IT consolidation – an optimization approachUlrik Franke∗, Oliver Holschke†, Markus Buschle∗, Per Narman∗, and Jannis Rake-Revelant†

∗Industrial Information and Control SystemsRoyal Institute of Technology

Stockholm, Sweden{ulrikf, markusb, pern}@ics.kth.se

†Technische Universitat BerlinBerlin, Germany

{oliver.holschke, jannis.rake}@sysedv.tu-berlin.de

Abstract—Consolidation of IT resources is a frequently citedtask for IT decision makers, aiming to remove redundancy andthereby to cut costs. However, while economically motivated,the methods described in the literature rarely address costsdirectly. Instead, the focus often remains on purely IT-relatedconsiderations. In this paper, IT consolidation is addressed froman operations research perspective, applying a binary integerprogramming model to find optimal solutions to consolidationproblems. Since accurate cost estimates are vital to successfulconsolidation, and play an important role in the presented binaryinteger program, the paper also addresses the costs involved inconsolidation, with a particular focus on the costs of modifyingbusiness processes. Applying the mathematical method, withaccurate cost estimates, enables decision makers to make optimaldecisions in a transparent and rigorous way. The use of theproposed method is demonstrated with an example based ona real consolidation problem from a large European powersupplier.

Index Terms—IT consolidation, Binary integer programming,Business process modifications

I. INTRODUCTION

In a world of limited enterprise budgets, IT consolidationoffers some attractive potential benefits: efficient utilization ofcapacities, improved control and ultimately substantial costsavings, while still meeting the same set of requirements.In [1], IT consolidation features as one of the applicationscenarios frequently encountered in industrial use of EnterpriseArchitecture, and [2] identifies consolidation as one of therecent trends in enterprise level IT. IT consolidation can beregarded as a distinct feature of architectural thinking [3].

However, poorly supervised and planned evolution of ITintensive organizations has led to heterogeneous applicationlandscapes. This complexity turns consolidation into a hardproblem. IT analysts and IT decision makers are hard pressedto generate alternative consolidation solutions and accuratelypresent their business values to the enterprise management.

Indeed, this task is so demanding that IT managers some-times fail even to make qualitative initial decisions and esti-mate their financial impact. However, such alternative evalua-tions are crucial to successful consolidation efforts.

The method proposed in this paper is novel in that it evalu-ates consolidation options not only from a functional view butalso from a cost perspective – a perspective often absent inexisting methods that focus on identifying redundancies and

drafting a to-be IT portfolio. Moreover, by using binary integerprogramming the method can efficiently deliver a cost-optimalsolution for the consolidation problem.

The remainder of this paper is structured as follows. Sec-tion II contrasts the present contribution with some relatedwork in the field of IT consolidation. In section III, a generaloptimization model of IT consolidation is developed. It isfollowed by a thorough discussion of the costs related to ITconsolidation in section IV. Section V illustrates the frame-work with a case study, followed by a discussion in section VI.Some concluding remarks are given in section VII.

II. RELATED WORK

While many sources stress the importance of IT consolida-tion for cost reduction [1], [2], [4], not all address costs ex-plicitly. Previous approaches typically fall into two categories:(i) work with a cost focus and (ii) work with a functional focus,suggesting methods for consolidation projects. In category (i),[5] examines whether consolidation of back-office operationsin banks really reduces operating costs, analyzing data fromthe US Federal Reserve. [6] looks at enterprise IT costs froman accounting perspective. However, not very much attention ispaid to the benefits of IT. IT project evaluation and IT Portfoliomanagement techniques estimate the value of planned ITprojects [7], including consolidation. However, they fail tograsp the relationship between the value and the large set ofconsolidation alternatives. These approaches thus fail to solveconcrete consolidation problems.

The methods of category (ii) typically focus on either oneof two activities: (a) redundancy identification or redundancyclassification and (b) to-be application portfolio design. In [8]a pattern based approach for Enterprise Architecture Man-agement is presented. [9] focuses on the information systemmanagement success factor ”optimal reuse” – every functionis only implemented once by an application. Another pattern-based approach is [10]. Patterns are used to map business re-quirements to application requirements and to derive potentialsolution designs from application requirements. [11] presentsan approach for finding and testing principles for the removalof redundant applications and organizing the Enterprise Ar-chitecture based on the remaining application portfolio. [12]and [13] are examples of very fine-grained architecture models

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(transaction response times) for consolidation. These modelsare too detailed for IT decision makers to use for largerdecisions such as project initiation.

In [14], application consolidation using Enterprise Archi-tecture methods is considered, including cost aspects. Theproblem is formalized using decision theory and a practicalframework is proposed.Compared to the present article, appli-cation consolidation is considered from a local single-systemperspective rather than a global architectural one.

From the literature review, we conclude that either (i) costsare absent in the models or (ii) the model constructs areinadequate when it comes to the requirements of IT decisionmakers, who are often interested in global IT consolidationencompassing the entire IT landscape.

III. AN OPTIMIZATION MODEL OF IT CONSOLIDATION

How should IT decision makers address consolidation prob-lems? In the following, an optimization-based approach tothis problem is described. Conceptually the process is simple,proceeding from a few key facts to an optimal decision. Themethod can be schematically illustrated as follows:

List of as-is business processes }(i)List of as-is abstract software functions

List of as-is IT systemsProcess requirements for functions

}(ii)System provisions of functions

Costs for maintaining systems}

(iii)+ Costs for new system-process connections= Optimal decision on systems to keep and

}(iv)remove, and new connections to be made

Roughly, the process is the following: (i) Collect lists ofbusiness processes, abstract software functions and IT systems,(ii) collect requirements and provisions, (iii) collect costs, and(iv) solve the optimization problem to make the decision. Itshould be noted that once step (iv) is reached, the method isnot stepwise, but takes all the (i)-(iii) facts simultaneously intoaccount (as suggested by the addition analogy above).

A. The binary integer program

Binary integer programming problems (BIP) frequentlyarise in practical applications where a number of interrelatedyes-or-no-decisions have to be made [15]. The BIP family ofproblems can be expressed as in Eq. 1:

min cT xs.t. Ax ≤ b

x ∈ Bn.(1)

c is a vector of costs, associated with the binary decisionsin the vector x of equal length. A is a matrix that, togetherwith the right hand side vector b encodes a number of linearinequality constraints on any feasible solution x. The costfunction cT x is to be minimized subject to these constraints.

Methods for finding optimal solutions to these problems isa large and vivid research area of its own at least since the1960s [16]. One general approach to solving BIP problems

Requirements

p1

p2

Processes

f1 f2 f3

Functions

s2 s3

Systems

As-is connectionsProcesses

Systems

s1 s2 s3s1

p1

p2

Fig. 1. A simple example of connections and requirements between businessprocesses, functions and systems.

is to solve a series of so called linear programming (LP)relaxation problems, where the constraints x ∈ B are relaxedto the weaker constraint 0 ≤ x ≤ 1. This is the approachimplemented in the MATLAB software, which has been usedin the present paper. More thorough descriptions of BIP canbe found for example in [15].

Consider an IT consolidation problem of the following form.Let there be p = (p1, . . . , pp) different business processes,each of which requires a number of abstract software functionsfrom f = (f1, . . . , ff ). Furthermore, let each of the abstractsoftware functions from f be implemented on one or severalsystems s = (s1, . . . , ss).

For example, let p1 Register new customer be onebusiness process, and p2 Register new employee beanother. p1 requires the abstract functions f1 File contactdetails and f2 Billing. p2 requires the abstract functionsf1 File contact details and f3 Send paycheck.The system s1 Codex provides only the function f1. Thesystem s2 Moneta provides the functions f2 and f3. Thesystem s3 Liber also provides only the function f1. In theas-is state, the process p1 is connected to systems s1 and s2,whereas the process p2 is connected to systems s2 and s3.

The example is depicted in Fig. 1, with process requirementsfor functions and system provisions of these depicted tothe left, and actual as-is connections between processes andsystems depicted to the right. Intuitively, one of the systemsCodex and Liber seems superfluous, as they provide thesame functionality, viz. File contact details. Thesystem maintenance costs and the cost of reconnecting thebusiness processes to the remaining system determine whichsystem – if any – to remove. Reconnection cost can be costlyenough to keep both systems.

To solve the problem, an integer programming approach canbe used. Let the connectivities illustrated in Fig. 1 be encodedas binary matrices: P encodes the requirements of processes(p rows) for functions (f columns), X encodes the (as-is)connections from processes (p rows) to systems (s columns)and F encodes the functions (f rows) provided by systems (s

2222

columns). Following the example, we have:

P =

( f1 f2 f3

p1 1 1 0

p2 1 0 1

)X =

( s1 s2 s3

p1 1 1 0

p2 0 1 1

)

F =

s1 s2 s3

f1 1 0 1

f2 0 1 0

f3 0 1 0

(2)

To account for the cost aspects, a vector cs (length s) contain-ing the system costs and a matrix CX (same dimensions asX) containing the (potential) costs for connecting processesand systems are defined.

s1 s2 s3cs = (c1 c2 c3)

CX =

( s1 s2 s3

p1 c11 c12 c13

p2 c21 c22 c23

)(3)

To comply with the form of Eq. 1, a full cost vector c =(c1, . . . , cs, c11, . . . , c1s, . . . , cp1, . . . , cps) is also defined.

Let x = (x1, . . . , xs, xs+1, . . . , xs+p·s) be a decision vectorwhere the components xi ∈ B represent the decisions to bemade concerning the existence of systems and connections.The first s decisions are made on whether to keep (1) orremove (0) systems, the consecutive p · s ones are made onwhether to keep/create (1) or remove/not create (0) connec-tions. The consolidation problem can now be put on the formof Eq. 1:

min cT x

s.t.

pIs −Is · · · −Is

0F(1) · · · 0

.... . .

...0 · · · F(p)

︸ ︷︷ ︸

(1+p)·s

x1

...xs

xs+1

...xs+p·s

≥

0(1)

...0(s)p11

...p1s

...pp1

...pps

(4)

Is is the identity matrix of size s×s. The F -matrix is repeatedp times as the diagonal of an (otherwise zero) block matrix. Itis worth remarking that the X matrix does not appear since theas-is state does not determine the optimal solution directly (butindirectly, through the costs). The optimal connectivity matrixis obtained from the solution vector x to the binary integerprogram. To explain the details, consider again the example.

min cT x

s.t.

2 0 0 −1 0 0 −1 0 00 2 0 0−1 0 0−1 00 0 2 0 0−1 0 0−10 0 0 1 0 1 0 0 00 0 0 0 1 0 0 0 00 0 0 0 1 0 0 0 00 0 0 0 0 0 1 0 10 0 0 0 0 0 0 1 00 0 0 0 0 0 0 1 0

x1x2x3x4x5x6x7x8x9

≥

000110101

(5)

The goal function to minimize is straightforward. The costsassociated with each decision are simply summed.

The first three inequalities express the constraint that aprocess must be connected to an existing system. x1 en-codes whether Codex is there, x4 whether it is connected

to Register new customer and x7 whether it is con-nected to Register new employee. Since the inequality2x1− x4− x7 ≥ 0 is fulfilled only if x1 = x4 = x7 = 0 or ifx1 = 1 (recall that all xi ∈ B), the first inequality encodes theconstraint with regard to the Codex system. The number 2appearing in the inequality is the total number of processes thata system can potentially be connected to, i.e. p. The second andthird inequalities similarly encode the constraint with regardto the other two systems.

Inequalities 4-6 express the requirements of the first process,Register new customer. The zero matrix blocks to theleft and right express that decisions x1, x2, x3 and x7, x8, x9

are irrelevant. What remains is the requirement that the Fmatrix block, corresponding to the functions provided by thesystems, multiplied with the systems connected x4, x5, x6

must yield at least the required functions of the processRegister new customer as encoded in the first line ofmatrix P . Inequalities 7-9 similarly encode this requirementwith regard to systems connections x7, x8, x9 and the func-tions required by the process Register new employeeas encoded in the second line of matrix P .

Given an accurate cost vector c, solving Eq. 5 yields anoptimal consolidation decision.

IV. THE COSTS OF CONSOLIDATION

Costs play a vital role in finding optimal solutions. Inthis section, cost estimates are addressed. Only costs for newsystem-process connections are considered. These are brokendown into two logical parts, viz. (i) costs of changing theIT systems and (ii) costs of changing the business processes.The costs for maintaining systems are not addressed here. Inpractice, finding these costs is relatively easy, as it is mostlya matter of book-keeping.

A. Costs of changing the IT systems

In [17] an IT investment cost taxonomy is presented:(i) project management costs, (ii) procurement costs, (iii) tech-nical implementation costs, (iv) human/organizational imple-mentation costs and (v) operation and maintenance costs.Factors (i)-(iv) drive capital expenditure while factor (v) coversthe operational expenditure.

We limit our discussion here to the direct costs incurredwhen an existing application is reused. Assuming reuse, wecan set procurement costs close to zero, leaving only pro-curement costs of additional hardware. Slight source codechanges may drive the technical implementation costs some-what. Migration of data and application integration into a neworganizational unit will drive some costs. Additional testingalso adds a little to the expenditure.

The human/organizational costs are usually much higherthan technical costs, and are addressed below. According to[18], the ”indirect costs” of IT investments, i.e. the costs ofchanging the business and making users work differently, canbe as much as four times higher than the direct costs relatedto the technical IT investment.

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For the purpose of this example, we stipulate that the costof migrating an application to a new business function is onecost unit, and the cost of each addition of a function is twocost units. These figures are somewhat arbitrary, but usedfor illustrative purposes. To apply the method in practice, anumber of published cost estimation methods can be used,e.g. [19] or [20].

B. Costs of changing the business processes

Whenever an application is changed, this entails somechanges in the corresponding business process. At this stagecost estimates must serve as input into the optimization [21].The taxonomy in [22] gives an overview of fundamentalbusiness process modifications: (i) addition, (ii) deletion,(iii) moving, (iv) adjusting and (v) creation of a businessprocess related construct.

By this typology, fine-grained cost estimates can be assignedto each change operation. In the example, we stipulate thatthe cost of an individual modification operation is four costunits. This is done for brevity and because the specific costsof these changes may vary from one organization to the nextdue to different organizational cultures, company regulations,accounting rules, etc. The main point is that this cost figure isgreater than the implementation cost figure introduced above.To apply the method in practice, a number of published costestimation methods can be used, e.g. [21].

C. Cost as a single criterion

Following this digression into costs, one might worry thatcost is not the only factor influencing a decision on consolida-tion. Factors such as flexibility, reduction of risk, reduction ofthe expertise needed to maintain a heterogeneous IT landscape,etc. are also important. This is a valid concern, but thevital insight here is that other factors could and should beexpressed in terms of costs. For example, the expertise neededto maintain a heterogeneous IT landscape is readily convertibleinto salary costs for hiring that expertise. Similarly, (reductionof) risk is managed every day in the CFO office, convertingrisks into interest rates and opportunity costs. Setting actualcosts (i.e. hard numbers) on all the multiple criteria is notmerely a way to reduce or simplify the problem, it is a wayof making otherwise intangible criteria explicit and useful.Using cost as a single objective makes a decision processmore rigorous and transparent, and it also allows the use ofsensitivity analysis to see the impact of different assumptions.If proper care is taken to include all the relevant cost (andindeed care does need to be taken!), then a BIP model mightvery well reflect all the relevant decision-making aspects.

V. CASE STUDY

As a practical example, the method is applied to a real-world application consolidation scenario taken from a largeEuropean power supplier. The processes, functions, and sys-tems described in the following are all accurate depictions ofthe actual situation at the company. However, no real costestimates were available from the company.

The case at hand consists of three similar, but not iden-tical, switching processes at three different locations withinthe company group. In Fig. 2 one process is shownin detail, including a variation from one of the otherprocesses. The processes require five main functions: f1

Create Data Object (DO) & Doc, f2 Check Doc,f3 Terminate and register, f4 Receive meterdata, and f5 Confirmation. The functions are offered byseven major systems, distributed among the three locations.The full list of processes, functions and systems is givenin Table I. The process requirements and systems provisionsare depicted as a graph structure in Fig. 3, sub-figure a),while the actual connections between processes, services andapplications are depicted in sub-figure b).

The corresponding binary connectivity matrices P (require-ments of processes for functions), X (as-is connections fromprocesses to systems) and F (functions provided by systems)are the following:

P =

f1 f2 f3 f4 f5

p1 1 1 1 1 1

p2 1 0 1 1 0

p3 1 1 1 1 0

X =

s1 s2 s3 s4 s5 s6 s7

p1 1 0 0 1 0 0 0

p2 0 1 0 0 1 0 1

p3 0 0 1 0 0 1 0

F =

s1 s2 s3 s4 s5 s6 s7

f1 1 0 1 0 1 0 0

f2 0 0 1 1 0 0 0

f3 0 1 0 1 1 1 1

f4 0 0 0 1 1 1 0

f5 1 0 0 0 0 0 0

A. Assigning costs

In this case-study, as no costs were available from thecompany, reasonable cost estimates based on the methodsdescribed in the previous section were used:

s1 s2 s3 s4 s5 s6 s7cs = (48 147.33 48 244 224 116 204)

CX =

s1 s2 s3 s4 s5 s6 s7

p1 0 99999999 11 0 61 43 41

p2 13 0 13 47 0 15 0

p3 11 99999999 0 75 51 0 61

TABLE IPROCESSES, FUNCTIONS AND SYSTEMS IN THE EXAMPLE.

Processes Functionsp1 Switch process location 1 f1 Create DO & Docp2 Switch process location 2 f2 Check Docp3 Switch process location 3 f3 Terminate and register

f4 Receive meter dataf5 Confirmation

Systemss1 CRM (1)s2 Online Customer Service App (2)s3 Utility Management App (3)s4 Utility Management App (1)s5 Utility Management App (2)s6 CRM (3)s7 Integration Platform (2)

2424

Po

we

r S

up

plie

rCreate

customer

object

Check

contract

Send

confirmation

Create

switch

contract

Send

switch req

to operator

Receive

answer

Send

termination to

old suppl.

Receive

meter data

POSITIVE

NEGATIVE

Receive

answer

OK

NOT OK

Outcome of

contract check? Answer OK?

Receive

customer

data

EXAMPLE VARIATION

IN OTHER PROCESS

Fig. 2. A sample process for switching customers at a power supply company.

It should be noted that the zero entries in the CX matrixcorrespond to the one entries in the X matrix, i.e. existingconnections are free, only new ones entail costs. Impossibleconnections are modeled with a cost of 99999999.

The pure system costs of making connections are set toone cost unit. These costs are associated with data migration,system integration etc. For every additional function to beimplemented or changed, a cost of two cost units is incurred.

Since the actual system costs were not discussed in theprevious section, they merit a note here. In practice, findingthese costs is relatively easy, as it is mostly a matter of book-keeping: what do the existing systems cost on a yearly basis,and what is the corresponding net present value? However, inthis case study, no books were available. Instead, the followingreasoning has been applied:

Let a system have a total life-time cost of c. Maintenanceis about 60-70% of c according to [23] so pure installation isthe remaining 30-40 %. Assuming (i) that a connection shouldbe about half as expensive as a new installation of a systemand (ii) that a connection does not have any maintenance cost,a connection should have a net present value of about 0.15cto 0.20c. That is roughly a fourth of the system 0.6c − 0.7cspent on maintenance, which is the net present value quantitysought here. (Recall that the installation is a sunk cost here,and should not be considered in the optimization problem.)Based on this, the system costs have been set to four times theaverage of the connection costs involving the systems, exceptthe cost for Online Customer Service App (2) which has beenset to the average of the other systems (since this system onlyfeatures infeasible connections).

B. Results

The optimal solution to the case study problem is the matrix:

X =

s1 s3 s6

p1 1 1 1

p2 0 1 1

p3 0 1 1

Four out of seven systems are disconnected and removed,as compared to the as-is state.The outcome is illustrated inFig. 3. Sub-figure a) illustrates the process requirements forfunctions, and the system provisions of functions, respectively.

Any feasible solution must fulfill these constraints, i.e. providethe processes with the functions they need, using systems thatcan deliver those functions. Sub-figure b) illustrates the as-is solution: three processes connected to seven systems. Inthe lower row, the associated costs are shown. In the as-isstate, the cost is maintenance only. Sub-figure c) illustrates theoptimal to-be solution. Here, only three out of seven systemsare retained. As seen in the cost diagram, this corresponds toa significantly lower systems cost. The price is new and costlyprocess-system connections, but the net result is improved.

VI. DISCUSSION

One key strength of the method proposed is its basis – ageneral mathematical framework for optimal decision making.Another key strength of the method is that it evaluates costswhile considering the functional structures of consolidationalternatives, going beyond intermediaries such as (i) redun-dancy identification or redundancy classification and (ii) to-be application portfolio design. This makes for more relevantdecision support, as costs are a primary driving force behindconsolidation efforts.

There is a flip-side to this cost focus, however. The methodis – obviously – sensitive to the cost estimates used. Ifaccurate cost assessments cannot be found, the resulting to-be architecture will be correspondingly inaccurate. Therefore,the cost estimates used should be based on reliable publishedsources such as [17], [20] and [22]. Finding the cost ofmaintaining systems is largely a matter of book-keeping.

VII. CONCLUSIONS

The contribution of the present paper is two-fold: First, theproblem of IT consolidation is addressed as an optimizationproblem, specifically as one of binary integer programming.This yields a general framework for thinking about and solvingsuch problems. Second, some cost aspects of modifications tobusiness processes and IT systems and the impact of thesecosts on IT consolidation are detailed.

While we have demonstrated the general applicability ofour method, more thorough evaluation will be needed. Futureresearch should focus on empirical validation of the methodproposed.

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RequirementsProcesses

Functions

Systems

As-is connectionsProcesses

Systems

Optimal to-be connectionsProcesses

Systems

Systems Connections Total0

500

1000

1500Costs as-is

Systems Connections Total0

500

1000

1500Costs to-be

p1

p2

p3p

1p2

p3

p1

p2

p3

s2 s3s1 s5 s6s4 s7 s3s1 s6s2 s3s1 s5 s6s4 s7

f1 f2 f3 f4 f5

a) b) c)

d) e)

Fig. 3. Sub-figure a) illustrates the process requirements for functions, and the system provisions of these. Sub-figure b) illustrates the as-is solution, sub-figurec) illustrates the optimal to-be solution, while sub-figures d) and e) illustrate the costs.

REFERENCES

[1] D. Minoli, Enterprise Architecture A to Z. Boca Raton: AuerbachPublications, 2008.

[2] T. Bucher, R. Fischer, S. Kurpjuweit, and R. Winter, “Analysis andapplication scenarios of enterprise architecture: An exploratory study,” inEDOCW ’06: Proceedings of the 10th IEEE on International EnterpriseDistributed Object Computing Conference Workshops. Washington,DC, USA: IEEE Computer Society, 2006, p. 28.

[3] D. Dreyfus and B. Iyer, “Managing architectural emergence: A concep-tual model and simulation,” Decision Support Systems, vol. 46, no. 1,pp. 115–127, 2008.

[4] M. Holm-Larsen, “ICT integration in an M&A process,” in Proceedingsof the 9th Pacific Asia Conference on Information Systems (PACIS).Paper 95., 2005.

[5] D. Hancock, D. B. Humphrey, and J. A. Wilcox, “Cost reductions inelectronic payments: The roles of consolidation, economies of scale, andtechnical change,” Journal of Banking & Finance, vol. 23, no. 2-4, pp.391–421, February 1999.

[6] T. Iijima, “A practical way to manage IT costs,” Journal of CorporateAccounting & Finance, vol. 16, no. 5, pp. 13–20, 2005.

[7] K. Joshi and S. Pant, “Development of a framework to assess andguide IT investments: An analysis based on a discretionary–mandatoryclassification,” International Journal of Information Management, 2008.

[8] S. Buckl, A. Ernst, J. Lankes, K. Schneider, and C. Schweda, “A patternbased approach for constructing enterprise architecture managementinformation models,” Wirtschaftsinformatik, pp. 145–162, 2007.

[9] J. Schelp and R. Winter, “Management for Heterogeneous InformationSystems,” in Agile Information Systems, K. C. Desouza, Ed. Elsevier,2007, ch. 11, pp. 134–149.

[10] H. Hess, “Aligning technology and business: Applying patterns forlegacy transformation,” IBM Systems Journal, vol. 44, no. 1, pp. 25–46, 2005.

[11] M. Op’t Land, H. Zwitzer, P. Ensink, and Q. Lebel, “Towards afast enterprise ontology based method for post merger integration,” inProceedings of the 2009 ACM symposium on Applied Computing. ACMNew York, NY, USA, 2009, pp. 245–252.

[12] C. Stewart, T. Kelly, and A. Zhang, “Exploiting nonstationarity forperformance prediction,” SIGOPS Oper. Syst. Rev., vol. 41, no. 3, pp.31–44, 2007.

[13] A. Spellmann, K. Erickson, and J. Reynolds, “Server consolidation usingperformance modeling,” IT Professional, vol. 5, no. 5, pp. 31–36, 2003.

[14] U. Franke and P. Johnson, “An enterprise architecture framework forapplication consolidation in the Swedish Armed Forces,” in EnterpriseDistributed Object Computing Conference Workshops, 2009. EDOCW2009. 13th, Sep. 2009.

[15] F. Hillier and G. Lieberman, Introduction to operations research.McGraw-Hill Science, Engineering & Mathematics, 2004.

[16] E. Balas, F. Glover, and S. Zionts, “An additive algorithm for solvinglinear programs with zero-one variables,” Operations Research, vol. 13,no. 4, pp. 517–549, 1965.

[17] P. Narman, T. Sommestad, S. Sandgren, and M. Ekstedt, “A frameworkfor assessing the cost of it investments,” in Proceedings of PortlandInternational Conference on Management of Engineering Technology(PICMET 2009), Aug. 2009, pp. 3154–3166.

[18] P. Love, Z. Irani, A. Ghoneim, and M. Themistocleous, “An exploratorystudy of indirect ICT costs using the structured case method,” Interna-tional Journal of Information Management, vol. 26, no. 2, pp. 167–177,2006.

[19] B. Boehm, R. Madachy, and B. Steece, Software Cost Estimation withCocomo II with Cdrom. Prentice Hall PTR Upper Saddle River, NJ,USA, 2000.

[20] R. Lagerstrom, U. Franke, P. Johnson, and J. Ullberg, “A Methodfor creating enterprise architecture metamodels - Applied to systemsmodifiability analysis,” International Journal of Computer Science andApplications, vol. 6, no. 5, pp. 89–120, 2009.

[21] W. J. Kettinger, J. T. Teng, and S. Guha, “Business process change: Astudy of methodologies, techniques, and tools,” MIS Quarterly, vol. 21,no. 1, pp. 55–98, 1997.

[22] O. Holschke, J. Rake-Revelant, P. Offermann, and U. Bub, “Improvingsoftware flexibility for business process changes,” Business and Infor-mation Systems Engineering, vol. 2, no. 1, pp. 3–13, 2010.

[23] S. Yip and T. Lam, “A software maintenance survey,” in SoftwareEngineering Conference, 1994. Proceedings., 1994 First Asia-Pacific,1994, pp. 70–79.

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