it infrastructure refresh planning for enterprises- a business process perspective

8/10/2019 IT Infrastructure Refresh Planning for Enterprises- A Business Process Perspective

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IT infrastructure refresh planningfor enterprises: a business

process perspectiveTugrul U. Daim

Portland State University, Portland, Oregon, USA

Matthew Letts, Mark Krampits, Rabah Khamis andPranabesh Dash

Intel Corp, Portland, Oregon, USA

Mitali MonalisaIntel Corp, Hillsboro, Oregon, USA, and

Jay JusticeMotorola Mobility, Beaverton, Oregon, USA

Abstract

Purpose This paper aims to research literature to describe the business processes used whenplanning IT infrastructure refreshes.

Design/methodology/approach The paper uses analytical hierarchical process (AHP) andpairwise comparisons to model and quantify the decision process for IT infrastructure refreshes.

Findings The research found that most companies keep their refresh processes private and verylittle academic research is available on this topic. While supportability, manageability, compatibility,cost, and scalability are important factors to large organizations, performance and availability of thesystems are important for smaller organizations.

Originality/value AHP was not ever used to evaluate the refresh planning. The paperdemonstrates that it would be a very useful tool.

KeywordsBusiness process re-engineering, Information technology, Analytical hierarchy process

Paper typeLiterature review

IntroductionThe rapid growth of computing technologies such as cloud computing andvirtualization is driving companies to refresh their IT infrastructure, sometimessooner than planned, in order to support these new technologies (Gartner Inc., 2009). Inmid to large enterprises with a complex IT infrastructure, a refresh can be costly andrequire careful planning in order to get the largest return on investment over time. The

authors of this paper researched literature to describe the business processes usedwhen planning IT infrastructure refreshes. Our research found that most companieskeep their refresh processes private and very little academic research is available onthis topic.

This paper uses the analytical hierarchical process (AHP) decision-making modelto help companies plan their IT infrastructure refresh. The model is intended to have theflexibility for any IT organization regardless of its size or strategic alignment. The modeluses pairwise comparison in order to tailor the criteria to the specific IT organization.

The current issue and full text archive of this journal is available at

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Business Process Management

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Vol. 17 No. 3, 2011

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q Emerald Group Publishing Limited

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DOI 10.1108/14637151111136397


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When implemented, the model will recommend one of seven possible strategies torefresh IT infrastructure.

Literature reviewThere are two important aspects to this paper the AHP model and applying it to an ITinfrastructure refresh. In researching these two areas, we found plenty of literature ondecision-making models, specifically AHP, but very little academic research has beenconducted on the processes used by businesses to update their IT hardware. Ourliterature review will start with an overview of AHP and conclude with the researchfound on refreshing IT infrastructures.

Saaty and Kearns describe the AHP as a decision-making framework for dealingwith problems with multiple criteria to be considered (Saaty and Kearns, 1985).

The AHP model consists of four stages (Roper-Lowe and Sharp, 1990):

. The first stage, building the decision hierarchy, is the most important stage as itsets the framework for the rest of the process. At the highest level is the goal to

accomplish. To reach that goal, a number of criteria need to be met; these arerepresented in the second tier. If necessary, the criteria can be further divided intosub-criteria. At the bottom of the hierarchy are the possible alternatives to beconsidered to meet the goal at the top.

. In the second stage, values are assigned to each criterion. This weighting can bedone one of two ways using quantitative engineering requirements or using aqualitative method that can turn preferences into values such as pairwisecomparison (Kocaoglu, 1983).

. The third stage is scoring each alternative with respect to each criterion. Thisrequires finding people qualified to make judgments, i.e. a finance person couldscore the alternatives with relation to cost but an engineer would be needed to

judge the technical specifications (Roper-Lowe and Sharp, 1990).. The final stage is applying the weights to the alternatives to obtain an overall

score and determine the preferred option to reach the goal.

In stages two and three above, a technique called pairwise comparison is used to find theweighting of criteria and alternatives. There areseveralmodels that can be used to do thecomparisons but typically an expert panel is used. The expert panel method iscommonly used when there are intangible criteria to be considered in the decision(Roper-Lowe and Sharp, 1990). One of the disadvantages of using the weights derivedfrom pairwise comparison is that it assumes a linear relationship between the value andthe user preferences. Gerdsri and Kocaoglu (2007) suggest using desirability curves thatpresent a higher resolution of the users preferences could solve this problem. However,

creating the curves requires a detailed dataset for each criterion and in the interest oftime linear weights were used in this model.

Interactions between criteria and sub-criteria can change depending on theapplication of the AHP. In some situations, it makes sense to weigh all criteria againsteach other but in others, direct comparisons would not make sense. In order toaccommodate this, AHP models have the flexibility to handle both situations. Gerdsriand Kocaoglu (2007) show in Figure 1 the application of a three-level decision modelwhere criteria are selected such that they are independent of each other and have no

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interaction with another group. This simplification makes the model very practical touse as judgment quantification experts are not asked to compare two sub-criteria thatbelong to different groups and difficult to evaluate.

AHP is a very effective and robust tool as evidenced by its widespread use (Chenand Kocaoglu, 2008). The following list is provided as a sample of AHP applications:

. Sevkliet al. (2008) supplier selection.

. Hafeez and Essmail (2007) evaluating core competences.

. Bhagwat and Sharma (2007) performance measurement.

. Al-Subhi and Al-Harbi (2001) project management.

. Karami (2006) adoption of irrigation methods.

. Leung et al. (1998) evaluating fisheries management.

. Wong and Li (2008) intelligent building systems.

In researching the strategies companies use to refresh their IT infrastructure,specifically server systems, there are not many academic studies available. A commontheme in the articles was a need for better planning in regards to IT. Management finds itdifficult to devote resources to improving infrastructure because it is hard to correlate tobusiness needs and often considered immeasurable (Duncan, 1995). Weill (1992)presented studies that show a correlation between an increase in business performanceand investment in IT infrastructure; meaning that investment in IT could be a critical

factor in competing with other companies.However, spending resources on new IT infrastructure to keep a competitive

advantage without a plan could lead to high costs in the future. Brill (2007) found thatless than 20 percent of 100 data center operators surveyed felt that their infrastructureplanning procedures were above average. In his report, Brill found that theinfrastructure costs of power, cooling, and space will exceed the initial cost of the IThardware in a three-year period. This means that any new IT deployments that only lookat the cost of hardware could be completely inaccurate. One of the solutions Brill (2007)

Figure 1.AHP model withindependent criteria

Objective

(O)

C1

F11

F12

T1

Source:Gerdsri and Kocaoglu (2007)

T2 T3 TN

F13

F1 F2

F23

F22 F32

F21F31

Fjk1 Fjk2FjkK

C2

Strategic technology evaluation

C3 CkCriteria(Cb)

Technologies

(Tn)

Factors

(Fnk)

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suggests is virtualizing older, inefficient hardware onto a single more powerful server,thereby reducing the power requirements.

Our research showed that the AHP is a useful tool when making decisions withtangible and intangible criteria to consider. In order to keep the intangible and tangible

factors separate from each other, we will create a four-level decision hierarchy withindependent criteria and use pairwise comparison to obtain the weighting of our criteria.

We have also shown that implementing an efficient and flexible IT infrastructurecan be a strategic advantage over competitors but there are many factors to consider inorder to get the best return on investment.

MethodologyThis section explains the research stages used in this paper. The first step was todevelop the hierarchy. A set of industry experts was used for this. Then, in parallelstages, the model was applied to two cases.

Expert panelAn expert panel made up of the authors was used to determine the criteria andsub-criteria required to choose a strategy to refresh the servers in an IT infrastructure.The expert panel consisted of seven individuals from the high-tech industry withexperience ranging from five to 27 years. The job roles of the experts included: seniordatabase engineers, enterprise technical marketing, and validation engineers. Thechoices of criteria and pairwise comparisons only had small inconsistencies betweenthe experts and were deemed negligible. If the inconsistencies were large then a moreadvanced method such as Delphi would have been used to reach a consensus.

Decision hierarchyThe primary requirement used when creating the hierarchy was to keep it flexible

enough to be applicable from small to large businesses. Based on experiences from theexpert panel, we found that, regardless of size, businesses consider the same criteriawhen updating their IT infrastructure. Two of the experts used were involved in severrefresh decision making in the past and helped validate our criteria.

Once the criteria were defined, the expert panel identified possible strategies toevaluate. Determining the possible alternatives for the lower level of the hierarchyturned out to be the most difficult part. There are too many possible strategies torefresh servers so the expert panel narrowed the scope to seven possible alternativesthat would represent the majority of the strategies.

ApplicationThe decision hierarchy should be applied on a regular basis to determine is a server

refresh would have a positive return on investment and, if so, what strategy would bestalign with company preferences. In order to apply the model, weights representing thecompanys preferences need to be applied to the criteria. One of the ways to get a weightis to take a survey of employees in relevant decision-making positions. In a smallbusiness, this could be an IT manager and database administrator while in a largecompany there could be multiple decision makers from groups such as management,engineering, or finance. In order to reduce the time needed to compile the results of asurvey, it is recommended to create an electronic version so the results can be


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calculated automatically. This also gives participants instant feedback on how theirresponses compare to others similar to the Delphi model. Once the weighting has beenapplied, the company can evaluate the possible alternatives and determine which thepreferred option is to refresh their servers.

Model developmentAs discussed previously, the decision hierarchy contains four levels, the goal, criteria,sub-criteria, and the seven alternative strategies. Starting at the top is the goal tochoose a server refresh strategy. All pairwise comparisons of the criteria andalternatives are done with respect to this goal.

The second level of the hierarchy is the criteria. Each of the criteria in this levelrequires a weight that calculates into scoring the alternatives. The values of theseweights are found through pairwise comparisons of each criterion by an expert panel inthe company considering a server refresh. The four criteria identified in this model are:reliability availability serviceability (RAS), performance, total cost of ownership (TCO),

and roadmap.The third level in the hierarchy contains the sub-criteria for each criterion. Thesub-criteria under each criterion are independent from sub-criteria under anothercriterion, i.e. the sub-criteria under performance cannot be compared to the sub-criteriaunder roadmap. The total contribution of any factor for any alternative is calculated bythe product of the criterion weight and the sub-criterion weight. For example, if thepairwise comparison at the criteria level for RAS indicates a 20 percent weight and thepairwise comparison for serviceability indicates a 30 percent weight, the final weightfor serviceability will be 20 *30 percent 6 percent. Descriptions of the sub-criteria canbe found in Appendix 1. The complete model is shown in Figure 2.

The final layer of the model is the actions which include the possible strategicalternatives. In order to derive the preference scores for each alternativem several

assumptions were made. In a real-world application of this model, the actualspecifications

Figure 2.Server refreshdecision hierarchy

Hardware

upgrade

Full

replacement

Serviceability Reliability Performance/

wattTransactions Recurring

cost Initial cost Scalability

Legacy

support

Service life UpgradabilityDensity

Availability

Partial

replacement New addition Virtualization Software

upgradeDelay refresh

Roadmap

Total cost of

ownership

(TCO)

Performance

Evaluating server refresh strategies

Reliability availability

serviceability (RAS)

Actions

Goal

Manageability

Sub-criter

ia

Criteria

Sub-criter

ia

Criteria

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can be found through research such as benchmarks or retail pricing. The following are theassumptions made in this paper in order to test the model:

. virtualization has not yet been implemented;

.

current servers have one year left of service life, while any new servers will havea three-year service life;

. new servers are twice as energy efficient as old servers;

. new servers have four times the processing power and memory as old servers;

. no previous software or hardware upgrades have been performed on the existingservers; and

. the new servers will only take up half the rack space as the old servers.

Based on the assumptions above, the following seven strategic alternatives wereidentified:

(1) Full replacement. Replace all existing servers with new hardware.

(2) Partial replacement. Replace about 50 percent of the existing servers.

(3) Hardware upgrades. Replace and/or add additional components in theexisting servers. Assumption is any addition will only increase performanceby 20 percent.

(4) New addition.Addition of 10 percent more servers to the existing infrastructure.

(5) Software upgrade. Installation of operating systems and/or applications thatlead to a more efficient computing environment.

(6) Virtualization. Consolidate several servers onto one server using virtual machines.This model assumes a consolidation ratio of five old servers to one new server.

(7) Delay refresh.Do nothing because the value added of the other alternatives doesnot outweigh the upfront cost factor.

Each of the alternatives has a score from zero to one for each of the sub-criteria. Forexample, the delaying the refresh alternative could have a very high preference scorefor upfront cost since initial cost is zero, but will have lower preference scores forperformance since newer servers will outperform the current hardware. In order toscore each alternative, they are compared to the sub-criteria. If the alternative is the bestoption for a given sub-criterion, it is given a score of one. The other six alternatives areweighed against the best to derive a value that is some percentage from zero to one.Tangible sub-criteria can be found through specification research. Expert opinion can beused when insufficient data are available to support specification comparisons.

After all the pairwise comparisons for the criteria are calculated and the preferencesfor the alternatives have been identified, a score can be calculated for each alternative

by multiplying the alternative score by each sub-criteria weight and summing theproducts. The alternative with the highest sum is an indication which strategy is mostin line with the company preferences.

Applying the model: small vs large businessesWe conducted two case studies to verify our model one large IT environment andone small IT environment. Two case studies were done to compare and contrast thedifferences and determine if the model could work in different situations.


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A panel of six IT professionals (Appendix 2) evaluated the seven alternatives basedon the descriptions and assumptions discussed above. The resulting scores are shown inTable I.

The pairwise comparison survey was given to two sets of IT professionals small IT

deployments and large IT deployments. The roles in the large IT environmentorganization included server support personnel, a data center manager and other key ITdecision makers. In the small IT environments, the interviewees were the serveradministrator and his manager. The survey can be seen in Appendix 1. The surveys weredistributed either in person or via e-mail and the results were compiled in Appendix 3.

The large IT environment serves multiple groups of people all over the worldperforming different workloads. As customer demands change over time and serversbecome dated the servers are considered for refresh. Supportability, manageability,compatibility, and scalability are important factors to this organization. Cost is also a veryimportant and careful consideration is used when factoring upfront and reoccurring costs.

The small IT environment considers a single server administrator and his managermaintaining three servers for a group of 20-30 people. Like the large IT environment, thegroup is spread out around the world so the server needs to be accessibleat all times. Thefactors that are important are the performance and availability of the systems.

The results of the surveys were applied to the scores of the seven alternatives.The results are shown in Table II.

Fullreplacement

Hardwareupgrade

Partialreplacement

Newaddition

Softwareupgrade Virtualization

Donothing

F1 servicability 1.00 0.25 0.63 0.32 0.45 1.00 0.25F2 reliability 1.00 0.50 0.63 0.32 0.50 1.00 0.25F3 manageability 0.75 0.25 0.15 0.25 0.75 1.00 0.25F4 availability 0.90 0.85 0.82 0.85 0.80 1.00 0.80F1 energy efficiency 0.20 0.05 0.15 0.10 0.10 1.00 0.10F2 transactian/sec 1.00 0.30 0.63 0.32 0.30 0.25 0.25F3 density 0.40 0.20 0.30 0.15 0.30 1.00 0.20Fl recurring cost 0.80 0.50 0.65 0.53 0.50 1.00 0.50FZ upfront cost 0.00 0.60 0.50 0.90 0.80 0.80 1.00Fl scalability 1.00 0.30 0.60 0.27 0.30 0.30 0.20F2 legacy

compatibility 0.30 0.90 0.50 0.80 0.50 0.20 1.00F3 upgradability 1.00 0.10 0.60 0.27 0.10 1.00 0.20F4 service life 1.00 0.20 0.60 0.27 0.30 1.00 0.20

Table I.Preference scores forseven alternatives

Small environment Large environment

Virtualization 0.26 0.21Full replacement 0.20 0.20Partial replacement 0.15 0.15Software upgrade 0.108 0.12New addition 0.106 0.113Delay refresh 0.105 0.110Hardware upgrade 0.096 0.10

Table II.Case study results

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Discussion and conclusionsThis paper presented a decision-making framework using the AHP model that could beused by businesses of different sizes. AHP was chosen because it is flexible enough tohandle the changing requirements over time and easily implemented through

electronic surveys and consulting experts within the companies. There are manyexamples of the AHP used for decision-making especially in multi-criteria situations(Vaidya and Kumar, 2006). The models output is a ranking by preference of possibleserver refresh strategies. When analyzing the results, it is important to remember thatthis is not a final decision, only a structured way of looking at the decision (Roper-Loweand Sharp, 1990).

Two cases were applied to this model, in order to test the validity of the model.Surveys were sent to two businesses, one with small and one with large serverenvironments. We expected to see different strategy preferences between the small andlarge businesses but the results showed that both environments ranked thealternatives in the same order, with minor differences in scores.

There are a few possible reasons why the results were so close. In the interest oftime, an expert panel made up of the authors was used to make assumptions in order togenerate scores for the alternatives. It is possible, the assumptions for the alternativeswere not truly representative of the costs or benefits of each alternative. It could also bethat the number of surveys was too few to get a significant difference in results.Alternatively, both groups may have coincidentally had similar organizationalobjectives or goals and testing the model with different companies might yielddifferent results. We recommend that future implementations of the model use actualvalues for the refresh strategies in order to get a true representation of the preferences.

Even though the results did not match our expectations, the model itself workedwell. The survey was simple for users to complete and feedback on the decisionhierarchy said that we covered the required criteria for this type of decision. The model

presented is expected to be used by decision makers every time a decision for a servergroup needs to be made. If the goals of the company change, some of the criteria mightbe scored differently. One advantage for working through the model is that isdocuments the preferences at the time and can be reviewed is a particular decision isquestioned in the future.

The growing need for efficient IT infrastructure means companies need plans inplace for refreshing that infrastructure. This paper provides a framework for a decisionhierarchy that can rank possible server refresh alternatives. We recommend as the nextstep for this model is to test the validity in a real-world scenario and verify that the topranking preference is truly feasible by the company. Another test of validity is to applythe model to a refresh that is already complete and compare the results with the actionstaken by the company. The final stage of testing would be to implement this model in

companies across multiple industries to test the flexibility of the model.

References

Al-Subhi, K.M. and Al-Harbi, A.-S. (2001), Application of the AHP in project management,International Journal of Project Management, Vol. 19 No. 1, pp. 19-27.

Bhagwat, R. and Sharma, M.K. (2007), Performance measurement of supply chain managementusing the analytical hierarchy process, Production Planning & Control, Vol. 18 No. 8,pp. 666-80.


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Brill, K.G. (2007), The Invisible Crisis in the Data Center: The Economic Meltdown of MooresLaw, Uptime Institute, Santa Fe, NM.

Chen, H. and Kocaoglu, D.F. (2008), A sensitivity analysis algorithm for hierarchicaldecision models, European Journal of Operational Research, Vol. 185 No. 1, pp. 266-88.

Duncan, N.B. (1995), Capturing flexibility of information technology infrastructure: a study ofresource characteristics and their measure, Journal of Management Information Systems,Vol. 12 No. 2, pp. 37-57.

Gartner, Inc. (2009), Gartner Identifies the Top 10 Strategic Technologies for 2010, Gartner,Orlando, FL.

Gerdsri, N. and Kocaoglu, D.F. (2007), Applying the analytic hierarchy process (AHP) to build astrategic framework for technology roadmapping, Mathematical and Computer

Modelling, Vol. 46 Nos 7/8, pp. 1071-80.

Hafeez, K. and Essmail, E.A. (2007), Evaluating organisation core competences and associatedpersonal competencies using analytical hierarchy process,Management Research News,Vol. 30 No. 8, pp. 530-47.

Karami, E. (2006), Appropriateness of farmers adoption of irrigation methods: the application ofthe AHP model, Agricultural Systems, Vol. 87 No. 1, pp. 101-19.

Kocaoglu, D.F. (1983), A participative approach to program evaluation,IEEE Transactions onEngineering Management, Vol. 30 No. 3, pp. 37-44.

Leung, P., Muraoka, J., Nakamoto, S.T. and Pooley, S. (1998), Evaluating fisheries managementoptions in Hawaii using analytic hierarchy process (AHP), Fisheries Research, Vol. 36Nos 2/3, pp. 171-83.

Vaidya, O.S. and Kumar, S. (2006), Analytic hierarchy process: an overview application,European Journal of Operational Research, Vol. 169, No. 1, pp. 1-29.

Roper-Lowe, G.C. and Sharp, J.A. (1990), The analytic process and its application to aninformation technology decision, Journal of Operational Research Society, Vol. 41,pp. 49-59.

Saaty, T.L. and Kearns, K.P. (1985), Analytical Hierarchy Process, Pergamom, New York, NY.Sevkli, M., Koh, S.C.L., Zaim, S., Demirbag, M. and Tatoglu, E. (2008), Hybrid analytical

hierarchy process model for supplier selection, Industrial Management & Data Systems,Vol. 108 No. 1, pp. 122-42.

Weill, P. (1992), The relationship between investment in information technology and firmperformance: a study of valve manufacturing sector, Information Systems Research,Vol. 3, pp. 307-58.

Wong, J.K.W. and Li, H. (2008), Application of the analytic hierarchy process (AHP) inmulti-criteria analysis of the selection of intelligent building systems, Building and

Environment, Vol. 43 No. 1, pp. 108-25.

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Appendix 1. Server refresh survey

SURVEY PARTICIPANT: ______________________________________________

POSITION (as it relates to Server Refreshs): _____________________________

A.1 Criteria and factors

Criteria Factors

Reliability Availability Serviceability (RAS) Serviceability

Reliability

Manageability

Availability

Performance Energy Efficiency

Transaction/sec

Density

Total Cost of Ownership (TCO) Recurring CostsUpfront costs

Roadmap ScalabilityLegacy Compatibility

Upgradeability

Service Life

A.2: Factors definitions

Factor Description

Serviceability The ability for a failure to be serviced while the server remains operational

Reliability The history of failure and perceived future failures of the server.

Manageability The ease of supporting the server hardware and software in the companys IT infrastructure.

Availability The percentage of uptime designed into the server implementation

Energy Efficiency The energy used to perform a set of transactions (use of a benchmark or baseline)

Transaction/secThe amount of transactions the server can perform in one second (use of a benchmark or

baseline)

Density The amount of server hardware in a given footprint (per sq. f t, per server rack, etc)

Recurring CostsThe cost of incurred over the life of the server. Including energy used, cooling costs,

maintenance, licensing, ROI.

Upfront costsThe cost of new server hardware, the loss of depreciation, the installation labor for the new

hardware and software

ScalabilityThe ability to incrementally add one or more systems to an existing cluster when the overall

load of the cluster exceeds its capabilities

Legacy

Compatibility

The servers ability to integrate with previous software and applications used in the computing

environment.

UpgradeabilityThe servers ability to have its components replaced or additional components added to increase

its capabilities

(continued)

The serviceability life of the server hardware or the application it runs on until it reaches its end

of service life (EOL).


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Example

RAS PERFORMANCE

60 40

CRITERIA COMPARISON

RAS PERFORMANCE

ROADMAP RAS

TCO RAS

TCO PERFORMANCE

PERFORMANCE ROADMAP

TCO ROADMAP

FACTOR COMPARISON

RAS CRITERIA

Reliability Availability Serviceability Manageability

Reliability Serviceability Reliability Manageability

Availability Serviceability Availability Manageability

PERFORMANCE CRITERIA

(continued)

A.3 PAIRWISE COMPARISONS

For each pair, give a weighting for each criteria. The values can be from 1 to 99 and the total between the

two needs to add 100.

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Appendix 2. Expert panels

Energy efficiency Transactions/sec Energy efficiency Density

Transactions/sec Density

TCO criteria

Upfront cost Recurring cost

Roadmap criteria

Scalability Legacy compatibility Scalability Upgradability

Scalability Service life Upgradability Legacy compatibility

Upgradability Service life Service life Legacy compatibility

Thank you for your participation

Expert panel foralternatives Job title Industry

Years ofexperience

Expert A Sr database engineer/information

technolgy manager

Microprocessor design and

manufacturing

27

Expert B Technical marketing engineer Enterprise products designand manufacturing

5

Expert C Software engineer Microprocessor architecture 7Expert D Validation engineer Microprocessor architecture 11Expert E Platform validation engineer Processor/platform product

development7

Expert F Solutions specialist Telecommunications 5Small organizationSurvey respondent1 small organization

Server administrator Enterprise products designand manufacturing

10

Survey respondent2 small organization

IT manager Enterprise products designand manufacturing

5

Large organizationSurvey respondent3 large organization

Data center managers Microprocessor design andmanufacturing

11

Survey respondent4 large organization


10



12


Sr database manager Telecommunications 11


Sr database manager Telecommunications 10Table A


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Appendix 3. Survey results

Figure A2.Large businesscriteria preferences


RAS 0.22 0.32Performance 0.41 0.25TCO 0.14 0.25Roadmap 0.22 0.19

Table AII.Criteria preferences

Figure A1.Small businesscriteria preferences


Serviceability 0.24 0.18Reliability 0.26 0.27Manageability 0.08 0.24Availability 0.42 0.31

Table AIII.RAS sub-criteriapreferences

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Figure ASmall business RA

sub-criteria preferenc

Figure ALarge business RA


Figure ASmall busine

performance sub-criterpreferenc


Energy efficiency 0.35 0.28Transactions/second 0.37 0.53Density 0.38 0.19

Table AIVPerforman



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Figure A6.Large businessperformance sub-criteriapreferences

Figure A7.Small business roadmapsub-criteria preferences

Figure A8.Large business roadmapsub-criteria preferences

Small business Large business

Upfront costs 0.35 0.54Recurring costs 0.65 0.46

Table AV.TCO sub-criteria

preferences

Small business Large business

Scalability 0.23 0.26Legacy compatibility 0.20 0.18Upgradeability 0.26 0.20Service life 0.30 0.36

Table AVI.Roadmap sub-criteriapreferences

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(1) TCO (small environment):

. Person 1: 20/80.

. Person 2: 50/50.

(2) TCO (large environment):. Person 1: 30/70.

. Person 2: 80/20.

. Person 3: 50/50.

. Person 4: 70/30.

. Person 5: 40/60.

Corresponding authorTugrul U. Daim can be contacted at: [email protected]


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