A Journal of Software Engineering and Applications, 2012, 5, 149-156
doi:10.4236/jsea.2012.512b029 Published Online December 2012 (http://www.scirp.org/journal/jsea)
Copyright © 2012 S ciRes. JSEA
Fuzzy Logic–Based Scheme for Load Balancing in Grid
Tarek Helmy*, Hamdi Al-Jamimi, Bashar Ahmed, Hamzah Loqman
Information and Computer Science Department, Col lege of Computer Scien ce and Engin eering, King Fahd Uni versit y of Pet roleum
and Minerals, Dhahran 31216, Mail Box, 413, Saudi Arabia; On leave from College of Engineering, Department of Computers
Engineering & Automatic Control, Tanta University, Egypt”.
Email: helmy@kfupm.edu.sa
Received 2012
Load balancing is essential for efficient utilization of resources and enhancing the responsiveness of a computational
grid, especially that hosts of services most fr eq uentl y us ed , i.e . food , health a nd nutritio n. Var io us te c hniques ha ve b ee n
developed and applied; each has its own limitations d ue to the dyna mic natur e of the grid. Efficient load balancing can
be achieved by an effective measure of the node’s/cluster’s utilization. In this paper, as a part of an NSTIP project #
10-INF1381-04 and in order to assess of FAQIH fra me work ability to support the load balance in a co mputational grid
that hosts of food, health and nutrition inquire services. We detail the design and implementation of a proposed
fuzzy-logic-based scheme for dyna mi c lo a d ba la nci ng in gr i d co mputi n g services. The proposed scheme works b y using
a fuzzy lo gic inference system which uses some metrics to capture the variability of loads and specifies the state of each
node per a cluster. Then, based on the overall nodes’ states, the state of the corresponding cluster will be d efined in o r-
der to assign the newly arrived inquires such that load balancing among different clusters and nodes is accomplished.
Many experiments are conducted to investigate the effectiveness of the proposed fuzzy-logi c-based scheme to support
the load balance where the results show that the proposed scheme achieves really satisfactory and consistently load
balance than of other randomize approaches in grid computing services.
Keywords: Fuzzy Logic; Load Balancing; Grid Co mputing.
1. Introduction
Grid computing can be considered as a type of parallel
and distributed systems that enables the selection,
distr ibutio n, and aggr egati on of r esour ces d ynamica lly a t
run time depending on their availability, capability, and
performance. It focuses on large-scale resource sharing
and distributed system integration for the sake of effective
utilization and high-performance orientation. Indeed, at
the service level of the grid, software infrastructure, the
wor klo ad and r e so ur c e mana ge me nt a re e ss e ntial p ro vide d
functions. In turn, distributed systems have become a
natural setting in various environments either for business
or academia [1,14,17-20]. In like these systems settings,
tasks arrive to the different no des of the cluster randomly.
This random fashion leads to a non-uniform distribution
of the workload among different nodes of the cluster.
Loading imbalance is harmful to the whole system
performance in terms of the mean response time of tasks
and resources utilization. Therefore, load balancing tries
to increase system throughput by making all processors
as busy as possible. The objective can be achieved by
two ways: either by avoid sending more tasks to the
overloaded nodes and keep checking the nodes’ states
periodically, or taking a migration decision to migrate
some tasks fr om the overloaded n odes t o the light ly loa ded
nodes for the sake of improving the s ystem per formance
as a whole. The load balancing dilemma in a distributed
computing environment has been addressed extensively
by many studies and researches. Based on that, many
algorithms have been introduced to tackle the load
balancing pro ble m. All kinds o f these algorith ms fall into
one of the follo wing two ca tegories: dynamic or static [ 2,
18]. Dynamic load balancing algorithms exploit the
information descr ibing the state o f the system in ord er to
improve th e accuracy o f load balanci ng decision. Therefore,
the dynamic algorithms are appropriate to be utilized in
practical situations. However, this kind of algorithms
should have the ability to collect the information about
*Corresponding author.
Fuzzy LogicBased Scheme for Load Balancing in Grid Services
Copyright © 2012 S ciRes. JSEA
the current state of the system, to store the collected
information and to analyze them. Thereby, the dynamic
algorithms may cause additional overhead computations
for the system. On the other hand, the static algorithms
don’t exploit such information. In static algorithms, the
decisions are taken based on a priori knowledge of the
system. Thus, the sta tic algorithms don’t have the abilit y
to deal with the dynamic changes of such environments.
An efficient load balancing scheme is needed for grid
computing environment. The effective scheme needs to
know the global system’s sta te such as the distrib ution of
the workload. Ho wever, the lo ad model of t he distrib uted
systems tends to change dynamically, and hence, it is
very complicated to model the system accurately. Thus,
precise and fast load balancing scheme is an essential
factor in increasing the efficiency and performance of
grid computing components. However, many factors lead
to ambiguous information about the cl us ter s/nodes states,
i.e. lack of shared memory among independent clusters.
This ambiguity leads to uncertainty in decision for load
balancing. Another important aspect is that the states of
the clusters/nodes can be changed rapidly. Therefore
there is always some amount of uncertainty in the state
information used for making a decision. Hence it is
necessary that the decision making process deals with
these uncertainties. Fuzzy logic is one of the methods
that deal with the uncertain information [4,5,13,15,16].
In this paper, in order to tackle the problem of load
balancing in the grid computing environment where
uncertainty is unavoidable, we utilize a Fuzzy Inference
System (FIS) -based approach to model those contributed
factors to determine the nodes’ states and thereby the
clusters’ states. In some previous studies, both length of
the ready queue and CPU utilization have been used to
provide a good load indicator. In the case of multiplicity
of resources, a good measure can be considered like a
linear combination of the length of all resource queues.
In this paper we use four parameters as input variables
for the sake of more accurate fuzz y-logic based approach.
We consider a continuous stream of independent jobs
which arrive to the system and are stored into a single
job queue. We assume that all jobs are not preemptable,
and each job is described by its ID, submission time,
deadline time, an estimation of its execution time and the
number of resources it requires. The computing grid is
composed of many independent clusters of homogenous
processing machi nes. Because of the lack o f a mechanism
in the computing platform to notify configuration changes
to the system, we assume to have a static number of
resource instances available to cluster’s processing node.
The rest of the paper is organized as follows: Section 2
describes technical background about fuzzy logic concepts.
Section 3 introduces the related work. The proposed
fuzzy-logic based scheme is demonstrated in Section 4.
The implementation details of the proposed scheme are
described in Section 5, while Section 6 introduces the
simulation environment and discusses the obtained
results. Section 7 concludes the paper and introduces
some trends for future work.
2. Technical Background
In this section, we introduce a brief ba ckground abo ut the
concept s used in this paper. Fuzzy logic methodolo gy can
be utilized in solving the problems tha t are complex to be
analyzed quantitatively or in the case of natural phenom-
ena that are not easy to be modeled mathematically [6].
IF-THEN rules are used in the FIS where the output is
concluded from the fired rules based on the given inputs
[3,4]. The system parameters are modeled as linguistic
variables based on expert knowledge; also the corre-
sponding membership functions are designed for each
parameter. Therefore, fuzzy logic theory can be employed
even for the nonlinear systems suffe ring from uncer tainty
and complexity. That complex uncertain system can be
modeled effectively based on fuzzy logic without need
for complicated mathematical models [5]. A general FIS
engine mainly involves the following components as
shown in Figure 1:
The fuzzifier receives the inputs which are the node’s
information in the proposed model case. The fuzzifier
maps t he given numerical inputs to fuzzy sets and lin-
guistic v ariables. Where th e def ined lin gui stic variables
can be matched with the fuzzy rules premises that are
predefined in the rule base of the specified application.
The rule base includes a set of lingui stic rules designed
in the form (i f-then rule s). These rule s define the con-
sequent of the model in terms of the given linguistic
variables such as low, moderate and high. In addition, it
specifies the type and number of the used membership
functions of input and output parameters.
The fuz zy infer ence engine is consi dered the central part
of the fuzzy system. In this stage, in order to produce
the intende d output, the inference engine is applied to a
set of rules included in the fuzzy rule base. This pro-
ced ure invo lves ma n y step s as follo ws: first, it matc hes
the linguistic variables of the input with the rules
premises. Second, it activates the matched rules in order
to deduce the resultant of each fired rule, and finally it
combines all consequents by using fuzzy set union in
order to generate the final output which is represented
as fuzzy set output.
The defuzzifier: as described in the previous phase, the
output is produced as a linguistic variable, which is
fuzzy and can be interpreted in different ways. There-
fore, the fuzzy set ou tput in th is stage is co nverted to a
Fuzzy LogicBased Scheme for Load Balancing in Grid Services
Copyright © 2012 S ciRes. JSEA
crisp output, which is a numerical value.
3. Related Work
The issues related to load balancing in the distributed
systems have been addressed broadly by many studies
and researches. In the literature, many studies have
proposed the using of fuzzy logic the ory to solve the l oad
balancing problem. Since the load balancing problem can
be considered as NP-hard [6], in this case heuristics are
sought to tac kle th is problem.
Nejad et al. [8] proposed a fuzzy logic-base load
balanci ng in ce ntraliz ed di stributed sys tem. T he effic iency
of proposed load balancing is studied in OPNET
simulation environment. The lengths of sent packet and
service rate in each node have been considered non-
const ant. The current load of t he syste m and waiting time
for the last processed task in each node are considered as
the fuz zy co ntr oll er inp uts . Ba sed on the weig ht as signe d
for each node, a percentage of existing tasks is assigned
to that nod e . The result of the experi ments in the states of
constant and variable nodes, and constant and variable
tasks, indicates that this algorithm performs much better
than both static and dynamic algorithms in terms of
thro ughput, dr op rate and response time.
Bey et al. [9] introduced a model that can be used for
pre dic tin g the f utur e CP U lo ad in a d yna mic en viro n ment.
The proposed model is for single-step-ahead CPU load
prediction. The multiple local Adaptive Networks-based
Fuzzy Inference Systems (ANFIS) predictors were used
to build the introduced prediction model. In turn, the
predictors controlled via the Naïve Bayesian Network
(NBN) inference between clusters states of CPU load
time points obtained b y C-me ans clustering process. The
ANFIS models were used to carry out short-term
accurate and mid-term reliable prediction. The selection
was based on the basis of divide-and-conquer principle
that divides a CPU load time series into several clusters.
Afterwards, those clusters can be used separately. I ndeed,
in that case, the ANFIS system behaved accurately and
gave a reasonable performance.
Figure 1. The general architecture of the fuzzy logic inference
Revar et al. [10] focused on analyzing load balancing
requirements in a grid environment and proposed design
and implementations of innovative load balancing system
for grid environment using machine learning. Their
technique balances the load dynamically. The technique
uses initial load information sto red in the database at t he
initial level. When load imbalance takes place, the
current load information is collected and stored as raw
data. Afterwards, various machine learning algorithms
have been used to process and analysis the recorded data.
As a final step, the rules automatically generated b y data
mining techniques and used for migrating jobs for load
K. Ming and C. Hsun [11] designed and implemented
a load-balancing system based on fuzzy logic control in
loosely coupled distributed system. The run-q ueue le ng t h
and CPU utilization were used as input variables. They
used si x wo r ksta tions running different UNIX OS to bu ild
a heterogeneous computing environment. These works-
tations are located on different workstations and connected
by communication network. Based on their experimental
results, the fuzzy logic model for load balancing decreased
effectively the amount of communication traffic of the
network and provided substantial enhancement in the
overall performance.
Rantonen et al. [12] designed and implemented a
fuzzy expert system for load balancing in a symmetric
multiprocessor environment. The novelty of their
algorithm is the use of the algorithm for load balancing
only on demand, instead of using the algorithm
periodically. Thereby the computations overhead can be
reduced that makes the algorithm more fair and fast. Two
parameters are considered as input to the proposed model:
the number of threads per processor and the sum load of
ready queues. They claimed that, the evaluation results
proved the best load balance using different techniques.
To the best of our knowledge, none of the previous
proposed fuzzy logic-based techniques has used more
than two parameters to evaluate the workload. In this
paper, to evaluate the node’s workload, we use four
parameters (Node’s ready queue length, burst time, CPU
utilization, and the available resources needed to
accomplish the assigned tasks), for the sake of more
accuracy. Moreover, two fuzzy-logic based models are
used i n di fferent level s whi ch ar e node -level and clu ster-
4. The Proposed Fuzzy-Logic-Based Load
Balancing Scheme
In the proposed scheme, the load balancing task is de-
signed to be performed at two levels: i) at the cluster
level by global manager, and ii) at the node level by local
manager. The grid consists of many clusters, where sev-
Fuzzy LogicBased Scheme for Load Balancing in Grid Services
Copyright © 2012 S ciRes. JSEA
eral nodes constitute one cluster. In each cluster, there is
a local manger to evaluate the states of the nodes be-
longing to its c luster and to per form local load balancing
in its own cluster. The nodes of each cluster only com-
municate with the local manager. Assuming that, the task
allocated to each cluster/node will be processed in that
cluster/node and will not be relocated. Global manager
communicates with the local manager of each cluster.
The states of nodes in each cluster are sent to the global
manager, to specify the state of the corresponding cluster.
The struct ure of the fuzzy lo gic-based scheme is d e mon-
strated in Figure 2. I n this scheme, the workload state for
the nodes and the clusters are evaluated. This evaluation
is carried out in the grid and the cluster levels by the
glob al and the local manager s respectively. Based on the
generated states, a newly arrived task will be assigned to
the lightly loaded cluster. Then, based on the states of the
nodes, the arrived tasks will be assigned to the lightly
loaded node/nodes. Deciding the state of the nodes is
made by fuzzy logic-based model, and thereby the clus-
ters states are evaluated. Four parameters as mentioned
above are collected from the current information tasks
and used as inputs to the fuzzy model. The process of
load balancing is handled in different stages as follow-
4.1 Data Collection Stage
Fuzz y logic -based scheme need s data about each node to
determine the state of that node. The parameters collector
collects the data about each node from the table contains
the information about the current tasks. This collected
information is then passed to the FI S that performs the
needed analysis and takes a decision about the node’s
state. All the nodes sates are sent from time to time to the
local manager that carries out the local balancing. To
exchange the data, each of the nodes notifies its manager
about its load state periodically. That is, to specify the
current state of the grid, clusters and nodes, the balancing
data in the system, is updated without any request. Each
local manager does the task of collecting the nodes data,
in its group, by using the data collector. Then, based on
the data received from the nodes, the fuzzy logic scheme
evaluates the workload of the nodes, determines their
states, and sends the results to the local manager. After-
wards the local manager store s the wor kload states of the
nodes and sends a copy to the global manager. Based on
the data received from local managers, the global man-
ager calculates the workload of each cluster and decides
about its global state in general, and also sends the clus-
ters states to the grid manager.
4.2 Specifying the Workload Sate stage
In this stage, the workload state of each node is deter-
mined and the decision on load distribution is taken. We
consider four different inputs variables to the fuzzy logic
model, namely: the node’s ready queue length, the esti-
mated burst time of the tasks assigned to the node, the
CPU bound for each task, and the available resources for
each node. Three fuzzy subsets have been considered for
each input to determine the fuzz y rules in order to get the
4.3 Distribution Satge
The grid manager, based on the inferred states of differ-
ent nodes and clusters, ranks the clusters and nodes
based on their availability. In that sense, the distributor
routes the arriving tasks to the appropriate cluster and
node based o n the rank. I n this stage, the grid d istributor
utilizes the workload information about the different
clusters to decide straightforwardly which cluster is
lightly loaded. It then distributes the new arriving tasks
to the clusters/nodes based on their workload states col-
lected in advance. Finally, the local manager inside the
target cluster forwards the arrived tasks to the lightly
loaded nodes based on their inferred states.
5. Implementation of the Proposed Scheme
Using Matlab’s FIS toolbox, we implemented two fuzzy
logic based models. The first model to evaluate the
node’s workload based on four parameters explained
above while the second fuzzy model is to evaluate the
clusters workload based in the nod es state s that belo ng to
the target cluster. Following, we will detail the design
and implementation of each model by introducing its
inputs, rules, output, and accuracy measure when train-
ing and validati ng each model.
Figure 2. Th e architecture of the propos ed scheme
Fuzzy LogicBased Scheme for Load Balancing in Grid Services
Copyright © 2012 S ciRes. JSEA
5.1 Fuzzy Logic Model – Node Level
Each input variable is represented by five Membership
Functions (MFs) of Gaussian type except the last para-
meter only three MFs are used. For example, when the
length of queue is 45, this value has around 0.05 degree
of membership to the function as short and about 0.75
degree of membership to the function as moderate and
also has zero degree of membership to the function is
long. Thus, we can classify the queue length of 45 as
moderate. For all the input s t he r ange s di ffer fro m o ne to
another. For instance, the range of queue length is [0
-100]. However, when the queue length is greater than
the maximum in the range which is 100, the MF is de-
fined as very long because the degree of membership to
the function very long is 1. The MFs for the rest of the
input variables operate in a similar way. In general, the
fuzzy rule base includes a set of linguistic rules. Those
rules represent the communitarians between the different
input parameters, where each parameter is represented by
linguistic variables. That is, the rules promises represent
the input parameters, where the consequent of each rule
indicates the resultant output. The rules are triggered by
mapping the input parameters to the input functions
which might be one or more. In our system, based on the
given inputs to the FIS and the degree membership of
each, 375 rules are obtained; t wo of them are shown in
Figure 3.
For example, in rule1, if the number listed in the ready
queue is low, and the CPU utilization is small, and the
available resources for the corresponding node to achieve
the assigned tasks are high, then the fuzzy output which
indicates the node state, would be very light. While in
rule 2, if the ready queue length is high and the CPU is
very heavily utilized and the resources needed are not too
much, then the node state would as heavy loaded node
and no more tasks will be send to it. The output of the
fuzzy model represents the workload value of the cor-
responding node based on the given parameters. For
trai ni ng a nd te sti ng the p ro p o sed fuz z y mo de l t o e va l uat e
the node’s workload, we generated a dataset consists of
the input parameters and has one output represents the
worklo ad base d on the give n input s. The d ataset consi sts
of six hundred data samples. To build the fuzzy logic
based model, the dataset is divided into training set and
validation set where 70 % of the samples are used for
training the model and 30% are used for validation. Fig-
ure 4 shows the actual and predicted output for both
training and testing stages. It is obvious from Figure 4
that no much difference between the actual and predicted
workload for each node. The accuracy measure for the
model is measured in terms of error measure, where the
Root Mean Square Error (RSME) for training and vali-
dation are 0.000291 and 0.00398 respectively.
5.2 Fuzzy Logic Model – Cluster Level
Similarly for this model, the input variables are
represented by five MFs of Gaussian type. Since the in-
put value is the node’s sate, the five MFs representing
the load state are ‘very light’, ‘light’, ‘moderate’, heavy’
and ‘very heavy’. The range of each parameter is be-
tween 0 and 1 representing the workload for each node
assu ming tha t we ha ve t wenty no des in e ach cl uster. T he
output of the fuzzy model represents the workload state
of the corresponding cluster based on its nodes states.
We trained and evaluated the proposed fuzzy log-
icbased model at the cluster level by using generated
dataset which consists of 300 data points. Each data
point has 4 inputs represents the cluster’s nodes states
and one outp ut which indicate s the workload o f the clus-
ter. 70% of the samples were used for training the model,
and 30% of the samples have been used for validation.
Figure 5 shows the actual and predicted output for both
training and testing stages. It is obvious from Figure 5
that, no much difference between the actual and pre-
dicted workload for each cluster. The accuracy measure
for the model is measured in terms of error measure,
where RSME were as follows: 0. 0.00095 for training
and 0.00329 for validation re sp e c tively.
6. Simulation and Experimental Results
In the simulatio n, we consider ed that a grid composed of
four independent clusters of homogenous processing
nodes. Because of the lack of a mechanism in the
computing platform to notify configuration changes to
the system, we assume to have a static number of
resource instances available to each cluster’s processing
node. In addition, we consider that the number of nodes
equally distributed among the different clusters where
each cluster consists of twenty nodes. We consider a
continuous stream of independent tasks which arrive to
the s ystem and are st ored into a s ingle jo b que ue. All t he
arrived tasks are not preemptable, and each task is
described by five attributes: the task ID, task’s
submission time, task’s deadline time, an estimation of
its execution time, and it’s CPU-bound. Moreover, a
dataset contains information about 5000 tasks was
generated and used. In the first run of the simulation, a
Figure 3 . Rules for fuzzy i nference system
1. If (ReadyQueueLength is VeryShort) and (BurstTime is
VeryLow) and (CPUBound is VeryLow) and
(AvailableResources is Low) then (WorkloadSatae is out1mf1
2. If (ReadyQueueLength is VeryHigh) and(BurstTime is
VeryHigh) and (CPUBound is VeryHigh) and
(AvailableResources is High) then (WorkloadSatae is
Fuzzy LogicBased Scheme for Load Balancing in Grid Services
Copyright © 2012 S ciRes. JSEA
Figure 4 . The fuzzy model – Node s tate (training and testi ng
accuracy measu res)
Figure 5. The fuzzy model Cluster state (training and
testing accuracy measures)
random algorithm has been used to assign around 800
tasks to the available clusters and nodes randomly
without asses sing the clusters and nodes workload state s.
That is, all the jobs entered the system in a unifor m way.
Figure 6 and 7 demonstrate the distribution of the
worklo a d amon g the d i ffe re nt c lust ers and var io us no de s.
It is obvious form the charts that the workload was not
distributed in a fair manner where some nodes have
workload more than other. It is noticeable, in the
different clusters the workload was not equally
distributed among the different nodes in each cluster. For
instance, in the first cluster the nodes 2 and 5 have the
heaviest workload, whereas the nodes 1 and 20 in the
same cl uster ha ve much l ess worklo ad. In the same way,
among the different clusters, the workload was not
distributed in a balanced way as shown in Figure 7 which
shows the total number of tasks assigned to each cluster.
In Figure 7, the second and third clusters have almost
equal workload. Similarly, the cluster 1 and 4 have
almost identical load. Nevertheless, the number of tasks
assigned to cluster 2 almost double the number of tasks
assigned to the first cluster. In the same way, the
workload assigned to the forth cluster equal the half of
the work assigned to the third cluster. After that, we have
considered the case with an initial workload of 800 tasks
that have been assigned randomly to different clusters
and nodes. Then the proposed algorithm was used to
evaluate the state of each node and thereby to evaluate
the clusters workload state. Based on the available in-
formation, the arriving tasks are assigned to the clusters
and nodes that have the lo wer lo ad .
Workload Distribution : First Cluster
The different Node
Thee Number of assigned Tasks
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Node 8
Node 9
Node 10
Node 11
Node 12
Node 13
Node 14
Node 15
Node 16
Node 17
Node 18
Node 19
Node 20
Workload Distribution : Second Cluster
The different Node
Thee Number of assigned Tasks
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Node 8
Node 9
Node 10
Node 11
Node 12
Node 13
Node 14
Node 15
Node 16
Node 17
Node 18
Node 19
Node 20
Workload Distribution : Third Cluster
The different Node
Thee Number of assigned Tasks
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 7
Node 8
Node 9
Node 10
Node 11
Node 12
Node 13
Node 14
Node 15
Node 16
Node 17
Node 18
Node 19
Node 20
Figure 6. The workload of nodes in different clusters at a
certain amount of time using random distribution algorithm
(after assigning 800 tasks)
Fuzzy LogicBased Scheme for Load Balancing in Grid Services
Copyright © 2012 S ciRes. JSEA
Figure 8 shows the system state after using the
proposed algorith m to evalua te the worklo ad state s o f the
clusters, and to distribute the new tas ks. Clearly the tasks
are distributed in a balanced way among the different
clusters; where the number of tasks assigned to each
cluster doesn’t vary much form the others. Moreover, the
distribution of workload among the different nodes
inside the same cluster is reasonable.
Even t houg h some nodes may be assigned more work,
most of the nodes have almost identical workload. For
instance, Figure 9 demonstrates the workload states of
the nodes related to the second and third cluster. It is
obvious the workload is distributed in approximately
balanced manner among the different nodes related to
this cluster. In order to evaluate the performance of the
proposed scheme, the load balancing of the overall
system was monitored during different points of time.
Figure 10 shows the states of the different cl utters during
different times. Obviously form Figure 10, during
observed intervals of time the workload was distributed
in almost balanced way. With the passing of time,
despite of the steady increase of the arriving tasks, the
Workload Distribution : Clusters 1- 4
(Randomized Manner)
The different Clusters
Thee Number of assigned Tasks
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Figure 7. The workload distributed randomly among different
clusters at a certain amount of time (after assigning 800
Workload Distribution : Clusters 1- 4
after distributing around 5000
The different Clusters
Thee Number of assigned Tasks
Cl u s ter2
Cl u s ter3
Cl u s ter4
Figure 8. The workload states of the clusters after
distributing the tasks by applying the proposed algor ithm
workload was distributed in a balanced way. That is, the
clusters have almost ide ntical workload. Co mparin g with
the random algorithm, shown in Figures 6 and 7, the
proposed fuzzy logic-based scheme achieves more
efficiency in distributing the arriving tasks based on the
workload states of the available nodes and clusters.
7. Conclusion and Future Work
Fuzzy logic systems have the ability to deal with the
imprecision and uncertainties surrounded the input
variables and their relationships. In this paper, we
presented a new fuzzy logic-based scheme for dynamic
Workload Distribution :Nodes of Cluster 3
After Submission for around 5000 Tasks
The different Clusters
Thee Number of assigned Tasks
Workload Distribution :Nodes of Cluster 2
After Submission for around 5000 Tasks
The different Clusters
Thee Number of assigned Tasks
No de1
No de2
No de3
No de4
No de5
No de6
No de7
No de8
No de9
No de10
No de11
No de12
No de13
No de14
No de15
No de16
No de17
No de18
No de19
No de20
Figure 9 . The workload states o f the different nodes rel ated
to clusters 2 and 3 respectively after distributing the tasks
The Workl oad of Clust er s at di f f erent t i m es
Time Interval
The number of assigned ta
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Figure 1 0. The clusters states at different times
Fuzzy LogicBased Scheme for Load Balancing in Grid Services
Copyright © 2012 S ciRes. JSEA
load balancing in the gr id comput in g services. Four input
variables have been considered in the proposed scheme
to evaluate the workload state of the presented node. W e
evaluated the performance of the proposed scheme in
terms of its ability to keep all nodes and clusters of the
overall system in a balanced way. The simulation res ult s
show that the proposed scheme achieves really
satisfactory and consistently load balancing than in other
randomized approaches. As a future work, it is possible
to use clustering for the nodes in the same cluster based
on their states since enormous number of nodes may
impair the proposed scheme performance. Another goal
is to embed this sch eme into the proposed framework of
the NSTIP project # 10-INF1381-04 in order to validate
the scalability of the proposed framework and to assess
its ability to support the load balance in a grid that hosts
of food, health and nutrition inquire services.
8. Acknowledgment
We would like to thank King Fahd University of Petro-
leum and Minerals for providing the computing faci lities.
The authors would like to acknowledge the support pro-
vided by King Abdulaziz City for Science and Technol-
ogy (KACST ) thr o ug h the Sc i e nce & T e chno log y Unit at
King F ahd University of Petroleum & Minerals (KFUPM)
for funding this work through project No.10-INF1381-04
as part of the National Science, Technology and Innova-
tion Plan.
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