Assess enterprise applications for cloud migration

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Assess enterprise applications for cloudmigrationUsing the Analytic Hierarchy Process to evaluate apps forthe cloud

Brijesh DebSenior Technology ArchitectInfosys

Skill Level: Intermediate

Date: 14 Sep 2010

It's a simple question, but the answer is anything but simple: How do you knowwhether an enterprise application is suited for the cloud? Follow along as theauthor demonstrates a step-by-step application portfolio assessment approach todetermining the suitability of your enterprise applications for the cloud based onthe Analytic Hierarchy Process (AHP).

Cloud computing: Fundamentals http://www.ibm.com/developerworks/training/kp/cl-kp-cloudfundamentals/index.htmlWithout a doubt cloud computing offers advantages for enterprise operations:

• It can help reduce costs (for instance, by setting up and configuring anapplication testbed or by being able to add and subtract computing power whenyou need it).

• It can help you process large data sets faster (by balancing workloads whereand when needed).

• It can help your business respond more quickly to changing conditions (bybeing able to apply business analytics to larger amounts of mixed-structure datain a more rapid way).

But how do you know whether an enterprise application is suited for the cloud?

There are varied business, technology, and risk considerations which can haveprofound effect on the overall success of cloud initiatives in an enterprise, meaningthere is no "one-size-fit-all" answer for whether an application "fits" in the cloud(which I will refer to as fitment). Each enterprise has to assess its application

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portfolio based on its own business imperatives, technology strategy, and riskappetite.

Some of the questions businesses need to ask themselves before undertaking cloudinitiatives are:

• What factors should I consider for cloud enablement of my enterpriseapplications? How do I judge different competing priorities?

• How do I identify the applications and services that are best suited for moving toa cloud environment based on business priority and technical fitment?

• How do I prioritize enterprise applications and services for a "phase-smart"cloud enablement? How can I avoid that "gut feeling" and bring objectivity intothe evaluation?

• What are the different risks involved?

With these questions in mind, I've developed an application portfolio assessmentapproach to determining the suitability of your enterprise applications for the cloud. Itis based on the Analytic Hierarchy Process (AHP).

AHP is a structured technique for making complex decisions that helps users sortout the "best" decision for their challenge, situation, and variables instead of thefinding the "correct" decision. It was first conceived in the 1970s. The process isstraightforward:

1. Decompose the problem into series of easier-to-understand sub-problems. Anyand all input variables are welcome, whether they are precise data tables orrough guesses — as long as it applies to the situation at hand.

2. Evaluate the elements by comparing them to each of the other elements, twoelements at a time. You can do this using concrete facts or judgments — youare deciding each element's relative importance.

3. Assign a numerical value to each of the evaluations, which allows you tocompare each element to the others across the life cycle of the problem-solvingprocess.

4. Calculate numerical priorities for each of the decision alternatives; theserepresent each alternatives' perceived relative ability to achieve the decisiongoal.

In this article, I provide details about AHP and demonstrate how to apply thisapproach to support your decision on whether or not an enterprise application isappropriate for implementation in the cloud. And since all cloud systems performunder the same general concepts, this technique should be useful to you regardlessof which cloud platform (or platforms) you choose to employ.

Assessment approach

Figure 1 illustrates the assessment approach via a high-level flow chart.

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Figure 1. Flow chart of application portfolio assessment for cloud

The approach is a multi-dimensional statistical evaluation; enterprise applications areevaluated in three dimensions:

• Business value: How much business value would the organization accrue bymoving the applications to cloud?

• Technical fitment: How feasible is it to move the applications to cloud?• Risk exposure: How much risk is involved in moving the applications to cloud?

Each of these dimensions has decisive effect on a go/no-go decision regarding cloudenablement of applications. For example, an application may be evaluated to havehigh scores in the business value and technical fitment dimensions, but it may not bea good candidate for cloud enablement if the risk exposure is higher than the level ofrisk a particular enterprise is willing to assume.

Evaluation of an application in each of these dimensions is a multi-criteria decisionanalysis (MCDA); AHP is one of the methods used in MCDA. (For more on MCDA,see Resources.) AHP involves the evolution of different alternatives based ofvarious criteria, some which may conflict with other alternatives, some which have acontrasting nature (be it qualitative or quantitative) or impact (positive or negative) onoverall suitability.

The techniques used in the AHP quantify relative priority for a given set of criteria ona ratio scale. AHP offers advantages over many other MCDA methods:

• AHP provides a comprehensive structure to combine both quantitative andqualitative criteria in the decision-making process.



• AHP brings an ability to judge consistency in analysis process to the table: Thishelps reduce anomalies and heighten objectivity.

Another point worth noting is that at the beginning of the assessment, applicationsthat obviously do not fit the profile are eliminated. Internal and external applicationsare segregated to be evaluated separately since they have concerns of varied natureand importance. Internal applications are those that are accessed only from withinthe firewall of an enterprise; external applications can be accessed from outside thefirewall. As an example to show how each deserves different considerations, securityconcerns of external applications are much more stringent then internal applications.

Now let's use AHP to evaluate a set of applications for cloud suitability.

Evaluation using AHP

There are several components, or steps, involved in using AHP to evaluate thesuitability of an application for the cloud. These include:

• Defining criteria hierarchy.• Determining criteria priority.• Comparing your application against the criteria.• Calculate overall AHP score.

Define criteria hierarchy

Each of the multiple dimensions I introduced (business value, technical fitment, riskexposure) has a number of criteria; these in turn can further have multiple levels ofgranular sub-criteria (Figure 2).



Figure 2. Schematic representation of AHP for evaluating cloud technicalfitment

Criteria pertaining to different dimensions are structured in hierarchy of levels inaccordance with the AHP framework. Figure 2 shows the hierarchy structure for atechnical fitment evaluation.

Remember, criteria and sub-criteria can be either quantitative or qualitative. Forexample "No of External System" is a quantitative value while "Well DefinedIntegration Point" is a qualitative one.

A cluster of criteria and its sub-criteria is called a criteria group. For example, inFigure 2, criteria "Application Design" and its two sub-criteria, "Loose Coupling"and "Virtualization," belong to same group making it a criteria group of three groupmembers.

Figure 3 provides an illustrative list of evaluation criteria hierarchy for all threedimensions, sort of a criteria tree. While broad criteria and sub-criteria can be reused,some of the criteria would need to be tailored based on the context of an enterprise.



Figure 3. Illustrative criteria hierarchy for all three dimensions

I will show you how to use technical criteria and sub-criteria to illustrate the stepsused to evaluate three sample applications for technical fitment.

Determine criteria priorityRelative priorities are assigned for different criteria using the 1-9 scale of AHP (Table1).

Table 1. AHP's 1-9 scale of criteria priority; scale for pairwise comparisonIntensity Definition Explanation

1 Equal importance Two elements contribute equally toobjective

3 Moderate importance Slightly favor one element over another

5 Strong importance Strongly favor one element over another

7 Very important Very strongly favor one element overanother

9 Extreme importance Extremely favor one element overanother

2, 4, 6, 8 Intermediate values



Priorities are first determined for criteria and then for individual sub-criteria undereach criteria. The sum of priorities of individual criteria in a particular level isnormalized to one.

Sub-criteria have both local and global priorities. Global priority is the product of itsown priority (local priority) and the priority of parent criteria. Thus global priority of "Noof External System" is product of its local priority and priority of "Integration Ease."

Table 2 show an estimation of relative priority for sample level 1 technical criteria.

Table 2. An estimation of relative priority; priority calculation for criteriaTechnical Fitment IE ME TS AD Priority

Integration Ease(IE)

1 1 0.5 0.2 0.1075

MigrationEase(ME)

1 1 0.33 0.2 0.0989

TechnologyStack(TS)

2 3 1 0.33 0.2304

Application Design(AD)

5 5 3 1 0.5633

Consistency Ratio 0.0127

All the diagonal elements of the matrix are 1 (the elements are compared tothemselves). Comparisons in only the upper triangular matrix are done; values in thelower triangle matrix are the reciprocal of upper triangular matrix.

For example, the importance of the Technical Stack (TS) is two times that ofIntegration Ease (IE). The list of relative Priority and Consistency Ratio is calculatedas per AHP methodology. The Consistency Ratio helps judge the consistency in pair-wise comparison.

Similarly, relative Priority is calculated for all criteria and sub-criteria. As shown inTable 3, global priority of sub-criteria is product of its local priority and the parent'spriority. Thus for No. of External Systems (ES), global priority is product of its localpriority (0.109586) and priority of Integration Ease (0.1075).

Table 3. Priority calculation for sub-criteria, both local and globalIntegration Ease ES IP HD Local Priority Global Priority

No. of ExternalSystems (ES)

1 0.333 0.2 0.10959 0.0117790

Well-definedIntegration Point(IP)

3 1 0.5 0.30915 0.0332293

No. of HW Devicesfor Integration (HD)

5 2 1 0.58126 0.0624777

Consistency Ratio 0.00319



Compare application against criteria

In this step, you'll see how to compare your enterprise application against bothquantitative and qualitative criteria

Comparison against quantitative criteriaTo evaluate the application via quantitative criteria, applications are comparedagainst each other by taking the quantitative value for the criteria:

• For criteria that have positive impact on the objective, application scores fora particular criterion are calculated by normalizing the values to 1. For a setof numbers ri, i=1 ... n, normalized value rin is ri divided by the sum of thefollowing of all the numbers in the set.

• For a criterion that has negative impact, relative score of applications iscalculated by first taking the reciprocal of the values and then normalizing them.Reciprocal value is the multiplicative inverse of a number; the reciprocal value

of x is 1/x.

Table 4 demonstrates what happens when No. of External Systems has negativeimpact; with the increase in number of external systems, the quantitative "score" ofIntegration Ease decreases. So if the three applications have to integrate with 5, 3,and 2 numbers of external systems respectively, you can see their relative scores.

Table 4. Score for quantitative criteria with negative impact

Application Evaluation Number of Systems Reciprocal Value (Neg.Impact)

Score

Application 1 5 0.20 0.194



Comparison against qualitative criteriaFor qualitative criteria, relative application scores are calculated by using pair-wisecomparison using the 1-9 scale of AHP. The process is same as determining thepriority for criteria.

Calculate overall AHP score

The overall AHP score of an application for a dimension is derived by the sum of theproduct of its relative priority in each criteria and the relative priority of respectivecriteria. Figure 4 shows the formula.



Figure 4. Formula to calculate overall AHP score

In this formula:

• Sx is the AHP score for the xth application.• M is the number of criteria group.• Ni is the number of the members in the ith criteria group.• Pi is the priority value of the ith criteria group.• pij is the priority value of the jth criteria belonging to the ith criteria group.• sijx is the score of the xth application comparison against the jth criteria in the ith

criteria group.

A top-level checklist for AHP

Before I wrap up this article, I'd like to provide a top-level checklist for the stepsinvolved in using AHP to evaluate your enterprise application portfolio for itssuitability in a cloud environment.

1. Define criteria hierarchya. Define criteria hierarchy for each dimensionb. Tailor the hierarchy based on enterprise context

2. Determine criteria prioritya. Using pair-wise comparison, determine relative importance of one criteria

over anotherb. Determine local priority for all criteriac. Determine global priority for all sub-criteria

3. Determine application score for a criteriaa. Using pair-wise comparison determine relative suitability of candidate

applications against each criteriab. Determine relative score of candidate applications against each criteriac. For criteria having negative impact, use reciprocal value to determine

score4. Determine AHP score

a. Determine overall AHP score for candidate applications using formula inFigure 4

In conclusion

I'd like to close by first summarizing the assessment result. Once the AHP evaluationis done for all three dimensions, application scores can be collated to arrive at adecision matrix, a sample of which is shown in Table 5. The group at the top is



best suited for cloud deployment; each successive group is less suited for clouddistribution.

The matrix will provide a holistic view of the impact of cloud enablement of differentapplications in an enterprise against different dimension and will aid in making aninformed decision.

Table 5. Sample suitability decision matrix

Application Score :Business Value

Application Score :Technical Fitment

Application Score : RiskExposure

Suitability

High High Low Favorable on alldimensions. Applications inthis group are most suitablefor cloud enablement; theirscore is favorable on alldimensions.

High Low Low Favorable in twodimensions. Applications inthis group are also suitablefor cloud enablement; theyscore favorably in at least twodimensions.

Low High Low Favorable in twodimensions.

High High Low Favorable in twodimensions.

Low Low Low Favorable in onedimension. Applicationsin this group are not ideallysuitable; they score favorablyin only one dimension.

High Low High Favorable in onedimension.

Low High High Favorable in onedimension.

Low Low High Favorable in nodimensions. Applications inthis group are best left "as-is";their score is not favorable onany dimensions.

Given the concerns and risk involved in cloud computing initiatives, each enterprisehas to assess its application portfolio based on its business imperatives, technologystrategy, and risk appetite before embarking on a flight into the clouds. With thisassessment that involves multiple competing criteria of varied nature, impact, andpriority, I've demonstrated how a multi-dimensional statistical approach using theAnalytic Hierarchy Process (AHP) can be used to help decide which, if any, of yourenterprise applications belong in the cloud.



Resources• Wikipedia offers a quick definition of the Analytic Hierarchy Process and multi-

criteria decision analysis (MCDA).• This tutorial on "Multi Criteria Decision Making" shows you how to get deeper

into using AHP, as well as some other methods of decision-making.• We don't want you think that AHP is only good for deciding whether or not to

deploy your enterprise applications to cloud: It's a really good tool for makingany complex decision (ask Google).

• Check out IBM Business Analytics software.• In the developerWorks Cloud zone, discover and share knowledge and

experience of application and services developers building their projects forcloud deployment.

• Join a cloud computing group on My developerWorks.• Read all the great cloud blogs on My developerWorks.• Join the My developerWorks community, a professional network and unified set

of community tools for connecting, sharing, and collaborating.

http://en.wikipedia.org/wiki/Analytic_Hierarchy_Process

http://en.wikipedia.org/wiki/Multi-criteria_decision_analysis

http://en.wikipedia.org/wiki/Multi-criteria_decision_analysis

http://people.revoledu.com/kardi/tutorial/AHP/Multi-Criteria-Decision-Making.htm

http://www.google.com/search?q=analytic+hierarchy+process

http://www.ibm.com/software/analytics/

https://www.ibm.com/developerworks/cloud/index.html

https://www.ibm.com/developerworks/mydeveloperworks/groups/service/html/allcommunities?tag=cloud&page=1

https://www.ibm.com/developerworks/mydeveloperworks/blogs/home/tags/cloud

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About the author

Brijesh Deb

Brijesh Deb is a Senior Technology Architect with SETLabs at Infosys.Deb has varied IT experience spanning enterprise architecture,technology consulting, applied research, and engineering management;his current focus is on cloud, Web 2.0, and JavaEE.

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