Int. J. Communications, Network and System Sciences, 2010, 3, 645-654
doi:10.4236/ijcns.2010.38087 Published Online August 2010 (http://www. SciRP.org/journal/ijcns)
A Load Balancing Policy for Distributed Web Service
Safiriyu Eludiora1, Olatunde Abiona2, Ganiyu Aderounmu1, Ayodeji Oluwatope1,
Clement Onime3, Lawrence Kehinde4
1Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
2Department of Computer Information Systems, Indiana University Northwest, Garry, USA
3Information and Communication Technology Section, Abdus Salam International Centre for Theoretical Physics,
Trieste, Italy
4Department of Engineering Technologies, Texas Southern University, Houston, USA
Received May 11, 2010; revised June 22, 2010; accepted July 28, 2010
Abstract
The proliferation of web services; and users appeal for high scalability, availability and reliability of web
servers to provide rapid response and high throughput for the Clients’ requests occurring at anytime. Distrib-
uted Web Servers (DWSs) provide an effective solution for improving the quality of web services. This pa-
per addresses un-regulated jobs/tasks migration among the servers. Considering distributed web services
with several servers running, a lot of bandwidth is wasted due to unnecessary job migration. Having consid-
ered bandwidth optimization, it is important to develop a policy that will address the bandwidth consumption
while loads/tasks are being transferred among the servers. The goal of this work is to regulate this movement
to minimize bandwidth consumption. From literatures, little or no attention was given to this problem, mak-
ing it difficult to implement some of these policies/schemes in bandwidth scarce environment. Our policy
“Cooperative Adaptive Symmetrical Initiated Dynamic/Diffusion (CASID)” was developed using Java De-
velopment Environment (JADE) a middle ware service oriented environment which is agent-based. The
software was used to simulate events (jobs distribution) on the servers. With no job transfer allowed when all
servers are busy, any over loaded server process jobs internally to completion. We achieved this by having
two different agents; static cognitive agents and dynamic cognitive agents. The results were compared with
the existing schemes. CASID policy outperforms PLB scheme in terms of response time and system
throughput.
Keywords: Web Service, Load Balancing, Distributed Web Service, Job Migration, Quality of Web Service
1. Introduction
Recently, Web Services are widely used daily by more
than six hundred million users over the Internet. The
proliferation of web services; and users appeal for high
scalability, availability and reliability of web servers to
provide rapid response and high throughput for the Cli-
ents’ requests occurring at anytime. Distributed Web Ser-
vers (DWSs) provide an effective solution for improving
the quality of web services. A collection of web servers
is used as a pool of replicated resources to provide con-
current services to the clients. The incoming requests can
be distributed to servers according to specific load dis-
tribution strategies and thus the requests are processed
quickly.
The DWSs can be organized in different ways, they
can be integrated into a cluster of web servers linked via
Local Area Networks (LANs) to act as a powerful web
server, and they can also be deployed at different sites
over open network (Internet).
The performance of a distributed system is enhanced
by distributing the workload among the servers. Norm-
ally, load balancing at the server side assists to balance
the load in distributed web servers. According to [1] the
most excellent mechanism for achieving optimal respo-
nse time is to distribute the workload equally among the
servers. Traditional load balancing approaches on distri-
buted web servers are implemented based on message
passing paradigm. Presently, mobile agents’ technology
is used to implement load balancing on distributed web

646 S. ELUDIORA ET AL.
servers [2].
A mobile agent is defined as a software component
that can move freely from one host to another on a net-
work, transport its state and code from home host to other
host and execute various operations on the site [2]. The
mobile agent based approaches have high flexibility, low
network traffic and high asynchrony. Load balancing (LB)
is an efficient strategy to improve throughput or speed
execution of the set of jobs while maintaining high proc-
essor utilization.
Basically, LB is the allocation of the workload among
a set of cooperating servers. The demand for high perfor-
mance computing continues to increase every day. LB stra-
tegies fall broadly into either one of two classes; static or
dynamic.
Static load distribution, also known as deterministic
scheduling, assigns a given job to a fixed server or node.
Every time the system is restarted, the same binding task
processor (allocation of a task to the same processor) is
used without considering changes that may occur during
the system’s lifetime.
Moreover, static load distribution may also character-
ize the strategy used at runtime; in the sense that it may
not result in the same task-processor assignment, but as-
signs the newly arrived jobs in a sequential or fixed fash-
ion. For example, using a simple static strategy; jobs can
be assigned to servers in a round-robin manner, so that
each processor executes approximately the same number
of tasks. However, dynamic load-balancing takes into ac-
count that the system parameters may not be known be-
forehand and using a fixed or static scheme will finally
produce poor results. A dynamic strategy is executed se-
veral times and may reassign a previously scheduled job
to a new server based on the current dynamics of the sy-
stem environment. The paper is organized as follows,
Section 1 introduced the readers to the issue web ser-
vices’ challenges in a distributed systems. Section 2
briefly discusses the intelligent agents and its potentials.
The related works was discussed in Section 3. The pro-
posed work was detailed in Section 4. The software
model was explained in Section 5. Theoretical frame-
work was discussed in Section 6. Section 7 narrates ex-
perimental results and Section 8 draws the conclusion
and proposed future work.
2. Intelligent Agents
Intelligent agents exhibit the following characteristics: au-
tonomy, social ability, reactivity, pro-activeness, mobil-
ity, learning, and beliefs. An intelligent agent is an inde-
pendent entity capable of performing complex actions
and resolving management problems on its own. Unlike
code mobility, an intelligent agent does not need to be
given task instructions to function, rather just the high-
level objectives. The use of intelligent agents completely
negate the need for dedicated manager entities, as intel-
ligent agents can perform the network management tasks
in a distributed and coordinated fashion, via inter agent
communications. Many researchers believe intelligent
agents are the future of network management, since there
are quite some significant advantages in using intelligent
agents for network management.
3. Related Works
An attempt to distribute workload among available proc-
essors to improve throughput or execution times of para-
llel computers in cluster computing environments was
discussed in [3]. This work proposed an approach called
DDLB (Distributed Dynamic Load Balancing) policy,
this makes an attempt to balance load by splitting proc-
esses into separate jobs and then balance them to nodes.
Mobile agent framework was proposed for DDLB policy
and was implemented on PMADE. The performance ev-
aluation shows that the multi-agent based approach out-
performs the message-passing paradigm with large num-
ber of clients’ requests in a heterogeneous cluster.
DDLB policy can only address issues on local LB th-
ere was little provision for global load balancing. There-
fore, may not be able to address a very large heteroge-
neous distributed environment. They need to compare
the performance of the DDLB with some other recent LB
policies apart from MPI. Since this is a mobile agent ba-
sed approach, it could be compared with some other mo-
bile agent based approaches. Various LB policies were
reviewed by [4], specifically MPI (message-passing in-
terface). Its wide availability and portability makes it an
attractive choice. However, the communication require-
ments are sometimes inefficient when implementing the
primitives provided by MPI.
Other works presented a dynamic LB framework cal-
led Platform for LB (PLB). It uses MAs to implement
reliable and scalable LB on distributed web servers. PLB
is implemented on PMADE (A Platform for Mobile
Agent Distributed and Execution). The performance
evaluation shows that PLB based approach outperforms
MPI when large number of servers and client requests
are involved.
The PLB SSP adopted two strategies: Best-fit and First-
fit. The best-fit will create two different speeds; the fa-
ster and the slower, this will affect the response time of
the servers. The first-fit equally has some latency over a
period of time. Overall, the two strategies adopted may
not perform well when compared to other policies. This
policy may not be efficient when compared with our
work; we approach the load balancing by considering the
heterogeneity of all nodes. We use broadcast approach to
determine the node(s) that are available and ready for
incoming jobs.
Load balancing on WAN is more time consuming [5],

S. ELUDIORA ET AL.
647
since it involves the iteration between remote servers for
gathering load information, negotiating on load reloca-
tion and transporting the work-load. Also, moving a lar-
ge amount of request requires high bandwidth and it is
difficult if a node’s load changes quickly in relation to
the time needed to move requests. The authors proposed
an approach that will address these problems. LDMA
(Load Balancing using Decentralized decision-making
Mobile Agents) strategy introduced the concept of de-
centralized decision making among the web servers in
the cluster for processing requests.
Also WLDMA (WANLDMA) was introduced, this
allow each server to process client requests independent-
ly and periodically interacts with others to share the work-
load.
The performance of load distribution on the three ser-
vers using WLDMA is two times better than the scheme
without load balancing. The result of this work shows
that the WLDMA scheme can improve the system thr-
oughput when increasing the number of servers.
The use of mobile agents implies high flexibility, low
network traffic and high asynchrony. Also, the result
shows that no replica remains idle at any time when the
other replicas are processing more request of each nodes.
It is clear from this work that waiting time of the re-
quest will go up leading to increase in response time.
This policy use SI (sender initiated) which can lead to
poor performance of the network, because of varying
value of state load information of other servers if applied
to larger distributed and heterogeneous environments.
MALD (Mobile Agent based Load Balancing) frame-
work that uses mobile agents technology was proposed
to implement scalable load balancing on distributed web
servers. To achieve this, two load balancing schemes were
presented. Cluster of web servers and wide area network
(www) web servers. The two schemes used MALD and
tested in a well distributed environment. The load bal-
ancing scheme on www uses shared LIA (load informa-
tion agent) for load information gathering [2].
The MALD framework performs well compared to
message-passing paradigm and no load balancing. The
system throughput, network traffic is better in LAN; wh-
ile in WANs, the LIAs are restricted to domains. Series
of simulations were done and the results are better than
message passing paradigm. The drawback of this work is
that, the waiting time of each task in the queue and
within the entire system may be more, because LIA will
continue to move from one server to the other until the
best server is found. In this approach, the domain name
system mentioned did not consider the minimum number
of servers that must be in a domain, in essence the policy
may not perform well in a system with large number of
servers. The scalability and good response time may not
be achieved. Our study address this problem by identify
the server that is overloaded that needed to share with
other server that is under-loaded or idle in the cluster.
This is achieved by proposing two agents, mobile and
stationary agents. The stationary agents periodically de-
termine the status of each server.
The work in [6] addresses the escalating complexity
and mobility of today’s network. This has led to the in-
crease in application of mobile agent paradigm; however
load balancing (LB) is one of the important problems of
computer heterogeneous network.
In that paper the authors introduced a decentralized
algorithm for diffusion dynamic LB based on mobile
agent paradigm. The architecture of three types of agents
was employed to meet the requirements of the proposed
diffusion LB algorithm. Packet format was suggested for
each agent as a data communication packet. Two perfor-
mance metrics were used in the proposed approach, they
are: the average response time of the clusters and the
variance of the load over the network. The algorithm was
tested with the situation where there is NoLB. In addition
it minimizes the bandwidth consumption and disruptive
nature of wireless communication. This algorithm offers
an alternative to client server based solution with appre-
ciable bandwidth; it implies that it would be more ap-
propriate in a wireless data communication application.
The clusters size was not mentioned, nodes threshold
was not stipulated and therefore there is no load inhibi-
tion, so in large networks it may lead to instability of the
system SI (sender-initiated) was used in the research
work. In our proposed algorithm we proposed threshold
for the RAM and CPU. We set the two at 90% and 80%
respectively.
Two of the existing static LB policies based on M/M/1
queues were reviewed [7]. This was done to address poor
system performance due to loads imbalance. The authors
proposed two new dynamic LB policies for multi-user
jobs in heterogeneous distributed systems. The two dy-
namic policies proposed are: DGOS and DNCOOPC, the
two static policies reviewed are GOS and NCOOPC. The
result of the simulations shows that static has low com-
munication overheads; the dynamic schemes show supe-
rior performance over the static policies. But as the
overheads increases, the dynamic policy is expected to
perform similar to the static policies.
According to [8], challenge faced by these cluster-
based applications is how to maintain desired real-time
performance and service availability in the face of highly
unpredictable work-loads in an open environment such
as internet. The authors investigate the problem of multi-
processor utilization control in large scale real-time clu-
sters.
The work proposed an effective approach that will
dynamically enforce desired utilization bounds on all the
processors in a cluster through online admission control.
The authors present DUC-LB, a novel Distributed Utili-
zation Control algorithm designed for large scale real-
time clusters with LB. DUC-LB can provide robust util-
ization guarantees to all processors in a real-time cluster

648 S. ELUDIORA ET AL.
through feedback-based admission control. The result sh-
ows that DUC-LB is rigorously designed based on recent
advance in distributed control theory, stability analysis
and simulation results demonstrate that DUC-LB can
dynamically enforce desired utilization set points under a
range of dynamic workloads. FCU-LB was used as a
baseline for performance comparison. In terms of utiliza-
tion, DUC-LB performs better. In this study, the major
drawbacks are:
• the topology
• the rejection of tasks
• communication cost
Challenges introduced by fine-grain services [9] for
server workloads can fluctuate rapidly for those network
services. Fine-grain services are sensitive to various
overhead which is hard to accurately capture in simula-
tions.
In recognizing this limitation the authors developed a
prototype that its implementation is based on a clustering
infrastructure and conducted evaluations on a Linux
cluster. The study and evaluations identify techniques
that are effective for fine-grain services and lead to the
following conclusions:
• Random polling based load-balancing policies are
well suited for fine-grain network services;
• A small poll-size provides sufficient information
for load balancing, while an exclusively large poll size
may even degrade the performance due to polling over-
head;
• An optimization of discarding slow responding polls
can further improve the performance up to 8.3%.
The drawback of this policy is stale load information
which is sensitive to the fine-grain network services. In
the literature, these are done locally, because it is delay
sensitive. In their work, authors use poll size that must
not be more than two. It implies that it can not support
broadcast policy and partly support random policy.
Finally, this policy cannot support a largely distributed
decentralized network environments.
This research work [10] focus on networks connecting
the different nodes typically exhibit significant latency in
the exchange of information and the actual transfer of
loads. The load balancing policy rely on latest informa-
tion about the system of nodes, which in turn may result
in unnecessary transfer of loads while certain nodes re-
main idle as the loads are in transit. The strategy intro-
duced by the authors put into account the following three
aspects:
• the connectivity among the nodes;
• the computational power of each node and;
• the throughput and bandwidth of the system, the pol-
icy was described by defining some parameters.
Values of (Ci term) are critical to the stability and effi-
ciency of the load balancing policy. The results show
that this policy is more scalable. The method used here
leads to optimal results, when the Ci/Cj has no delay fac-
tor. Transfer delays incurred when tasks are migrated
from node to another may be unexpectedly large and
result in a negative impact on the overall system per-
formance. The traffic may be a little higher in a large
heterogeneous distributed environment. The performance
may not give optimal result. We take care of this in our
work by introducing Job Migration regulatory mecha-
nism.
In [11], the authors explained that modeling the dy-
namic load balancing behavior of a given set of tasks is
difficult. They define an approximation problem using
“bi-modal” task distribution, which serves as an accurate
abstraction of the original task set. This approximation
consists of only two task types and can therefore be
tackled analytically. This work model the run time of the
slowest processor as this will determine the overall run
time of the parallel applications. All comparably un-
der-loaded nodes will be probed before a suitable node
will be located. The results of the experiments using
synthetic micro-benchmarks and a parallel mesh genera-
tion application, demonstrate that this technique when
used in-conjunction with the PREMA runtime toolkit can
offer users significant performance improvements over
several well known load balancing tools used in practice
today. Little overhead is incurred using PREMA, when
compared with others. The drawback of this work is the
adaptive issue that was not taking care of. In essence, the
decision of each node will be relatively poor, therefore
the response time of each node will be prolonged and it
will affect overall performance. Global load balancing
cannot be easily achieved, because stale state informa-
tion will be available to several nodes.
This work [12], overviewed the homogeneous and
heterogeneous clusters. A general station model from a
heterogeneous cluster was proposed.
The model used considered a station as characterized
by the following elements:
• Unique ID
• Computing power
• Availability
• Task lists
Three network topologies were considered: mesh, trees
and hyper-cube. The three configurations prove to be the
least efficient. The hypercube seems to have best per-
formance. The mesh topology is at the middle in terms of
performance. Authors proposed a more intelligent rout-
ing technique as a future work. Point of failure could be
experienced in tree topology. The success of this policy
rely much on network connections and available band-
width to really address the load transfer from neighbors
to neighbors until it reaches a powerful node among
them. It then implies that, the scheme is not conservative
at all and it is not scalable in the overall performance.
Our proposed work addresses this drawback by intro-
ducing cluster-based system that will allow server to join

S. ELUDIORA ET AL.
649
and exist at will. The cluster balances the loads by re-
ducing the jobs intake through job migration regulation.
This allows good quality of service (QoS) in the network.
CASID policy is discussed in the next section.
4. CASID Policy
The proposed policy tagged Cooperative Adaptive Sym-
metrical Initiated Diffusion (CASID) consists of sender
and receiver communicating through a communication
networks links. The sender and receiver refer to as serv-
ers. They are web servers and database servers as shown
in Figure 1. The load threshold of each server describes
whether it is sender (overloaded) or receiver (under
loaded). The main goal of this policy is to minimize re-
sponse time and increase system throughput. CASID
implements dynamic policy, where the server can be-
come a sender or a receiver at anytime. In essence, the
policy gives room for the server to interact and there is
inter-communication among the servers. Also, CASID
has adaptive properties whereby server can accept or tra-
nsfer load to/from other servers. Symmetric-initiated dif-
fusion affirmed the relationship among the servers. Dif-
fusion can be used to describe dynamism of distributed
networks system (DNS). The main problem of any load
balancing policy is how to get the right server to estab-
lish the job exchange in the distributed networks system.
The heterogeneous nature of dynamic policy explains
whether a better performance will be achieved. CASID
policy proposed a model that attempt to minimize the
response time of the entire DNS to the user. The propo-
sed policy will increase system throughput when com-
pared with the existing policies. In this case, CASID po-
licy considered the global load balancing within the DNS
a priority.
4.1. Job Migration Regulatory Mechanism
Job migration determines the success of any load balan-
cing policy, especially in the area of minimizing band-
width consumption. Most of the previous works did not
lay much emphasis on this bandwidth minimization as a
key issue in any distributed networks environment. It is
Internet Router
user
Incoming
HTTP
Firewall
Web servers Data
b
ase servers
Figure 1. Proposed CASID policy architecture.
well known fact that bandwidth consumption must be
minimized. There must be a control measure on job mi-
gration whenever LB is being deployed. As a result, the
proposed policy will deal with this issue with more em-
phasis on the server status information whenever any
initiation wants to be made by servers. In this study,
server status information is gathered through the station-
ary agents. The stationary agents periodically check the
status of the I/O networks, memory and CPU. This is
done to have closed to real-time status information of
every server. The mobile agents do the job transfer
whenever there is need for such transfer. If this is done it
will enhance better performance of this LB policy.
The essence of this regulatory mechanism is to achieve
the following:
• reduce bandwidth consumption
• achieve local and global load balancing
• decrease response time of the users
• reduce waiting time of the users
• reduce congestion in the system
However, the proposed policy will address job migra-
tion mechanism. The server determines its load threshold
by calculating the load on the queue and in service. The
server status information will be broadcasted to other
servers. Servers that meet such requirements will signify.
The sending and receiving servers will establish commu-
nication and job migration can commence as illustrated
in Figure 2. In a situation where there is no under-loaded
server that meets up with the conditions set by the sender,
such task will be processed by the sending server. Only
profitable job migration will be allowed in this policy.
This is further discussed in Section 5 of this paper.
5. System Model
This software model has some modules and each module
Figure 2. Job migration using dynamic (mobile) cognitive
agents.

S. ELUDIORA ET AL.
650
describes the operation of ServerAgents. Simple model-
ing language was used as illustrated in Figure 3 to mo-
del the behavior of the agents on the server. This model
will translate to the software that will be developed using
JADE. Some of these modules are listed and discussed
below.
MigrateJobCompleted()
This module is used to load migrated and completed
jobs unto the ServerAgent (migrater) queue.
jobLoader()
This module loads new jobs unto the ServerAgent’s queue.
jobLoaderFinishedMigrate(jobSerialisable jbL)
IDLEServerBehaviour() This module is used to get some statistics on migrate
job like completion time, waiting time, migrate time, thr-
oughput, transfer rate of job between ServerAgents etc.
This module is used by the ServerAgent whenever it
enters the IDLE state. This module is actually a sub-class
of the ServerAgent class and it is used to interact with
BUSY ServerAgents. As a class, this module has its own
modules which are action and done. The action module
is executed each time the ServerAgent is called on.
While done is executed when the ServerAgent’s state is
to be changed to IDLE.
ServerIsIDLE()
This module returns the state of the ServerAgent (i.e.
IDLE or BUSY).
5.1. Mobile Agent Modules
BUSYServerBehaviour()
ServerInteractionBeha viour()
This module is used by the ServerAgent whenever it
enters the BUSY state. This module is actually a sub-cla-
ss of the ServerAgent class and it is used to interact with
IDLE ServerAgents. As a class, this module has its own
modules which are action and done. The action module
is executed each time the ServerAgent is called on.
While done module is called when the ServerAgent’s
state is to be changed to IDLE.
This module is used by the mobileAgent to interact
with the IDLE ServerAgent during the mobileAgent’s
sojourn at the IDLE ServerAgent’s domain. It is also
used in interacting with the BUSY serverAgent.
registerDFServices()
This module is called when the ServerAgent is acti-
vated or instantiated. The module registers this agent
with the Directory Facilitator (DF) which records the
name, address, location of the agent, its host and the ser-
vices the agent renders. This registration makes interac-
tion with other agents possible and seamless.
registerDFServices()
This module is called when the ServerAgent is activ-
ated or instantiated. The module registers this agent with
the Directory Facilitator (DF) which records the name,
address, location of the agent, its host and the services
the agent renders. This registration makes interaction
with other agents possible and seamless.
sendJobToServer(AID ServerAgent, String conID)
This module is used by the mobileAgent to transfer
jobs to ServerAgents either when the job is finished.
Figure 3. Class diagram of the system model.

S. ELUDIORA ET AL.
651
6. Theoretical Framework
Having discussed related issues concerning this paper,
we need to address the framework in which this paper
was based on. We want to compute the network traffic,
system throughput, mean response time and system
utilization of CASID and compare with PLB.
We consider N exponential servers each having a dif-
ferent service rate µi with i € (1, N). Each server has its
own local queue and the number of jobs in each queue is
represented by qi with Poisson distribution stream of
strength λ (lamda). However, communication cost is
considered to be significant, the arrival stream at each
server is λ + δi, where λ is a constant rate at which tasks
are initiated at any node (user) and δi is the rate of trans-
fer of jobs to server i.
The expected delay job i (Di) will experience if sent to
the j-th server is
(1) /
j
j
Dq
j

 (1)
where µi service rate and qi no of jobs in the queue.
The selected (destination) sever will normally be the
one for which Dj is minimal. We have another equation
when communication delay is added to Equation (1).
Thus
(, )
(1) /
j
jjcomi
Dq t 

 

j
(2)
where µi service rate and qi no of jobs in the queue. tcom(i.j)
represent communication delay experienced by jobs
when it is being transferred from server i to server j.
If the job is processed locally then, tcom(i.i) = 0 in addi-
tion, the expected execution time is considered in Equa-
tion (3).
That is,


(, )(1 /) execcom ijj
Et t

 (3)
where (1/µj) is the service rate.
The throughput of was determined using Equation (4)

1
() /n
jj
j
Tj 



 
(4)
where n
j is the total number of jobs in the j-th queue in-
cluding any job in service. For each server j, the ratio µj /
(λ + µj) is less than 1. Thus the greater n
j is the smaller
T(j) will be and the less “recommendable” (exponentially)
the server j becomes to be selected as a destination
server.
11
j
jj
nsq
j
t (5)
where qj is the number of jobs in queue and ti is the
number of jobs being transferred to server j, that are ex-
pected to arrive before the next job initiation in the sys-
tem. tj is simply the exact number of jobs being trans-
ferred to server j at the instant that the decision is being
made.
As for sj, it is equal to zero if server j is idle. If server j
is busy sj is equal to 1, if the job currently in service at
server j is not expected to be completed before the next
job initiation i.e if (1/µj) – time(already spent in service)
≥ 1/λ (arrival rate). Otherwise, sj is equal to zero [13].
The load size on each server was determined as
12
3
_
_/
_
Loadsizev CPUloadvЌЂ
vfreemem



 (6)
where CPU_load is the workload on the server, measured
in the length of jobs in queue; Ќ is the number of active
connections on the server; Ђ is the maximum number of
connections allowed to the server; free_mem is the per-
centage of free memory space;
v1 + v2 + v3 = 1
System utilization was determined from Equation (7)
1
busy idle



 (7)
the parameter αbusy is the time averaged of busy servers in
the system. A busy server is defined as a server having
one job in service and zero or more jobs in queue. Sys-
tem utilization involves the percentages of CPU, Mem-
ory and I/O being used at a point in time [14].
The network traffic was determined using Equation (8)
[]
ij
R


(8)
where γij is the average transmission rate from source
(server i) to destination (server j). In some cases, we def-
ine the requirement matrix as R = ρR’, where R’ is a kn-
own basic traffic pattern and ρ is a variable scaling factor
usually referred to as traffic level [15]. In general, R or R’
cannot be estimated accurately a priori, because of its
dependence upon network parameters (e.g. allocation of
resources to computers, demand for resources etc which
are difficult to forecast and are subject to changes with
time and with network growth.
7. Experimental Results
To analyze the proposed load-balancing algorithm, it was
implemented on a cluster-based web server. For evalua-
tion of CASID algorithm, throughput and average re-
sponse time were selected as criteria [16,17]. To give a
better evaluation, a network traffic analysis of the algo-
rithm was also provided.
We implement two of the commonly used load-balan-
cing algorithms PLB and NO-LB, we compare the perf-
ormance of the proposed algorithm with them. The PLB
is a commonly used agent-based algorithm that uses the
average CPU threshold to determine whether a server is
busy/idle. PLB uses the same update intervals as used in
the CASID to collect CPU and memory loads from web
server nodes.
The following sections describe the implementation

652 S. ELUDIORA ET AL.
setups which are used in our experiments and finally the
results are analyzed.
7.1. Implementation Setup
We implement the experimental test bed with the hard-
ware and software configurations as described below.
7.1.1. Hardware Setup
The clustered web server consists of four (4) machines
configured as follows. Each web server is connected to a
dedicated database server node, so four (4) machines are
used as database servers. Also, variable numbers of ma-
chines were used as client emulators to simulate real us-
ers’ interactions with the web site. The web server nodes
and corresponding database server nodes are having Pen-
tium 4 processors (ranging from 1 GHZ to 2 GHZ) with
minimum of 512 MB of DDR RAM.
7.1.2. Software Setup
All the machines in the cluster run windows xp 2007 op-
erating system; we developed our software using JADE
as discussed in the previous section to perform the exp-
erimental test. We implement a client-browser emulator
to generate load against the cluster. We vary the loads on
the cluster by varying the number of concurrent clients.
We use to determine which machine become overload
and ready to perform job migration. The overloaded ser-
ver broadcast its status to other servers in the cluster. Si-
nce the developed software are installed and configured
on each server. The server that is under-loaded or idle
will receive the message and acknowledge the message
and the jobs are transferred. Matlab [18] was used to plot
the graphs and the data were generated from the results
from the simulations
7.2. Implementation Results
In the following sections, we demonstrated the results of
our experiments with the CASID benchmark (non-stan-
dard). We change load intensity, machines and measure
the cluster throughput, response time and network traffic
for the two load-balancing algorithms PLB and CASID).
7.2.1. Network Traffic Evaluation
Figure 4 compares CASID policy network traffic with
PLB scheme. CASID policy performs better than the
PLB scheme; it reduces congestion in the network, beca-
use it introduces job migration regulatory mechanism
where loads cannot migrate to other server(s) without
acknowledgement from other servers. If no acceptance
from other servers, such server will process the tasks till
completion.
We consider situations where all the servers are idle
and busy. The CASID scheme shows that when all the
servers are busy the network traffic was low or insignifi-
cant. As the number of busy and idle servers increase in
PLB scheme the traffic increase accordingly, because of
non-implementation of job migration regulatory mechan-
ism as in CASID policy. CASID policy allows an overl-
oaded server to broadcast its status before it can send
jobs to other servers. The receiving server will acknowle-
dge before the jobs is being transferred to the receiver
(server). PLB scheme will send to under-loaded server ir-
respective of volume of jobs at hand and processing cap-
ability of that server. We work with maximum of 2 Mbps
network within the average of 90 minutes.
7.2.2. Throughput Evaluation
System throughput was evaluated and Analyzed in Fig-
ure 5, we generate requests through requests emulator,
the number of requests/sec range from 0 to 100 and the
requests range from 0 to 1200. The two schemes were
subjected to the same environments and conditions. The
11.5 22.533.5 4
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Number of Servers
Network Traffic(Mbps/10min)
CASID
PLB
Figure 4. Network traffic (average 90 minutes).
0200 400 6008001000 1200
0
10
20
30
40
50
60
70
80
90
100
Number of Requests
Requests/sec
system throughput
CASID
PLB
Figure 5. System throughput.

S. ELUDIORA ET AL.
653
schemes performance was close. CASID still has better
performance, because the servers are able to manage
their resources by ignoring load transfer messages.
We used Figure 6 to explain the throughput of the sy-
stem without load balancing and compare it with our
CASID policy. We discover that the job done when serv-
ers are many only have little improvement compare with
when server is one. The mobile agents assisted our pol-
icy couple with our job migration scheme. The CASID
perform well than when the system was not load balanc-
ing at all.
7.2.3. Mean Response Time Evaluation
Response time was evaluated and analyzed in Figures
7-9, these figures analyze the high, medium and low het-
erogeneous of the servers used. The inter-arrival of the
11.5 22.5 33.5 4
10
20
30
40
50
60
70
80
Number of Servers
Requests/sec
without load balancing
CASID Approach
Figure 6. System throughputs of the CASID policy and the
case without load balancing.
0 0.05 0.1 0.15 0.2 0.25
0
5
10
15
int er-ar riv al(1/l am da)
CASID
PLB
NO-LB
Figure 7. Mean Response time of a highly heterogeneous
system.
requests to the system was processed by different server
with their processing capabilities. This determined the
mean response time of the servers at different time inter-
val. Inter-arrival of the requests was plotted against re-
sponse time. The CASID, PLB and NO-LB schemes
were compared. CASID and PLB have better perform-
ance. The CASID has improved performance over PLB,
because of its job regulatory mechanism scheme. Dif-
ferent machines were used as servers to confirm the ef-
fect of heterogeneity of the nodes on the mean response
time of the cluster.
8. Conclusions and Future Work
This paper addresses un-regulated jobs/tasks migration
among the servers. Considering distributed web services
00.02 0.04 0.06 0.080.1 0.12
0
5
10
15
inter-arrival rate (1/lamda)
server response time
CASID
PLB
NO-LB
Figure 8. Mean Response time of a system with medium
heterogeneity.
00.005 0.01 0.015 0.02 0.0250.03 0.0350.04
0
5
10
15
inter-arrival rate
(
1/lamda
)
server response time
CASID
PLB
NO-LB
Figure 9. Mean response time of a system with low hetero-
geneity.

S. ELUDIORA ET AL.
654
with several servers running, a lot of bandwidth is wa-
sted due to unnecessary job migration. It is important to
develop a policy that will address the bandwidth con-
sumption while loads/tasks are being transferred among
the servers. This work attempts to regulate this move-
ment to minimize bandwidth consumption. The results of
this first phase of the experiment as illustrated in the gra-
phs show that our policy performs better. The second
experiments that will be carried out in future will consi-
der the system utilization, response time and throughput
that will be experienced by the job while running the
software on LANs and open networks (Internet).
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