A COST-ORIENTED TOOL TO SUPPORT SERVER

CONSOLIDATION

Danilo Ardagna, Chiara Francalanci

Dipartimento di Elettronica e Informazione, Politecnico di Milano, Via Ponzio 34/5, 10133, Milano, Italy

Gianfranco Bazzigaluppi, Mauro Gatti, Francesco Silveri

IBM Italy, Circonvallazione Idroscalo, 20090 Segrate, Milano, Italy

Marco Trubian

Dipartimento di Sicenze dell’Informazione, Università degli Studi di Milano, Via Comelico 39, 20135 Milano, Italy

Keywords: IT Costs, Distrib

uted systems, Server Consolidation, Optimization

Abstract: Nowadays, Companies perceive the IT infrastructure as a commodity not delivering any competitive

advantage and

usually, as the first candidate for budget squeezing and costs reductions. Server

consolidation is a broad term which encompasses all the projects put in place in order to rationalize the IT

infrastructure and reduce operating costs. This paper presents a design methodology and a software tool to

support Server Consolidation projects. The aim is to identify a minimum cost solution which satisfies user

requirements. The tool has been tested by considering four real test cases, taken from different geographical

areas and encompassing multiple application types. Preliminary results from the empirical verification

indicate that the tool identifies a realistic solution to be refined by technology experts, which reduces

consolidation projects costs, time and efforts.

1 INTRODUCTION

There is increasingly clear evidence that IT

contribution to productivity growth is sizeable and

positively affects both firms and countries (Dedrick

2003). Yet, the benefits of IT investments exhibit a

striking variance among firms and the continual

growth of the portion of capital spending devoted to

IT is getting to unsustainable levels. There is

therefore an increasing focus on curbing IT costs,

notably on those areas not delivering visible and

short term business benefits. The IT infrastructure,

being often perceived as a commodity not delivering

any competitive advantage (Carr 2003), is thus the

first candidate for budget squeezing; with ultimate

goals as heterogeneous as cost savings or freeing

resources for more business-related IT investments.

Server consolidation is a broad, weakly defined term

whic

h encompasses all the projects put in place in

order to rationalize the IT infrastructure. Although

each project is unique, we can identify in each server

consolidation project five main phases: a) goals and

constraints identification; b) data gathering; c)

analysis; d) solution test; and e) deployment.

For most of the projects the key goal is to reduce

costs without adve

rsely affecting the key operational

requirements, i.e. performance, scalability and

dependability. Thus the architectural design has to

be optimized against the cost variable, while the

operational requirements act as constraints.

The data gathering phase aim

s at collecting basic

information about the to-be–consolidated servers

(e.g. server configuration, OS, major applications)

and workload data (e.g. peak CPU utilization).

Since no non-intrusive tool existed in the market

able to gather workload data, IBM has developed its

own tool (IBM CDAT). All the customer data

analyzed in this article were obtained using this tool.

During the analysis phase the data gathered during

the previ

ous phase are sorted out and architectural

decisions are taken. The project team needs at least

to: a) to select the consolidation technique; b) select

the server models; c) size the systems; d) assign each

application to a server e) determine the optimal

323

Ardagna D., Francalanci C., Bazzigaluppi G., Gatti M., Silveri F. and Trubian M. (2005).

A COST-ORIENTED TOOL TO SUPPORT SERVER CONSOLIDATION.

In Proceedings of the Seventh International Conference on Enterprise Information Systems - DISI, pages 323-330

 SciTePress

location (if appropriate). This article is focused on

identifying an optimal solution to problems (b), (d)

and (e), where optimal means the cheapest solution

meeting operational requirements.

The three key operational requirements are

performance, scalability and dependability. As to

performance, the aforementioned IBM CDAT tool

measures the average and peak CPU utilization; it is

therefore possible to select the server models and

configuration in such a way that performance

requirements are met. As to scalability, the model

will be extended to cover scalability issues in future

work. As to dependability, two High Availability

(HA) cluster configurations have been considered

(see Marcus 2000): a) 1-to-1 (symmetric), b) load

sharing. 1-to-1 HA clusters consist of two nodes that

deliver different services (even when the two nodes

host the same type of application the two servers

deliver different services). 1-to-1 clusters can be

configured in asymmetric mode (also known as

Active-Passive) or symmetric mode (also known as

Active-Active). In the asymmetric configuration

server applications run on the two servers but only

one machine delivers service to users while the

second one is in standby. In the Active-Active

configuration, vice versa, server applications are

installed on the two machines but only one instance

of a server application is executed on the cluster.

The asymmetric configuration makes a suboptimal

usage of the resource and therefore it is implemented

only when the symmetric configuration is not

supported. Load sharing HA clusters consist of two

or more nodes that deliver the same service. The

multiple nodes extensions of 1-to-1 HA cluster (e.g.

N-to-1 in which multiple nodes can fail over one

standby node), albeit considered in the analysis, are

not widespread enough in the Intel-based servers

market and therefore have not been described in this

article.

This paper is the result of a joint project between

IBM and Politecnico di Milano. In a previous work

(see (Ardagna and Francalanci 2002), (Ardagna et

al. 2004) and references therein), we have developed

a cost oriented methodology and a software tool,

ISIDE (Information System Integrated Design

Environment) for the design of the IT architecture.

In this paper, we apply our tool to four server

consolidation projects implemented by IBM for their

customers, in order to evaluate the quality of our

solutions. The results we obtained show that ISIDE

can identify a low cost candidate solution, which can

be refined by the project team experts, which

reduces the cost and time of server consolidation

projects. The current version of the tool does not

consider scalability issues. However, the tool can be

extended in order to entirely support the server

consolidation process considering additional

constraints.

This paper is organized as follows. The next section

reviews previous approaches provided by the

literature. Section 3 discusses a model for an

Enterprise-wide Information System which supports

a server consolidation project. Section 4 describes

the current version of ISIDE which has been adopted

to investigate case studies discussed in Section 5.

Conclusions are drawn in Section 6.

2 RELATED WORK

A server consolidation project is a special case of

design of an IT infrastructure. Modern

infrastructures are comprised of hardware and

network components (Menascé and Almeida 2000).

Since hardware and network components

cooperatively interact with each other, the design of

the IT infrastructure is a systemic problem. The

main systemic objective of infrastructural design is

the minimization of the costs required to satisfy the

computing and communication requirements of a

given group of users (Jain 1987; Blyler and Ray

1998). In most cases, multiple combinations of

infrastructural components can satisfy requirements

and, accordingly, overall performance requirements

can be differently translated into processing and

communication capabilities of individual

components. These degrees of freedom generate two

infrastructural design steps: a) the selection of a

combination of hardware and network components;

b) their individual sizing.

Cost-performance analyses are executed at both

steps. Performance analyses receive a pre-defined

combination of components as input and initially

focus on the application of mathematical models to

define the configuration of each component

(Lazowska et al. 1984; Menascé and Almeida 2000).

Conversely cost analyses start at a system level, to

identify a combination of components that

minimizes overall costs, which is initially calculated

from rough estimates of individual components’

configurations and corresponding costs (Blyler and

Ray 1998; Zachman 1999). The evaluation of costs

of individual components is subsequently refined

based on more precise sizing information from

performance analyses.

The literature provides various approaches to

support the design process, especially in the

performance evaluation field (Menascé and Gomaa

2000) or for specialized applications (Gillman et al.

2000) and often only a limited set of architectural

variables or sub-problems are considered (e.g. the

ICEIS 2005 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION

324

configuration of an application server (Xi et al.

2004)).

A scientific approach has been rarely applied to

cost minimization and a rigorous methodological

support to cost issues of infrastructural design is still

lacking. Our model draws from (Jain 1987) the

approach to the representation of infrastructural

design alternatives as a single cost-minimization

problem; however design variables and steps have

been significantly extended to account for the

complexity of modern computer systems and to

support server consolidation projects. The cost

optimization problem is NP-hard and in previous

works (Ardagna et al. 2004) we have proposed an

heuristic solution based on the tabu search algorithm

which will be briefly discussed in Section 4.

3 THE SYSTEM MODEL

The model of the Enterprise Information System we

consider is depicted in Figure 1. The Internet ties

multiple local networks, whose number and

extension depends on the topology of organizational

sites and on the location of users. The Infrastructure

is associated with a “Total Cost of Ownership”

(TCO), defined as the summation of network costs,

investment and management costs of all

infrastructural components (Faye Borthick and Roth

1994). The server consolidation problem consists of

the joint problem of selecting new hardware

components and localizing them while minimizing

the TCO of the system, according to operational

requirements. The set of servers to be consolidated

is a subset of application servers adopted in the

current enterprise infrastructure which execute

server applications (web servers, e-mail servers,

DBMSs, etc.) and thin/Hybrid Fat Client (HFC)

servers, i.e. servers which support users which adopt

thin or HFC client computers (Ardagna et al. 2004)

and access a Metaframe or Terminal Services

environment (Microsoft 2003).

The system is described by the following

fundamental variables:

• Organization sites S

, defined as sets of

organizational resources (users, premises and

technologies) connected by a LAN.

• User classes C

, defined as a group of users with

a common application profile located in an

organization site, where an application profile is

characterized by the set of applications and

functionalities, computing requirements, and

user think time.

Figure 1: Enterprise Information System Model.

• Applications A

, defined as a set of

functionalities that can be accessed by

activating a single computing process.

Applications are characterized by computing

and memory (primary and secondary)

requirements.

• Databases D

, defined as separate sets of data

that can be independently stored, accessed and

managed. Note that DBMSs are supposed to be

specified as server applications and,

accordingly, databases are simply described by

the size of secondary memory that they require.

Applications and databases are the main drivers of

architectural design of server farms, while the

specification of sites and user classes is critical to

select and size network components. All users in a

user class are supposed to use the same set of

applications. Application computing capacity

requirements are evaluated by considering the

average utilization of the server which support their

execution in the original system. Note that a

thin/HFC servers in a server consolidation project

can be modelled as a single application. User classes

and applications exchange data. The data exchange

is modelled as a weighted directed graph whose

nodes represent an application or a user class and the

weight of each edge (i,j) represents the average

bandwidth required to support data exchange

between node i and j.

Different sites are constrained to be connected

through an IP-based Virtual Private Network (VPN).

VPNs have been selected due to their flexibility in

realizing point-to-point connections. In this way,

network design is performed by sizing link capacity

and taking into account the associated costs. This

provides a necessary input for the evaluation of

overall infrastructural costs and allows the analysis

of the impact of Internet costs on infrastructural

design choices.

The optimization domain can be limited by the

project team by specifying one or multiple

A COST-ORIENTED TOOL TO SUPPORT SERVER CONSOLIDATION

325

constraints on the association between a) hardware

components which will constitute the new

consolidated system; b) existing applications.

Constraints are defined by the project team before

optimization and represent an input to the software

tool presented in the next section. The set of all

possible optimization constraints is defined as

follows:

• Client Typology: each user class can be

associated to a client typology (thin, fat or

hybrid). The target operating system and,

possibly, remote protocol are also specified.

• Allocation of applications and thin/HFC users:

server application can be supported by the same

cluster (i.e., a set of nodes which work

collectively as a single system). This is

specified by consolidation island

, i.e., sets of

server applications A

that can be possibly

allocated to the same cluster. The set of

consolidation islands is denoted as G

• In the same way, thin and HFC servers can be

shared among different user classes associated

with thin/HFC clients and consolidation island

are defined accordingly. The set of

consolidation islands for thin/HFC servers is

denoted as G

. Note that G

is a partition since

usually user classes of an Information System

are partitioned for security reason or privileges.

If the cardinality of a consolidation island is n,

-1 different allocations of server applications

or user classes can be selected (that is, the

consolidation island’s power set, excluding the

empty set). For example, if there are two

instances A

and A

of web servers the

consolidation island

={A

, A

} is introduced,

then the set of candidate clusters is {A

, A

} and {A

For each consolidation island

the following

technology constraints are specified:

• The family of servers that will be adopted for

the server consolidation (e.g., IBM xSeries 345

and xSeries 440).

• The virtualization of the servers in the

consolidation island by means of a Virtual

Machine Monitor (VMM), e.g. VMware ESX

Server. VMMs allow multiple operating

systems to run on the same server. Since most

applications do not scale up, VMMs increase

thereby server utilization.

• The value of availability AV required for the

hardware platform.

• The fault tolerant schema implemented in the

consolidated system (1-to-1 or load sharing).

• Thin/HFC servers and application servers, and

corresponding user classes/server applications,

can be constrained to be located in a specific

organizational site.

As discussed above G

is a partition, while

consolidation islands in G

can overlap. In this way

multiple tiers allocation for server application can be

defined. As an example, a servlet engine can be

executed with a web server or an application server

or as an independent tier, vice versa application and

DBMS servers are usually allocated to individual

machines (possibly supporting multiple application

instances) for management and security reasons.

Such a situation can be characterized by defining

three consolidation islands: the first consolidation

island contains web servers and servlet engines; the

second consolidation island contains the servlet

engines and the application servers and the third

consolidation island contains all of DBMS

applications.

Technology constraints are satisfied as follows:

Computing Capacity

The computing capacity of thin/HFC servers is

evaluated as the maximum value of MIPS required

by applications that are executed remotely

multiplied by the number of concurrent users of the

corresponding user class (Ardagna and Francalanci

2002). Servers are selected to provide a computing

and storage capacity that guarantee a utilization of

CPU and disk lower than 60% (Menascé and

Almeida 2000; Ardagna et al. 2004) under the

hypothesis of load balancing in the cluster and a

single faulty server of the consolidated system.

With values of utilization greater than 60%, small

variations of throughput would cause a substantial

growth of response time and, overall, performance

would become unreliable. This empirical rule of

thumb, which is commonly applied in practice

(Menascé and Almeida 2000; Microsoft 2003), has

been provided a formal validation. It has been

formally demonstrated that a consolidation island of

a-periodic tasks will always meet their deadlines as

long as CPU and disk utilization of the bottleneck

resource are lower than 58% (Abdelzaher et al.

2002). Note that performance analyses should follow

cost analyses to refine sizing according to a formal

queuing model. The aim of the proposed tool is to

evaluate a large number of alternative solutions and

find a candidate minimum-cost infrastructure that

can be analyzed subsequently by the project team by

applying fine-tuning performance evaluation tech-

niques. The prediction of computing requirements

for applications on the consolidated system is

evaluated by benchmarking data (e.g. SpecInt, TPC-

C). If a Virtual Machine Monitor is adopted, then

corrective factors, which take into account the

system overhead for the virtualization both for CPUs

and disks, are considered.

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326

Servers Availability

The availability required by a single server of the

consolidated system depends on the fault tolerance

schema required for each consolidation island. If the

load sharing is implemented, then the availability

Server

of each of the physical servers which

compose a cluster of N machines is evaluated as

follows:

this equation is derived by the relation that evaluates

the availability of parallel systems (Trivedi 2002):

The cluster which can support a given set of

application is evaluated by an exhaustive search;

anyway the enumeration can be stopped before

reaching the maximum number of servers which can

be supported by server applications (Ardagna et al.

2004).

Vice versa, if the 1-to-1 schema is selected, then a

cluster of two machines is identified as a solution

and the availability of each server is simply given

by:

Note that, in this latter case, the system has to be

sized in order to guarantee that a single server can

sustain the overall application load independent of

the configuration Active-Active or Active-Passive

adopted.

Network bandwidth requirements

Once physical hardware resources have been

selected and servers have been localized to

organization sites, the VPN is designed and sized.

Each site is associated with its total input and output

bandwidth requirements, calculated as the

summation of input and output bandwidth

requirements of all client/server requests exiting or

entering the site and of thin clients and HFCs

accessing remote servers. The bandwidth

requirements of an active user of class C

accessing a

remote thin/HFC server are evaluated according to

professional benchmarks as a function of think-time

and of the remote protocol (Microsoft 2003). The

capacity of the physical VPN, referred to as physical

bandwidth, is then calculated according to

Kleinrock’s model:

Physical-Bandwidth=Total-Bandwidth+l/T,

where T is the time latency and l is the average

packet size, which for IP-based VPNs can be

empirically set to 200 ms and 550 bytes, respectively

(Yuan and Strayer 2001).

4 SOFTWARE TOOL

The selection of a cost-minimizing combination of

hardware and network components that satisfy

organizational requirements is a complex design

problem with multiple degrees of freedom. The

corresponding optimization problem not only

embeds the structure of NP-hard problems, but also

represents a challenge for well-structured heuristic

approaches. To reduce complexity we have applied a

problem decomposition technique. We have

identified three sub-problems which are sequentially

solved. The given solution is improved by a final re-

optimization step. The following decomposition is

performed:

• Thin/HFC servers optimization: Thin clients

and HFCs are assigned a minimum-cost clusters

to support the remote execution of client

applications. Clusters are assigned to their

clients’site to minimize network communication

costs. The solution of the thin/HFC server

optimization sub-problem is modelled as a set

partitioning problem (Papadimitriou and

Steiglitz 1982) and is solved by a state of the art

integer linear programming tool.

• Server optimization: Server applications are

assigned to minimum-cost clusters that satisfy

computing requirements and constrains. This

optimization sub-problem is formalized as a set

partitioning problem and is solved as the

previous problem.

• Server localization: Server machines identified

by solving the previous sub-problems are

allocated to sites by minimizing overall network

and management costs. This optimization sub-

problem is formalized as an extension of a min

k-cut problem (Lengauer 1990) and is solved by

implementing a tabu search heuristic (Glover

and Laguna 1997).

The problem decomposition is discussed in

(Ardagna et al. 2004). The decomposition of the

overall optimization problem into three sub-

problems does not guarantee that the final solution is

a global optimum. Hence, an overall re-optimization

process based on a tabu-search approach has been

implemented to improve the (possibly) local

optimum obtained by separately solving the three

sub-problems.

5 EMPIRICAL ANALYSES

This section provides empirical evidence of the

quality of the solution which can be obtained by the

software tool. Analyses focus on four case studies,

which have substantially different requirements. In

A COST-ORIENTED TOOL TO SUPPORT SERVER CONSOLIDATION

327

the first case study, a single site system is considered

and the server consolidation includes only a limited

number of servers of the customer infrastructure.

The second test case considers a more complex IT

infrastructure extended over three sites. The third

server consolidation project is based on new

technologies, i.e., server virtualization and blade

servers; finally, fault tolerance issues are addressed

in the last case study. The solutions provided by the

software tool have been compared with those of the

project team. The project team solution considers

always peak CPU utilization load provided by IBM

CDAT for the sizing of the hardware platform. Vice

versa, the solution provided by the software tool

considers two different scenarios. The first scenario

considers the peak CPU utilization load, while the

second scenario, in a more conservative way,

assumes that the CPU utilization is 100%. This

second solution will provide as a result an hardware

infrastructure whose total computing capacity is

greater or equal to that of the initial system

configuration. Such solution can be used as

benchmark even if it provides an over-sized estimate

of system configuration and costs.

The TCO is evaluated by benchmarking data

(Ardagna and Francalanci 2002, Ardagna et al.

2004) over a three year period. Management costs

are estimated as a percentage of hardware

investment costs as in (Blyler and Ray 1998). In the

following tables TCO is expressed in Euros.

Server Consolidation Project A

The customer infrastructure includes 134 servers in a

single site but the server consolidation project

considers only 20 of them: 8 e-mail servers, 8 file

servers and 4 print servers. The peak utilization of

the set of servers target of the consolidation varies

between 5% and 77%. The project team has

considered two different alternatives:

• Alternative a): file and print servers are

consolidated on separate machines.

• Alternative b): file and print servers are

consolidated on the same set of machines.

From the methodological point of view, in the first

alternative three different consolidation islands

and

are defined in G

, one for each application

type. Vice versa, in alternative b, only two islands

are specified.

includes file and print servers,

includes e-mail application servers. The server

families considered in the server consolidation

project are xSeries 345 and xSeries 360. No

availability constraints (in terms of availability

required and fault tolerance schema) are introduced.

Table 1 reports the solution provided by the IBM

project team, for the two alternatives,

Table 2 reports the solution identified by the

software tool.

Table 1: Project A IBM Project Team Solution.

Altern.

Initial

numb. of

servers

Numb. of servers

of the cons.

Solution

TCO

A 6 160.544

4 166.112

Table 2: Project A Software Tool Solution.

Altern.

Initial

numb. of

servers

Numb. of servers

of the cons.

Solution

TCO

peak

CPU

12 111.620

100%

CPU

20 170.042

peak

CPU

12 106.422

100%

CPU

20 168.658

The software tool solution is cheaper in the peak

utilization scenario of about 30%, while in the 100%

scenario is more expensive than the IBM solution of

about 5%. Both solutions are based on xSeries 345

and xSeries 360 servers. In the peak utilization

scenario, overall the same computing capacity is

implemented for both the software tool and the

project team solution, but with different

configurations. The software tool solution has lower

costs, but the number of servers identified is twice as

high as those of the IBM solution.

Server Consolidation Project B

The customer infrastructure comprises 62 servers in

three remote sites. The server consolidation project

considers 27 servers which are classified and located

as reported in Table 3. The peak utilization of the set

of servers target of the consolidation varies between

30% and 90%.

Table 3: Server applications in project B.

Site

Application S

Application Server (A) 0 1 0

Application Server+DBMS (AD) 8 0 4

DBMS (DB) 0 2 0

E-mail (E) 1 4 2

Web (W) 0 4 1

Initial numb. of servers 9 11 7

The project team has identified three consolidation

islands:

•

includes applications of type A, AD and DB;

•

includes e-mail servers;

•

includes web servers.

ICEIS 2005 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION

328

The server families considered in the server

consolidation project are xSeries 360 and xSeries

440. No availability constraints are introduced.

Table 4 shows results in terms of total number of

servers and costs for both the IBM project team, and

the software tool solution.

Table 4: Project B Solutions.

Solution

Initial numb.

Of servers

Numb. Of

servers of the

cons. Solution

TCO

IBM 5 149.148

Softw. Tool

peak. CPU

4 61.512

Softw. Tool

100% CPU

6 97.480

Here, the difference between the peak and 100%

utilization solution identified by the software tool

are lower than in project A, since there is a lower

variance in server CPU utilizations. Even the 100%

CPU analysis has a lower cost than the solution

provided by the IBM project team. The main reason

for this difference of about 30%, origins in the

scalability required by the customer which is not

currently supported by the software tool. Indeed, the

minimum cost solution identified by the software

tool introduces only xSeries 360 servers, while the

IBM solution introduces a few xSeries 440 servers

which, with an additional cost of about 20-30%,

provide higher scalability.

Server Consolidation Project C

The customer infrastructure includes 67 servers in

two remote sites. The project addresses a large

portion of the IT infrastructure since 56 servers, are

considered for the consolidation. The peak servers’

CPU utilization varies between 1% and 89%. The

server families target for the server consolidation are

xSeries 445 and BladeCenter. A single consolidation

island is defined, which is supported by a virtual

machine monitor and severs are constrained to be

located in a single site. 30 applications with peak

utilization lower than 5% are modelled as a single

application (this reduce the number of alternatives

explored by the software tool and it is reasonable to

centralize under-utilized applications on a single

server). No availability constraints are introduced.

Results are reported in Table 5. A BladeCenter is

considered as a single server independent of the

number of blades installed. As results show, the

software tool solution is 35% cheaper than the

solution provided by the project team in the peak

utilization scenario. Vice versa the 100% scenario is

more expensive but the system is over-sized (30

severs in the original system have peak utilization

lower than 5%). Both project team and software tool

solutions employ a large number of blades. This is

very attractive since the IT architecture is

centralized, while it could be less interesting from a

cost perspective in a multi-site scenario.

Table 5: Project C Solutions.

Solution

Initial

numb. of

servers

Numb. of

servers of

the cons.

solution

TCO

IBM 5 245.972

Softw. Tool

peak CPU

3 157.916

Softw. Tool

100% CPU

4 280.032

Server Consolidation Project D

The customer infrastructure comprises 32 servers in

two remote sites and the server consolidation project

considers the overall infrastructure. The peak

utilization of the set of servers target of the

consolidation varies between 2% and 90%. The

project team has identified four consolidation

islands:

•

includes 8 DBMS servers, the target system

is xSeries 440;

•

includes 2 e-mail servers, the target system is

xSeries 440;

•

includes heterogeneous servers (mainly web

and application servers), the target system is

BladeCenter;

•

includes heterogeneous servers (network and

file servers), the target system is BladeCenter

with a Virtual Machine Monitor (VMware ESX

Server).

The first two consolidation islands support mission

critical applications and the availability is fixed to

0.99999. The fault tolerance schema implemented is

the load sharing. Results are reported in Table 6.

Table 7 shows the solution identified by the software

tool in the peak utilization scenario for consolidation

islands

and

as a function of hardware

availability.

Table 6: Project D Solutions.

Solution

Initial

numb. of

servers

Numb. of

servers of the

cons. Solution

TCO (€)

IBM 7 354.572

Softw. Tool

peak CPU

6 276.566

Softw. Tool

100% CPU

5 429.202

It is interesting to note that, with current

technologies, low availability requirements can be

satisfied even by introducing a single server. The

100% utilization scenario is very expensive since the

system is oversized, the cost difference between the

peak utilization scenario and the solution identified

by the project team is about 22%.

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329

6 CONCLUSIONS AND FUTURE

WORK

We have developed a software tool which supports

server consolidation projects and identifies the IT

infrastructure of possible minimum cost. The tool is

based on the decomposition of the overall problem

into sub-problems which are sequentially solved by

optimization techniques and whose solution is fine-

tuned by a local search based heuristic (tabu-search).

The tool has been tested by considering four real

projects implemented by IBM system designers.

Results show that our minimum cost solution is

realistic and the cost reduction with respect to an

expert ranges in 20-30% of the total infrastructural

cost. The minimum cost solution can support

technology experts on further analysis and allows to

reduce the time, costs and efforts required in server

consolidation projects.

Availability constraints are included in the

design of the IT infrastructure. The current version

of the software tool identifies a possible minimum

cost architecture, however it does not guarantee that

the solution can be easily upgraded at low costs in

order to support new customer requirements. Future

work will consider scalability issues in the server

consolidation process and analyses will be based on

management costs provided by customers.

ACKNOWLEDGEMENTS

Particular thanks are expressed to Thomas Vezina

(IBM US) who provided most of the data on which

our analysis has been based. Thanks are expressed

also to Mattia Poretti, Andrea Molteni and Simone

Amati for their assistance in data collection and

development activities.

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Table 7: Solutions for DBMS and e-mail servers as a function of availability requirements.

AV=0.9 AV=0.99 AV=0.999 AV=0.9999

Cons.

island

Initial

numb.

servers

Servers

cons.

solution

TCO (€)

Servers

cons.

solution

TCO (€)

Servers

cons.

solution

TCO (€)

Servers

cons.

solution

TCO (€)

8 1 60.712 1 60.712 1 97.860 2 121.446

2 2

16.954

19.752

25.434

29.630

ICEIS 2005 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION

330