ENTERPRISE NETWORK REDESIGN THROUGH

SERVER CONSOLIDATION

Abeer Al-Fadhel, Paulvanna N. Marimuthu and Sami J. Habib

Kuwait University, Computer Engineering Department, P. O. Box 5969 Safat, 13060 Kuwait

Keywords: Utilization, Server Consolidation, Redesign, Optimization, Simulated Annealing.

Abstract: In this paper, we have explored the utilization of existing servers within an enterprise information network

(EIN), and we have proposed redesign operations on servers to identify and remove the low-utilized servers.

The low-utilized servers consume unnecessary power and increase the operational and maintenance cost.

The removal of low-utilized server is viewed as an EIN redesign problem, which removes the low-utilized

servers within the EIN and re-distributes the clients of the purged servers to the remaining servers, thereby

reducing a portion of expenditure on maintenance and operation. We have proposed three approaches on

distributing the clients of removed servers and the approaches are; single server pure random distribution,

selective distribution and multiple servers pure random distribution. We have employed Simulated

Annealing to search for best possible random server/servers in order to distribute the workload of the

removed server, thereby improving the utilization of the remaining servers. The simulation results for a

given EIN with 10 servers and 25 clusters show that our proposed server consolidation approaches improve

the initial average server utilization of around 25% to 60%, 68.5%, and 90% respectively in the proposed

three methods.

1 INTRODUCTION

An enterprise information network (EIN) is a

medium scale computer network, designed to

support the activities of an enterprise such as bank or

scientific institution. Typically, an EIN is comprised

of several servers offering specific services to a

number of clusters containing clients; Moreover, it

offers high quality service and enables enterprises to

coordinate their processes across all functional and

management levels. Many enterprises install

separate servers to house new applications mainly to

prevent the risk of negative effects of one

application on another, when both the applications

are installed on the same server. Commonly,

enterprise data centers use to have many servers to

handle their operations in various departments, as

the installation of a new application on an existing

server with several applications running on it,

sometimes leads to degrade the server performance.

Moreover, the servers purchased by different

decision makers over a period of time to offer

specific services to a group of clients may also

increase the number of servers. As years pass by, the

added servers within the enterprise network have

lead to a situation known as ‘server sprawl’,

whereby too-many servers are running at very low

utilization, consuming physical space and power, as

well as wasting the internal resources of servers such

as CPU, memory, and storage devices. On net result,

server sprawl increases the operational and

maintenance cost of the EIN.

The present economic crisis forces the enterprise

networks to cut unnecessary operational and

maintenance costs. In this paper, we have examined

the utilization of installed servers in an enterprise

network and apply server consolidation techniques

to remove the under-utilized servers and redistribute

the clients of the removed servers to the selected

server from the remaining servers. We have

extended the work of Abdulgafer et al (2010),

whereby the authors redesign a grid-based enterprise

information network through servers consolidation.

We have proposed three different approaches to

distribute the workload of the removed low utilized

server; single server pure random distribution,

selective distribution and multiple servers pure

random distribution. We have employed Simulated

Annealing to search for the best server to distribute

the clients of the removed server that improves the

186

Al-Fadhel A., N. Marimuthu P. and J. Habib S..

ENTERPRISE NETWORK REDESIGN THROUGH SERVER CONSOLIDATION.

DOI: 10.5220/0003854201860191

In Proceedings of the 1st International Conference on Operations Research and Enterprise Systems (ICORES-2012), pages 186-191

ISBN: 978-989-8425-97-3

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

utilization of the existing servers. We have

compared the utilization of the servers present in the

EIN before and after the application of the redesign

process, whereby it shows a maximum increase of

65.2% in the average utilization.

This paper is organized as follows: Section 2,

discusses the related work and Section 3 describes

the server sprawl problem. The server consolidation

methods are presented in Section 4. Section 5

elaborates on Simulated Annealing approach and

Section 6 presents the experimental results and

analysis, and Section 7 concludes our work.

2 RELATED WORK

Several studies were carried out by various

researchers to devise techniques to reduce the costs

of enterprise network by improving the utilization of

installed servers. A decision model for server

consolidation in data centers was presented by

Speitkamp and Bichler (2010) in order to minimize

the costs of servers in terms of hardware costs (e.g.,

CPU and memory bandwidth). By optimally

allocating virtual servers to physical servers, the

authors reduced the hardware cost.

Spellmann, et al. (2003) applied a performance

modelling and stepwise refinement to analyze the

consolidation alternatives before making any

physical changes. They defined three consolidation

alternatives, which were centralization, physical

consolidation, and, data and application integration.

Servers were moved from different geographic data

centers to a common location (centralization),

several small servers were replaced with fewer large

servers to achieve storage consolidation (physical

consolidation) and data was consolidated into a

single server, and similar applications were merged

into a single server (data and application

integration).

Gupta et al. (2008) discussed the problem of

server sprawl. The authors modelled the problem of

server consolidation as a variant of the bin packing

problem, where the items to be packed were the

servers being consolidated and bins were the target

servers. The authors developed a new heuristic

algorithm for determining the number of destination

servers in the presence of the incompatibility

constraints including bin-item incompatibilities.

The problem of data-centers consolidation was

formulated as a minimization problem (Anselmi,

Cremonesi and Amaldi, 2009). The weighted sum of

server costs comprised of cost of energy

consumption, and maintenance, subject to satisfying

performance constraints on utilizations and data-

center response times was studied. Server costs were

minimized by reducing the number of servers used

in the data-center, which was achieved through

installing a given software application on a number

of servers, while maintaining a maximum utilization

thresholds.

Uddin and Abdul Rahman (2010) presented a

server consolidation solution to reduce the energy

consumption from underutilized servers and reach

energy efficient data centers using virtualization.

They defined virtualization as a technology that

combines multiple virtual servers on a single server,

and thus, increased the utilization of server. The

utilization of a server was estimated by measuring

its performed workloads and executed applications.

Dhyani et al. (2010) presented a constraint

programming approach for the service consolidation

problem in data-centers. The problem was to find an

allocation of applications to servers while

minimizing the data-center costs and satisfying

constraints on the resource utilizations. The authors

developed a constraint programming approach using

the Comet programming language to assess the

impact of the rule-based constraints in reducing the

problem search space and to improve the solution

quality and scalability.

Cardosa et al. (2009) presented a suite of

techniques for placement and power consolidation of

virtual machines in data centers taking advantage of

the min-max and shared features inherent in

virtualization technologies. The objective was to

place virtual machines among a set of physical

servers in the data center and estimated the shares

that should be given to each VM.

The behavior of server consolidation workloads was

studied by Jerger, Vantrease and Lipasti (2007),

which focused particularly on sharing of caches

across a variety of configurations. The authors

presented a study of a variety of last level cache

sharing arrangements to illuminate some of the

pressures felt by the cache hierarchy, and showed

that both performance and fairness were affected.

They presented a simulation methodology which

was designed to mimic a dynamically partitioned

system running a hypervisor or virtual machine.

Our work focuses on redesign of an existing EIN

through the removal of underutilized servers and the

distribution of their clients' to the remaining servers.

3 PROBLEM FORMULATION

The server consolidation problem is formulated as

ENTERPRISE NETWORK REDESIGN THROUGH SERVER CONSOLIDATION

187

an optimization problem, where the objective

function is to maximize the servers’ utilization as

shown in Equation (1). The term represents the

utilization for server j, and S is the total number of

servers in the system

Utilization of Servers = max

∑

(1)

Here, we highlight the core constraints, which

facilitate the uniform distribution of clients to the

existing servers. Constraint (2) states that each

cluster of clients is attached to only one server,

thereby ensuring that each client is served by one

server.

∑

jij

βα

Cj ,...,2,1=∀

(2)

In constraint (2), represents the binding of ith

cluster to jth server. represents the allocation of

server j in the EIN.

Constraint (3) ensures that an installed server

serves more than one cluster. Moreover, it ensures

that the number of connected clusters is less than the

total number of clusters in the system so that the

server will not be over-utilized. The term C

represents the total number of clusters within EIN.

iij

∑

βα

Si ...,3,,2,1=

∀

(3)

4 REDESIGN THROUGH

SERVER CONSOLIDATION

4.1 Server Utilization

The server utilization is calculated by taking the

average CPU usage over a period of time (during

one hour) (Abdulgafer et al., 2010), which includes

the number of file requests received by each server

and time taken by each server to process the file

requests. The performance of each server is

estimated based on its utilization and higher server

utilization leads to higher server performance.

We have defined define the server utilization (U)

as the amount of time the server is busy during a

period of time, (for example; duration of one hour)

as in Equation (4).

U = (amount of time the server is busy during

one hour / 3600 seconds) * 100

(4)

With known server capacity, which is the

maximum number of files the server can handle

during one hour, the amount of time (T) the server is

busy during one hour can be calculated using

Equation (5). The average file size is measured in

Megabytes and server capacity is measured in

Megabytes/hour.

T = (average file size * average number of

requested files per one hour) / server capacity)

(5)

4.2 Load Redistribution Approaches

We have proposed three approaches on distributing

the clients of the removed servers; single server pure

random distribution, selective distribution into the

second lowest utilized server and multiple servers

pure random distribution as shown in Figure 1. In all

the three methods, the utilization of each server is

computed and the server with lowest utilization is

selected for removal from the EIN. The single server

pure random distribution method selects a server

randomly from the remaining servers list and it

distributes the clients of the removed server. The

second approach allocates the clients of the removed

server to the second lowest utilized server in the

EIN. The third approach distributes the clients of the

removed server to two servers selected randomly

from the remaining servers in the EIN. Since the

first approach distributes the workload to only one

server, the probability of the randomly selected

server to become over utilized may occur more

frequently than the other methods. But the

overloading of server is decreased in the second

approach as it adds the clients to the second lowest

utilized server. However, the third approach

balances the workload by distributing the clients

over two servers, which result in overall

improvement in the servers’ utilizations.

5 ROLE OF SIMULATED

ANNEALING IN EIN

REDESIGN

Simulated Annealing (SA) is used widely to solve

different optimization problems. SA starts with an

initial solution S

, and then, it generates a new

solution using a function called neighbor in the

optimization process (Kirkpatrick et al., 1983). The

neighbor function is defined with a function known

as the Metropolis, which accepts a new solution

based on the dominance of cost of new solution or

based on a probabilistic function. We have

employed SA in our consolidation algorithm to

ICORES 2012 - 1st International Conference on Operations Research and Enterprise Systems

188

Figure 1: Load redistribution techniques utilized by neighborhood functions within SA.

optimize the utilizations of servers in the given EIS.

If the cost of the computed solution is positive,

then, the new solution gives a higher average

utilization and it is accepted. Otherwise, if the

random number in the Metropolis satisfies the given

condition, then the new solution is accepted.

Metropolis consolidation function is considered

as the solution modification function, which

generates new solution and passes it to a function

called neighbor in all the iterations. The neighbor

function tests the new solution and computes the

average utilizations of all servers for the new

solution without removing a server from EIN.

Metropolis algorithm accepts the new solution if the

cost of the new solution is greater than the previous

solution.

ENTERPRISE NETWORK REDESIGN THROUGH SERVER CONSOLIDATION

189

6 RESULTS AND DISCUSSION

We have experimented with an enterprise

information network using all the three clients’

distribution approaches. EIN comprises of 10 servers

and 25 clusters. Figure 2 shows the distribution of

servers’ utilization within the initial EIN. The

average server utilization of the initial network is of

25.8%. The Simulated Annealing parameters are set

to the following values; initial temperature = 1000˚

C, α = 0.8, β = 1, and maximum-time = 100 time

units. We have coded all the three experiments

within Simulated Annealing in C++.

Figure 2: Server utilization in initial EIN network.

According to the first approach, the average

utilization of the remaining servers is computed for

an optimization period as in Figure 3, and it is equal

to 68.25%. In each of the iteration, the algorithm

removes one underutilized server and distributes

their clients' randomly to the remaining servers that

exist in the system.

Figure 3: Utilization of remaining servers in first

approach.

The second approach increases the server

utilization by 34.55% from 25.8 to 60.35% as shown

in Figure 4. In each of the iteration, the algorithm

removes one underutilized server and distributes

their clients' clusters to the lowest utilized server

selected from the remaining servers. For the similar

EIN, the third approach ends with a final redesign

solution consisting of 3 servers and with the average

utilizations of 91% as shown in Figure 5.

Figure 4: Utilization of remaining servers in second

approach.

Figure 5: Utilization of remaining servers in third

approach.

The first and second approaches reduce the servers

from 10 servers to 4 servers where it represents a

60% reduction after consolidation, whereas the third

approach reduced the servers to 3, where it

represents a 70% consolidation.

7 CONCLUSIONS

In this paper, we have proposed three server

consolidation approaches namely, single server pure

random distribution, selective distribution and

multiple servers pure random distribution to

distribute the clients of a low-utilized server in an

existing enterprise information network (EIN). The

three methods are tested on EIN with 10 servers and

25 clusters, and the utilization of the EIN improves

by 42.45%, 34.55% and 65.2% respectively. The

experimental results show that the redesign

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algorithm reduces total number of servers from 10

servers to 4 servers, by eliminating most of the

under-utilized servers, and thereby reduces the EIN

operational and maintenance costs with acceptable

performance.

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