Assess enterprise applications for cloud migration

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Assess enterprise applications for cloud
migration
Using the Analytic Hierarchy Process to evaluate apps for
the cloud
Brijesh Deb
Senior Technology Architect
Infosys
Skill Level: Intermediate
Date: 14 Sep 2010
It's a simple question, but the answer is anything but simple: How do you know
whether an enterprise application is suited for the cloud? Follow along as the
author demonstrates a step-by-step application portfolio assessment approach to
determining the suitability of your enterprise applications for the cloud based on
the Analytic Hierarchy Process (AHP).
Cloud computing: Fundamentals http://www.ibm.com/developerworks/training/kp/cl-
kp-cloudfundamentals/index.html
Without a doubt cloud computing offers advantages for enterprise operations:
• It can help reduce costs (for instance, by setting up and configuring an
application testbed or by being able to add and subtract computing power when
you need it).
• It can help you process large data sets faster (by balancing workloads where
and when needed).
• It can help your business respond more quickly to changing conditions (by
being able to apply business analytics to larger amounts of mixed-structure data
in 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 have
profound effect on the overall success of cloud initiatives in an enterprise, meaning
there 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 risk
appetite.
Some of the questions businesses need to ask themselves before undertaking cloud
initiatives are:
• What factors should I consider for cloud enablement of my enterprise
applications? How do I judge different competing priorities?
• How do I identify the applications and services that are best suited for moving to
a 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 into
the evaluation?
• What are the different risks involved?
With these questions in mind, I've developed an application portfolio assessment
approach to determining the suitability of your enterprise applications for the cloud. It
is based on the Analytic Hierarchy Process (AHP).
AHP is a structured technique for making complex decisions that helps users sort
out the "best" decision for their challenge, situation, and variables instead of the
finding the "correct" decision. It was first conceived in the 1970s. The process is
straightforward:
1. Decompose the problem into series of easier-to-understand sub-problems. Any
and all input variables are welcome, whether they are precise data tables or
rough guesses — as long as it applies to the situation at hand.
2. Evaluate the elements by comparing them to each of the other elements, two
elements at a time. You can do this using concrete facts or judgments — you
are deciding each element's relative importance.
3. Assign a numerical value to each of the evaluations, which allows you to
compare each element to the others across the life cycle of the problem-solving
process.
4. Calculate numerical priorities for each of the decision alternatives; these
represent each alternatives' perceived relative ability to achieve the decision
goal.
In this article, I provide details about AHP and demonstrate how to apply this
approach to support your decision on whether or not an enterprise application is
appropriate for implementation in the cloud. And since all cloud systems perform
under the same general concepts, this technique should be useful to you regardless
of 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 are
evaluated in three dimensions:
• Business value: How much business value would the organization accrue by
moving 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 cloud
enablement of applications. For example, an application may be evaluated to have
high scores in the business value and technical fitment dimensions, but it may not be
a good candidate for cloud enablement if the risk exposure is higher than the level of
risk a particular enterprise is willing to assume.
Evaluation of an application in each of these dimensions is a multi-criteria decision
analysis (MCDA); AHP is one of the methods used in MCDA. (For more on MCDA,
see Resources.) AHP involves the evolution of different alternatives based of
various criteria, some which may conflict with other alternatives, some which have a
contrasting nature (be it qualitative or quantitative) or impact (positive or negative) on
overall suitability.
The techniques used in the AHP quantify relative priority for a given set of criteria on
a ratio scale. AHP offers advantages over many other MCDA methods:
• AHP provides a comprehensive structure to combine both quantitative and
qualitative criteria in the decision-making process.

• AHP brings an ability to judge consistency in analysis process to the table: This
helps reduce anomalies and heighten objectivity.
Another point worth noting is that at the beginning of the assessment, applications
that obviously do not fit the profile are eliminated. Internal and external applications
are segregated to be evaluated separately since they have concerns of varied nature
and importance. Internal applications are those that are accessed only from within
the firewall of an enterprise; external applications can be accessed from outside the
firewall. As an example to show how each deserves different considerations, security
concerns 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 the
suitability 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, risk
exposure) has a number of criteria; these in turn can further have multiple levels of
granular sub-criteria (Figure 2).

Figure 2. Schematic representation of AHP for evaluating cloud technical
fitment
Criteria pertaining to different dimensions are structured in hierarchy of levels in
accordance with the AHP framework. Figure 2 shows the hierarchy structure for a
technical fitment evaluation.
Remember, criteria and sub-criteria can be either quantitative or qualitative. For
example "No of External System" is a quantitative value while "Well Defined
Integration Point" is a qualitative one.
A cluster of criteria and its sub-criteria is called a criteria group. For example, in
Figure 2, criteria "Application Design" and its two sub-criteria, "Loose Coupling"
and "Virtualization," belong to same group making it a criteria group of three group
members.
Figure 3 provides an illustrative list of evaluation criteria hierarchy for all three
dimensions, 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 steps
used to evaluate three sample applications for technical fitment.
Determine criteria priority
Relative priorities are assigned for different criteria using the 1-9 scale of AHP (Table
1).
Table 1. AHP's 1-9 scale of criteria priority; scale for pairwise comparison
Intensity Definition Explanation
1 Equal importance Two elements contribute equally to
objective
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 over
another
9 Extreme importance Extremely favor one element over
another
2, 4, 6, 8 Intermediate values

Priorities are first determined for criteria and then for individual sub-criteria under
each criteria. The sum of priorities of individual criteria in a particular level is
normalized to one.
Sub-criteria have both local and global priorities. Global priority is the product of its
own priority (local priority) and the priority of parent criteria. Thus global priority of "No
of 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 criteria
Technical Fitment IE ME TS AD Priority
Integration Ease
(IE)
1 1 0.5 0.2 0.1075
Migration
Ease(ME)
1 1 0.33 0.2 0.0989
Technology
Stack(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 to
themselves). Comparisons in only the upper triangular matrix are done; values in the
lower triangle matrix are the reciprocal of upper triangular matrix.
For example, the importance of the Technical Stack (TS) is two times that of
Integration Ease (IE). The list of relative Priority and Consistency Ratio is calculated
as 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 in
Table 3, global priority of sub-criteria is product of its local priority and the parent's
priority. Thus for No. of External Systems (ES), global priority is product of its local
priority (0.109586) and priority of Integration Ease (0.1075).
Table 3. Priority calculation for sub-criteria, both local and global
Integration Ease ES IP HD Local Priority Global Priority
No. of External
Systems (ES)
1 0.333 0.2 0.10959 0.0117790
Well-defined
Integration Point
(IP)
3 1 0.5 0.30915 0.0332293
No. of HW Devices
for 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 both
quantitative and qualitative criteria
Comparison against quantitative criteria
To evaluate the application via quantitative criteria, applications are compared
against each other by taking the quantitative value for the criteria:
• For criteria that have positive impact on the objective, application scores for
a particular criterion are calculated by normalizing the values to 1. For a set
of numbers ri, i=1 ... n, normalized value rin is ri divided by the sum of the
following of all the numbers in the set.
• For a criterion that has negative impact, relative score of applications is
calculated 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 negative
impact; with the increase in number of external systems, the quantitative "score" of
Integration 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 criteria
For qualitative criteria, relative application scores are calculated by using pair-wise
comparison using the 1-9 scale of AHP. The process is same as determining the
priority for criteria.
Calculate overall AHP score
The overall AHP score of an application for a dimension is derived by the sum of the
product of its relative priority in each criteria and the relative priority of respective
criteria. 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 steps
involved in using AHP to evaluate your enterprise application portfolio for its
suitability in a cloud environment.
1. Define criteria hierarchy
a. Define criteria hierarchy for each dimension
b. Tailor the hierarchy based on enterprise context
2. Determine criteria priority
a. Using pair-wise comparison, determine relative importance of one criteria
over another
b. Determine local priority for all criteria
c. Determine global priority for all sub-criteria
3. Determine application score for a criteria
a. Using pair-wise comparison determine relative suitability of candidate
applications against each criteria
b. Determine relative score of candidate applications against each criteria
c. For criteria having negative impact, use reciprocal value to determine
score
4. Determine AHP score
a. Determine overall AHP score for candidate applications using formula in
Figure 4
In conclusion
I'd like to close by first summarizing the assessment result. Once the AHP evaluation
is done for all three dimensions, application scores can be collated to arrive at a
decision 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 cloud
distribution.
The matrix will provide a holistic view of the impact of cloud enablement of different
applications in an enterprise against different dimension and will aid in making an
informed decision.
Table 5. Sample suitability decision matrix
Application Score :
Business Value
Application Score :
Technical Fitment
Application Score : Risk
Exposure
Suitability
High High Low Favorable on all
dimensions. Applications in
this group are most suitable
for cloud enablement; their
score is favorable on all
dimensions.
High Low Low Favorable in two
this group are also suitable
for cloud enablement; they
score favorably in at least two
dimensions.
Low High Low Favorable in two
dimensions.
High High Low Favorable in two
dimensions.
Low Low Low Favorable in one
dimension. Applications
in this group are not ideally
suitable; they score favorably
in only one dimension.
High Low High Favorable in one
dimension.
Low High High Favorable in one
dimension.
Low Low High Favorable in no
this group are best left "as-is";
their score is not favorable on
any dimensions.
Given the concerns and risk involved in cloud computing initiatives, each enterprise
has to assess its application portfolio based on its business imperatives, technology
strategy, and risk appetite before embarking on a flight into the clouds. With this
assessment that involves multiple competing criteria of varied nature, impact, and
priority, I've demonstrated how a multi-dimensional statistical approach using the
Analytic Hierarchy Process (AHP) can be used to help decide which, if any, of your
enterprise 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 making
any 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 for
cloud 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.

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.
© Copyright IBM Corporation 2010
(www.ibm.com/legal/copytrade.shtml)
Trademarks
(www.ibm.com/developerworks/ibm/trademarks/)

Assess enterprise applications for cloud migration

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