk-adjusted values. To construct the distribution, it is necessary to first

estimate the low (the best case for the estimate), most likely (expected value for the estimate), and

high (worst case value for the estimates) values that could occur within the current cost or benefit

environment. The risk-adjusted value is the mean of the distribution of those points. For example,

the risk associated with project implementation cost is rated as high because GEC could likely

spend more time and money on the initial implementation and roll-out phases than originally

planned. Therefore, a reasonable likelihood exists that the organization will incur more costs at this

stage of the project. The original estimated cost is $71,051 (PV). To calculate the risk-adjusted cost,

the most likely scenario was set at 100% of cost, the high scenario was assigned 120% of cost, and

the low scenario was assigned 95% of cost. The rounded mean of these three values is 105%. The

resulting cost used in the risk adjustment to costs table is therefore $74,60$, or 105% of $71,051.

The project's benefits and costs are rated as either low, medium or high risk. The following Table

shows the values used to adjust for uncertainty in cost and benefit estimates and provide rational for

that scoring.

Table 29: Costs and Benefits Risk Scoring

Risk / Cost

Adjustment

Risk

Score

Low Most

Likely

High Mean Comments

Planing and Design

Cost

Low 100% 100% 110% 103% The risk is low but it is unlikely that

there is an opportunity to decrease

the expected cost

Project

Implementation Cost

Medium 95% 100% 120% 105% The risk is moderate with a possible

slight opportunity to see a decrease



Risk / Cost

Adjustment

Risk

Score

Low Most

Likely

High Mean Comments

of the expected cost

Hybrid Cloud

Infrastructure Cost

High 95% 100% 150% 115% The risk is high with a possible

slight opportunity to see a decrease

of the expected cost

Support Staff Cost Low 90% 100% 105% 98% The risk is low but the likelihood to

overachieving the expected savings

is also low.

Operational Cost

Savings

Medium 75% 100% 110% 95% The risk is moderate with a possible

slight overachieving of the expected

savings

Productivity Savings High 20% 40% 100% 53% The risk is (very) high and hence

discounted heavily with little hope to

achieving the expected savings in

this category in workforce costs

cutting times.

• The Planning and Design Cost is estimated low risk because the Virtual Instance

Reservation Portal (VIRP) application already exists and GEC has the necessary

expertise to adapt the application. However the AWS cloud infrastructure learning curve

may be slightly longer than expected.

• The Project Implementation Cost is estimated medium risk because because GEC's lab

managers and engineers have previous experience with server virtualization and the

VIRP application, which means they have the necessary expertise to quickly and easily

implement the roll-out phases. However, migrating the development and test activities to

the AWS cloud over a nine months period is still a difficult and challenging task that will

necessitate focus and acceptance from the end users.

• The Hybrid Cloud Infrastructure Cost is estimated high risk primarily because the

technology is new and because GEC has no prior experience with working in the AWS

cloud. There is very little usage metrics that allow to precisely figure out the cost



associated with using the AWS's EC2, EBS and VPC services. For example, it is quite

possible that the assumptions made about the hours of baseline and peak instances usage

are wrong, as well as the amount of data transfered between the datacenter and the cloud

through the VPN connections, and that the number of I/O operations on the EBS

volumes are underestimated or even overestimated. Only a pilot program running for

several months and covering the complete software engineering life-cycle of a product

could ascertain the accuracy of those usage metrics. On the other hand, the risk

quantification of development and test activities in the AWS cloud avoid most the typical

regulatory compliance issued discussed in Section 3.5.7. This is because most of

regulatory compliance risks pertaining to data privacy, SOX, PCI DSS, and so forth do

not apply directly to the business of software R&D. Furthermore, the AWS VPC service

implemented in the hybrid cloud solution provide a solid security foundation for the

protection of the intellectual property (IP) assets of GEC since it is sand-boxed in a

private zone of the AWS cloud infrastructure. The other risks listed in Section 3.5 appear

of minor concern to this use case.

• The Operational Cost Benefits is estimated medium risk because it may happen that the

number of on-site servers is not sufficient to perform the performance benchmarks,

product qualification, and other similar platform certification tasks. It may be necessary

to provision more servers in the datacenter than initially planned that would directly

impact negatively the datacenter costs by increasing the hardware cost, the maintenance

cost and the electricity consumption cost.

• The User Productivity Benefits is estimated (very) high risk and hence discounted

heavily because in practice the redeployment of engineering resources to new or

different R&D projects often faces numerous frictions that arise from an inadequacy of

technical skills and willingness to change. This concern is aggravated by the fact that in

workforce costs cutting times, there is a hiring freeze, which means that new projects are

staff by internal redeployments as opposed to new hirings. In addition, the cost of hiring

additional resources is relevant only to the extent that there are new projects coming in,

which at this point in time is not guaranteed. However, it is expected that when

productivity gains are obtained they should be leveraged by management to create new



business opportunities, therefore it is not completely unlikely that in the course of the

three-year period, productivity gains may entail some new hirings.

6.4.4 Flexibility Quantification

Flexibility, as defined by TEI, represents an investment in additional capacity or capability that

could be converted into business benefit for some additional future investments. Flexibility would

also be quantified when evaluated as part of a specific project. The organization described in this

study has several flexibility options that can deliver future benefits.

For example, GEC could expand its R&D operation to the AWS cloud, thereby reducing costs. It

could also provision additional EC2 peak instances to avoid computing resource contentions at low

cost. However, for this study, these benefits were not quantified as part of the ROI analysis.

6.5 Discounting of Costs and Benefits Flows

Considering the financial framework constructed above, the results of the costs, benefits, and

risk sections can be used to determine a return on investment, net present value, and payback

period.

Table 30 and Table 31, below, show the risk-adjusted cost and benefit values, applying the risk

adjustment method indicated in the Risk Quantification Section and the values from Table 25 and

Table 28 to the numbers in Table 9 and Table 16, respectively.

Table 30: Total Risk-Adjusted Costs

Table 31: Total Risk-Adjusted Benefits


Items Year 1 Year 2 Year 3 TotalP1 Planning & Design Cost (E3)P2 Implementation Cost (F3)P3 Support Staff Cost (L3)P4 Hybrid Cloud Cost (J19)P5 Total Risk-Adjusted Costs

Ref.Initial Outlay

Present Value

$15 481 $15 481 $15 481$31 563 $47 345 $78 908 $74 604

$372 204 $383 370 $394 871 $1 150 445 $951 854$156 170 $280 073 $320 691 $320 691 $1 077 626 $916 739$203 214 $699 622 $704 061 $715 563 $2 322 460 $1 958 679

Items Year 1 Year 2 Year 3 TotalQ1 Operational Benefits (O1)Q2 Engineer Productivity Benefits (O2)Q3 Manager Productivity Benefits (O3)Q4 Total Risk-Adjusted Benefits

Ref.Present Value

$467 236 $471 083 $475 046 $1 413 364 $1 170 969$473 025 $487 216 $501 832 $1 462 073 $1 209 689

$16 457 $16 950 $17 459 $50 865 $42 085$956 717 $975 249 $994 337 $2 926 303 $2 422 742


6.6 Financial Analysis Conclusion

The financial analysis provided in this CBA study illustrates the potential way in which an

organization can evaluate the value proposition of a cloud computing solution in general and the

AWS cloud in particular. Based on information collected in the in-depth interview with the ICT staff

of GEC, I calculated some key financial metrics that are summarized in Table 32. All final estimates

are risk-adjusted to incorporate potential uncertainty in the calculation of costs and benefits.

With the AWS hybrid cloud solution, GEC can achieve substantial benefits in several areas.

• Reduce operational costs

• Reduce hardware costs

• Reduce the electricity bill

• Improve the overall efficiency of the software development and test computing

environment for the software R&D activities of GEC

• Improve the productivity of the ICT staff as well as the engineers working on software

product development, test and qualification

6.7 Key Financial Metrics

The table below summarizes the key financial metrics findings for the CBA study.

Table 32: Key Financial Metrics, Original and Risk-Adjusted


Items Risk AdjustedROI three-year 1131% 297%Payback (months) 3 9Total three-year costs (PV)Total three-year benefits (PV)Total three-year net savings (NPV)IRR 396% 117%

Unadjusted (best case)

$1 854 526 $1 958 679$3 515 030 $2 422 742$1 660 504 $464 063


Table 33: Internal Rate of Return, Payback and ROI calculation (Non-Risk-Adjusted)

Table 34: Internal Rate of Return, Payback and ROI calculation (Risk-Adjusted)

The return on investment (ROI) over the three-year period is calculated by :

ROI = ( (Savings - Initial Outlay) / Initial Outlay) *100

The internal rate of return (IRR) is defined as the discount rate which makes NPV = 0, that is a

$0 net savings for this project. Finding the IRR is important, because it indicates at what discount

rate (currently 10% in the context of this analysis) the project becomes unattractive and therefore

should be rejected. Finding the risk-adjusted IRR for this project must solve the following equation:

NPV = -$203,214 + $257,095/(1 + IRR) + $271,188/(1 + IRR)² + $278,774/(1 + IRR)³ = 0

Solving this equation using trial and error computations is time consuming and error prone so it

is calculated using the IRR() function of the spreadsheet calculator (OpenOffice Calc). Since the

risk-adjusted IRR value (117%) is significantly superior to the current discount rate (10%), we

should be confident that the project will be successful and therefore should be accepted. To further

illustrate these findings, I provide below a graphic representation that shows the evolution of the

cumulative cash flow and payback point in time that roughly occurs at the end of the first year.


Initial Year 1 Year 2 Year 3 IRR Payback ROICostsBenefitsSavings 396,00% 2,93 1131%

-$180 890 -$668 432 -$670 056 -$681 792$1 384 327 $1 415 152 $1 446 901

-$180 890 $715 895 $745 096 $765 109

Initial Year 1 Year 2 Year 3 IRR Payback ROICostsBenefits

Savings 117,09% 9,06 297%

-$203 214 -$699 622 -$704 061 -$715 563$956 717 $975 249 $994 337

-$203 214 $257 095 $271 188 $278 774


Table 35: Cash Flow Over Three-Year Period (Non-Risk-Adjusted)

Table 36: Cash Flow Over Three-Year Period (Risk-Adjusted)

Table 37: Graphic Representation of Cash Flow Over Three-Year Period (Risk-Adjusted)


Initial Year 1 Year 2 Year 3 Total$0

Costs (PV)Benefits (PV)Cash Flow (PV)

Carryforward $650 820 $1 266 567-$180 890 -$607 672 -$553 734 -$512 230 -$1 854 526

$1 258 492 $1 169 482 $1 087 057 $3 515 030$650 820 $1 266 567 $1 841 394

Initial Year 1 Year 2 Year 3 Total

Costs (PV)Benefits (PV)

Cash Flow (PV)

Carryforward -$203 214 $30 511 $254 621-$203 214 -$636 026 -$581 836 -$537 602 -$1 958 679

$869 751 $805 946 $747 045 $2 422 743

-$203 214 $30 511 $254 621 $464 064

Initial Year 1 Year 2 Year 3

-$800,000

-$600,000

-$400,000

-$200,000

$0

$200,000

$400,000

$600,000

$800,000

$1,000,000

Cumulative Cash Flow

Costs (PV)Benefits (PV)Cash Flow (PV)


7 Conclusion

The CBA of the migration project for the software development and test activities at GEC to the

AWS cloud shows positive financial results. However, it was not possible to demonstrate that the

assumed environmental benefits of cloud computing played a sensitive role there. By migrating

parts of the computing resources of the datacenter to the AWS cloud, the financial analysis

demonstrated that GEC could achieve significant cost savings in areas of hardware equipment costs,

electricity consumption costs for the servers' power and cooling, as well as in user productivity

gained from the better effectiveness of the hybrid cloud solution. The financial analysis shows that

GEC could obtain a risk-adjusted return on investment (ROI) of 117%, with a payback period of 9

months, by migrating its software R&D's development and test activities to the AWS cloud.

However, the initial environmental benefits assumption about cloud computing―resulting from a

higher computing efficiency―could not be objectively quantified in the analysis. Failure do to so,

can be explained through two main reasons:

Firstly, it is not argued that cloud computing can save billions of kW-hours in energy

consumption because cloud providers can squeeze the performance and efficiency of their

infrastructures at much higher levels than private datacenters, especially when compared to those of

small firms of limited innovation and cash resources. But while the energy efficiency benefits of

cloud computing are generally not contested, claiming that cloud computing is a green technology

is a totally different story, as reported by a number of ICT practitioners and ONGs like Greenpeace.

Despite the fact that some cloud providers are reaching extremely low PUEs, and are also looking

to build massive datacenters in places so as to maximize energy efficiency and harness renewable or

clean energy, the primary motivation is cost containment, which doesn't necessarily meet

environmental and social responsibility objectives. The study showed that while energy efficiency

reduces the energy consumption footprint, it is not green if cloud providers are simply looking at

maximizing output from the cheapest and dirtiest source of energy available, such as Microsoft's

Chicago cloud who supplies power to its datacenter from a coal-burning electricity grid.

Secondly, the current body of environmental legislations that are enacted by governments and

regulatory organizations, that apply to the ICT sectors, are not to a large extent quantifiable in

financial terms. This observation I think is coherent with the findings of this study and coherent



with the common perception that the economics of green IT are stimulated primarily by the concern

of cutting costs in areas of energy-related expenses as well as hardware and maintenance expenses.

In other words, “do the right thing for the environment” is not sufficiently rewarded by today's

legislations as we saw in section 4.1.3 “Energy Policies and Implication”. For example, the EU

Emission Trading Scheme (ETS) that regulates the emission of greenhouse gases for the energy

sector and other heavy energy consuming industries is not enforceable (yet) to the ICT industry

sectors. With regard to energy policies that are of importance to the ICT industry sectors, including

the EU Energy Performance of Buildings Directive, the EC Code of Conduct on Data Centers

Energy Efficiency, and the Grenelle of the Environment for France, have had, so far, minor to zero

financial impacts for the datacenter sector. All this may change in the future, but at the time of this

writing it is the current state of business.



8 Future Research Implications

An interesting future research objective would be to revisit this CBA when enforceable

environmental laws applicable to the ICT sectors are enacted. A change in the European legislative

landscape including the Carbon Trading Scheme, and the introduction of effective tax incentives for

those enterprises that comply with the EC Code of Conduct requirements, will affect the result of

the financial analysis with respect to the quantification and monetary valuation of the environmental

benefits. I think it is important to keep an eye on the enactment of similar environmental laws in the

US and in emerging countries like India and China because these fast-growing economies are

concerning prospects of GHG emission increases. To echo Greenpeace's concerns about cloud

computing's possible negative impact on the environment, it may prove of capital importance to dig

further into the issue of how big the cloud really is when it comes to electricity consumption and

GHG emissions and how big it will become given its rapid growth and given that many major cloud

brands refuse to disclose their energy footprint.

Another issue worth investigating further concerns the extent to which European economies are

becoming increasingly dependable upon US-centric firms like Google and Microsoft for the

procurement of computing resources when the cloud as a utility computing grid becomes

ubiquitous.

A corollary business sustainability issue related to the widespread use of cloud computing for

the firm's business processes resides in the diffuse control of the Internet as the broadband conduit

linking datacenters together, and the relative fragility of its architecture. Lawrence G. Roberts, one

of the founders of the Internet, says, in an address to the IEEE organization, that the Internet is

broken, and that network routers are too slow, costly, and power hungry (Roberts 2009). Today's

Internet traffic is rapidly expanding and also becoming more varied and complex in particular due

to an explosion in voice and video traffic. The shift is not without causing problems, he says, even

though everybody is using Skype or YouTube today without too much of a hitch, because the packet

switching technology at the heart of the Internet's TCP/IP protocol was not designed for that type of

application. Packet switching routers around the world are becoming increasingly congested,

causing quality of service deteriorations. This may not be perceivable today because the Internet has

been grossly over-provisioned by network operators who have deployed mountains of optical fibers



during the dot-com era, but at the current rate of growth, cloud computing combined with the

massive arrival of the iPad, iPhone, netbooks and other tablet computers, may put the viability of

the Internet at risk. The resulting effects would be devastating for those enterprises who rely heavily

on cloud computing to perform their business operations.



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Appendix I: Amazon Web Services (AWS)

Elastic Compute Cloud (Amazon EC2)

Amazon EC2’s simple web service interface allows enterprises to obtain and configure capacity

with minimal friction. It provides enterprises with complete control of their computing resources

that run on Amazon’s proven computing environment. Amazon EC2 reduces the time required to

obtain and boot new server instances to minutes, allowing enterprises to quickly scale capacity, both

up and down, as the enterprise's computing requirements change. Amazon EC2 changes the

economics of computing by allowing enterprises to pay only for capacity actually used. Amazon

EC2 provides developers the tools to build failure resilient applications and isolate themselves from

common failure scenarios.63

Amazon EC2 Instances Characteristics

Amazon Elastic Compute Cloud (Amazon EC2) provides the flexibility to choose from a

number of different instance types to meet customer's computing needs. Each instance provides a

predictable amount of dedicated compute capacity and is charged per instance-hour consumed.

The amount of CPU that is allocated to a particular instance is expressed in terms of EC2

Compute Units. Amazon says that one EC2 Compute Unit provides the equivalent CPU capacity of

a 1.0-1.2 GHz 2007 Opteron or 2007 Xeon processor. This is also the equivalent to an early-2006

1.7 GHz Xeon processor referenced in Amazon's original documentation.

63 http://aws.amazon.com/ec2/



Instance Model Characteristics Pricing

Standard Instances

Instances of this family are well suited for most applications.

Small Instance (default)64 1.7 GB memory

1 EC2 Compute Unit (1 virtual core with 1 EC2

Compute Unit)

160 GB instance storage (150 GB plus 10 GB root

partition)

32-bit platform

I/O Performance: Moderate

See below

Large Instance 7.5 GB memory

4 EC2 Compute Units (2 virtual cores with 2 EC2

Compute Units each)

850 GB instance storage (2×420 GB plus 10 GB root

partition)

64-bit platform

I/O Performance: High

See below

Extra Large Instance 15 GB memory

8 EC2 Compute Units (4 virtual cores with 2 EC2

Compute Units each)

1,690 GB instance storage (4×420 GB plus 10 GB root

partition)

64-bit platform


See below

High-Memory Instances

Instances of this family offer large memory sizes for high throughput applications, including

database and memory caching applications

High-Memory Double

Extra Large Instance

34.2 GB of memory

13 EC2 Compute Units (4 virtual cores with 3.25 EC2

Compute Units each)

850 GB of instance storage

See below

64 Default and original Amazon EC2 instance type that has been available since Amazon EC2 launch. Corresponds to a

1.0-GHz x86 ISA Virtual Machines (a.k.a a slice)



64-bit platform


High-Memory

Quadruple Extra Large

Instance

68.4 GB of memory


Compute Units each)


64-bit platform


See below

High-CPU Instances

Instances of this family have proportionally more CPU resources than memory (RAM) and are

well suited for compute-intensive applications.

High-CPU Medium

Instance

1.7 GB of memory


Compute Units each)


32-bit platform

I/O Performance: Moderate

See below

High-CPU Extra Large

Instance

7 GB of memory


Compute Units each)


64-bit platform


See below

Table 38: Amazon EC2 Instance Types (Source Amazon)

Amazon EC2 Pricing

Amazon EC2 proposes three different purchasing options:

1. On-Demand Instances: On-Demand Instances let customers pay for compute capacity by

the hour with no long-term commitments or upfront payments. Customers can increase or

decrease their compute capacity depending on the demands of their application and only pay

the specified hourly rate for the instances they use. On-demand instances are used mostly for

short term workloads and for workloads with unpredictable resource demand characteristics.



2. Reserved Instances: Reserved Instances let customers make a low, one-time, upfront

payment for an instance, reserve it for a one or three year term, and pay a significantly lower

rate for each hour that instance run. Customers are assured that a Reserved Instance will

always be available in the Availability Zone in which it is purchased. These instances are

used for longer running workloads with predictable resource demands.

3. Spot Instances - Spot Instances allow customers to specify the maximum hourly price that

they are willing to pay to run a particular instance type. AWS sets a Spot Price for each

instance type in each region, which is the price all customers will pay to run a Spot Instance

for that given hour. The Spot Price fluctuates based on supply and demand for instances, but

customers will never pay more than the maximum price they have specified. These instances

are used for workloads with flexible completion times.

The figures below presents the cost to run private and public amazon Machine Instances (AMIs)

on the specified operating system (Linux/Unix or Windows) in the EU (Ireland) region. Each partial

instance-hour consumed is billed as a full hour.

On-Demand Instances Pricing

Reserved Instances Pricing


Illustration 17: Amazon EC2 On-DEmand Instances Pricing


Spot Instances Pricing

Spot Instances pricing fluctuates periodically depending on the supply of and demand for Spot

Instance capacity. The illustration below takes a snapshot pricing for the EU Region at Wednesday


Illustration 18: Amazon EC2 Reserved Instances Pricing

Illustration 19: Amazon EC2 Spot Instances Pricing


January 13 10:28:06 UTC 2010.

Internet Data Transfer Pricing

The pricing below is based on data transferred "in" and "out" of Amazon EC2.

There is no Data Transfer charge between Amazon EC2 and other Amazon Web Services within

the same region (i.e. between Amazon EC2 US West and Amazon S3 in US West). Data transferred

between Amazon EC2 instances located in different Availability Zones in the same Region will be

charged Regional Data Transfer. Data transferred between AWS services in different regions will be

charged as Internet Data Transfer on both sides of the transfer.

(*)Data Transfer In will be $0.10 per GB after June 30, 2010.

Amazon Elastic Block Storage (EBS) Pricing


Illustration 20: Amazon Internet Data Transfer Pricing

Illustration 21: Amazon Elastic Block Storage (EBS) Pricing


AWS Import/Export Service

AWS now offers physical data import/export service makes it easy to quickly transfer large

amounts of data into and out of the AWS Cloud. It is an economical alternative to sending large

volumes of data across the Internet. The AWS Import/Export service allow 2TB of data to be

imported or exported globally from AWS S3. With that service, customers can send Amazon a blank

storage device and Amazon will copy the contents of one or more Amazon S3 buckets to it before

shipping it back. Alternatively, customers can send Amazon a storage device full of data that

Amazon will copy it to the S3 buckets of the customer's choice. Customers can use AWS

Import/Export for:

• data Migration

• Offsite Backup

• Direct Data Interchange

• Disaster Recovery

When to consider AWS Import/Export?

When loading the enterprise's data over the Internet takes a week or more. Below is a theoretical

minimum number of days to transfer 1TB at 80% of network capacity through the Internet65:

Connection speed to the

Internet

Number of days to transfer 1TB Volume at which the transfer

will take a week or more

T1 (1.544Mbps) 82 days 100 GB or more

10Mbps 13 days 600GB or more

T3 (44.736Mbps) 3 days 2TB or more

100Mbps 1 to 2 days 5TB or more

1000Mbps Less than 1 day 60TB or more

Table 39: Number of days to transfer 1TB per connection speed (Source: Green datacenter Blog66)

65 http://www.greenm3.com/2009/12/amazon-web-services-adds-global-physical-data-shipping-and-receiving-to-

Cloud-computing-services.html

66 See: http://www.greenm3.com/2009/12/amazon-web-services-adds-global-physical-data-shipping-and-receiving-to-

Cloud-computing-services.html



Appendix II: Gartner's Hype Cycle Explained

Gartner's Hype Cycles help technology planners to decide when to invest in that technology. A

Hype Cycle is a useful educational tool that:

• Establishes the expectation that most technologies will inevitably progress through the

pattern of over-enthusiasm and disillusionment before proving their real value.

• Provides a snapshot of the relative level and pace of maturity of technologies within a

certain segment of the IT world, such as a technology area, horizontal or vertical business

market, or a certain demographic audience.

• Has a simple and clear message: companies should not invest in a technology just because it

is being hyped, nor should they ignore a technology just because it is not living up to early

over-expectations.

The Hype Cycle Graphic

Gartner's Hype Cycle, introduced in 1995, characterizes the typical progression of an emerging


Illustration 22: Gartner Hype Curve (Source: Gartner)


technology, from over-enthusiasm through a period of disillusionment to an eventual understanding

of the technology's relevance and role in a market or domain Since then, the use of Hype Cycles has

expanded, within Gartner and by our clients, as a graphical way to track multiple technologies

within an IT domain or technology portfolio. Gartner's Hype Cycle characterizes the typical

progression of an emerging technology, from over-enthusiasm through a period of disillusionment

to an eventual understanding of the technology's relevance and role in a market or domain. Each

phase is characterized by distinct indicators of market, investment and adoption activities as

described in the two figures below:

A technology passes through several stages on its path to productivity:

• Technology Trigger: The Hype Cycle starts when a breakthrough, public demonstration,

product launch, or some other event generates press and industry interest in a technology

innovation. The technology may have been under development for quite some time, but at

this point it reaches a stage where word of its existence and excitement about its possibilities

extend beyond the close confines of its inventors or developers. Increasingly people hear of

its potential, and a wave of "buzz" quickly builds as everyone wants to be the first to pass on

the news. This social "market" interaction starts to influence the path of the innovation.

• Peak of Inflated Expectations: Companies that like to be ahead of the curve seek out the

technology and jump on it before their competitors. The suppliers of the technology boast

about their early prestigious customers, and other companies want to join in so they aren't

left behind. A bandwagon effect kicks in, and the technology is pushed to its limits as

companies try it out in a range of settings. At this point, the technology is viewed as a

panacea, with little regard to its suitability for each application. Stories in the press capture

the excitement around the technology and reinforce the need to become a part of it or be left

behind.

• Trough of Disillusionment: As time passes, impatience for results begins to replace the

original excitement about potential value. The same few stories of early success have been

repeated over and over, but now a deeper look often shows those same companies still

struggling to derive meaningful value. Problems with performance, slower-than-expected

adoption, or a failure to deliver financial returns in the time anticipated all lead to missed



expectations. Many of these failures center around inappropriate uses of the technology.

• Slope of Enlightenment: Some early adopters overcome the initial hurdles, begin to

experience benefits, see the "light at the end of the tunnel," and recommit efforts to move

forward. Drawing on the experience of early adopters, understanding grows about where the

technology can be used to good effect and, just as importantly, where it brings little or no

value. Over time, the technology matures as suppliers improve products on the basis of early

feedback. Methodologies for applying it successfully are codified, and best practices for its

use are socialized.

• Plateau of Productivity: With the real-world benefits of the technology demonstrated and

accepted, growing numbers of organizations feel comfortable with the now greatly reduced

levels of risk. A sharp uptick ("hockey stick") in adoption begins, and penetration accelerates

rapidly as a result of productive and useful value.

Gartner analysts position technologies along the Hype Cycle based on a consensus assessment

of hype and maturity. To represent the varying speeds, all technologies on the Hype Cycle are


Illustration 23: Gartner's Hype Curve Breakdown (Source: Gartner)


assigned to a "years to mainstream adoption" category (for example, two to five years), representing

how long they will take to reach the Plateau of Productivity from their current position on the Hype

Cycle — that is, how far they are from the start of mainstream adoption.

For more detailed information on the causes, traps and opportunities of the Hype Cycle, see

"Understanding Gartner's Hype Cycles, 2009" and the "Mastering the Hype Cycle" book.

The Priority Matrix Graphic

The Gartner Hype Cycle is widely used to provide a snapshot of a set of technologies in terms of

their level of hype and their rate of maturation. The Hype Cycle is an excellent educational tool to

show business and other executives the common pattern of excitement and subsequent

disillusionment that inevitably accompanies new technology.

However, for internal planning and the prioritization of emerging technologies, technology

planners must look beyond the hype and assess technology opportunities in terms of their relative

impact on the enterprise. A useful graphical tool for presenting this information is the Priority

Matrix as shown in Exhibit 3.



Appendix III: Gartner's Hype Cycle for Emerging Technologies

in 2009


Illustration 24: Hype Cycle for Emerging Technologies, 2009 (Source Gartner)


Appendix IV: Gartner's Hype Cycle for Cloud Computing in

2009


Illustration 25: Hype Cycle for Cloud Computing, 2009 (Source: Gartner)


Appendix V: Gartner's Priority Matrix for Cloud Computing,

2009

In the Priority Matrix, the vertical axis focuses on the potential benefit of the technology (rather

than on the expectation levels presented in the Hype Cycle).

Options for the benefit rating are presented in the Priority Matrix as follow:

• Transformational: Enables new ways of doing business across industries that will result in

major shifts in industry dynamics


Illustration 26: Gartner's Priority Matrix for Cloud Computing, 2009 Source: Gartner)


• High: Enables new ways of performing horizontal or vertical processes that will result in

significantly increased revenue or cost savings for an enterprise

• Moderate: Provides incremental improvements to established processes that will result in

increased revenue or cost savings for an enterprise

• Low: Slightly improves processes (for example, improved user experience) that will be

difficult to translate into increased revenue or cost savings.

The horizontal axis groups the technologies according to the same years-to mainstream adoption

rating used on the Hype Cycle.

High-priority investments are in the top left of the Priority Matrix, where the technologies will

potentially have a high impact and have reached a reasonable level of maturity. Companies that are

conservative in their technology adoption (Type C organizations) may limit their focus to this area.

Companies that are more-aggressive technology adopters (Type A and Type B organizations) are

likely already using technologies that will mature in less than two years.

Therefore, they will probably want to evaluate technologies further to the right or lower on the

Priority Matrix — for example, technologies that will not be in widespread use for at least five

years but that may provide a competitive edge in the interim.



Glossary of Terms

Terms Definitions

Interoperability Interoperability is concerned with the ability of systems to communicate. It

requires that the communicated information is understood by the receiving

system. In the world of cloud computing, this means the ability to write code

that works with more than one cloud provider simultaneously, regardless of

the differences between the providers 67

Portability Portability is the ability to run components or systems written for one

environment in another environment. In the world of cloud computing, this

includes software and hardware environments (both physical and virtual).

Integration Integration is the process of combining components or systems into

an overall system. Integration among cloud-based components and systems

can be complicated by issues such as multi-tenancy, federation and

government regulations.

Service Level

Agreement (SLA)

An SLA is contract between a provider and a consumer that specifies

consumer requirements and the provider’s commitment to them. Typically an

SLA includes items such as uptime, privacy, security and backup procedures.

Federation Federation is the act of combining data or identities across multiple

administrative domains. Federation can be done by a cloud provider or by a

cloud broker.

Broker A broker has no cloud resources of its own, but matches consumers and

providers based on the SLA required by the consumer. The consumer has no

knowledge that the broker does not control the resources.

Multi-Tenancy Multi-tenancy is the property of multiple systems, applications or data from

different enterprises hosted on the same physical hardware. Multi-tenancy is

common to most cloud-based systems.

Cloud bursting Cloud bursting is a technique used by hybrid clouds to provide additional

resources to private clouds on an as-needed basis. If the private cloud has the

67 The definitions of interoperability, portability and integration are based on the work at

http://www.testingstandards.co.uk/interop_et_al.htm



Terms Definitions

processing power to handle its workloads, the hybrid cloud is not used.

When workloads exceed the private cloud’s capacity, the hybrid cloud

automatically allocates additional resources to the private cloud.

Policy A policy is a general term for an operating procedure. For example, a

security policy might specify that all requests to a particular cloud service

must be encrypted.

Governance Governance refers to the controls and processes that make sure policies are

enforced.

Virtual Machine

(VM)

A file (typically called an image) that, when executed, looks to the user like

an actual machine. Infrastructure as a Service is often provided as a VM

image that can be started or stopped as needed. Changes made to the VM

while it is running can be stored to disk to make them persistent.

Application

Programming

Interface (API)

An application programming interface (API) is a contract that tells a

developer how to write code to interact with some kind of system. The API

describes the syntax of the operations supported by the system. For each

operation, the API specifies the information that should

Discount rate The interest rate used in cash flow analysis to take into account the time

value of money. Although the Federal Reserve Bank sets a discount rate,

companies often set a discount

rate based on their business and investment environment. Organizations

typically use discount rates between 8% and 16% based on their current

environment.

Net present value

(NPV)

The present or current value of (discounted) future net cash flows given an

interest rate (the discount rate). A positive project NPV normally indicates

that the investment should be made, unless other projects have higher NPVs.

Present value (PV) The present or current value of (discounted) cost and benefit estimates given

at an interest rate (the discount rate). The PV of costs and benefits feed into

the total net present value of cash flows.

Payback period The payback period is the breakeven point for an investment — the point in

time at which net benefits (benefits minus costs) equal initial investment or



Terms Definitions

cost.

Return on

investment (ROI)

A measure of a project’s expected return in percentage terms. ROI is

calculated by dividing net benefits (benefits minus costs) by costs.

Carbon Dioxide A naturally occurring gas, and also a by-product of burning fossil fuels and

biomass, as well as land-use changes and other industrial processes. It is the

principal anthropogenic greenhouse gas that affects the Earth's radiative

balance. It is the reference gas against which other greenhouse gases are

measured and therefore has a Global Warming Potential (GWP) of 1

Carbon Dioxide

Equivalent

A metric measure used to compare the emissions from various greenhouse

gases based upon their global warming potential (GWP). Carbon dioxide

equivalents are commonly expressed as "million metric tons of carbon

dioxide equivalents (MMTCO2Eq)." The carbon dioxide equivalent for a gas

is derived by multiplying the tons of the gas by the associated GWP. The use

of carbon equivalents (MMTCE) is declining.

MMTCO2Eq = (million metric tons of a gas) * (GWP of the gas)


a cloud computing cost-benefit analysis assessing green it benefits

Documents