Grid computing for load balancing strategies

International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
Volume 1 Issue 3, December 2012
www.ijsr.net
Grid Computing for Load Balancing Strategies
Kavya S.A1
, M.V.Panduranga Rao2
, S.Basavaraj Patil3
1
Dept of Computer Science and Engineering
BTL Institute of Technology
Bangalore, India
sa.kavya@gmail.com
2
Bangalore, India
raomvp@yahoo.com
3
csehodbtlit@gmail.com
Abstract: In this paper, we addressed the problem of load balancing in large scale distributed systems. We study various
load balancing strategies based on a tree representation of a Grid. The study allows transforming any Grid architecture
into a unique tree with at most four levels. From this generic tree, we can derive three sub models depending on the
elements that compose a Grid. Using this model, we defined a hierarchical load balancing strategy that privileges local
balancing in first (load balance within groups without communication between groups). After load balancing at group
level (if load is not balanced at group level) it will be balanced at region level and after balancing at region level (if load is
not balanced at region level) it will be balanced at grid level.
Keywords: Dynamic Load Balancing, Static Load Balancing.
1.Introduction
Grid computing is a form of networking. Unlike
conventional networks that focus on communication among
devices, grid computing harnesses unused processing cycles
of all computers in a network for solving problems too
intensive for any stand-alone machine. Grid computing
requires the use of software that can divide and farm out
pieces of a program to as many as several thousand
computers. Grid computing can be thought of as distributed
and large-scale cluster computing and as a form of network
distributed parallel processing. It can be confined to the
network of computer workstations within a corporation or it
can be a public collaboration (in which case it is also
sometimes known as a form of peer-to-peer computing).A
number of corporations, professional groups, university
consortiums, and other groups have developed or are
developing frameworks and software for managing grid
computing projects.
The European Community (EU) is sponsoring a project
for a grid for high-energy physics, earth observation, and
biology applications. In the United States, the National
Technology Grid is prototyping a computational grid for
infrastructure and an access grid for people. Sun
Microsystems offers Grid Engine software. Described as a
distributed resource management (DRM) tool, Grid Engine
allows engineers at companies like Sony and Synopsys to
pool the computer cycles on up to 80 workstations at a time.
(At this scale, grid computing can be seen as a more extreme
case of load balancing).
Grid computing appears to be a promising trend for
three reasons: (1) its ability to make more cost-effective use
of a given amount of computer resources, (2) as a way to
solve problems that can't be approached without an
enormous amount of computing power, and (3) because it
suggests that the resources of many computers can be
cooperatively and perhaps synergistically harnessed and
managed as a collaboration toward a common objective. In
some grid computing systems, the computers may
collaborate rather than being directed by one managing
computer. One likely area for the use of grid computing will
be pervasive computing applications - those in which
computers pervade our environment without our necessary
awareness.
Thus grid computing is widely used to solve large-scale
computational problems. Unlike traditional cluster
computing, computational capabilities of resources in grid
computing environments are usually different. In order to
fulfill the user expectations in terms of performance and
efficiency, the Grid system needs efficient load balancing
algorithms for the distribution of tasks. A load balancing
algorithm attempts to improve the response time of user’s
submitted applications by ensuring maximal utilization of
available resources. The main goal is to prevent, if possible,
the condition where some processors are overloaded with a
set of tasks while others are lightly loaded or even idle[4].
Although load balancing problem in conventional distributed
systems has been intensively studied, new challenges in Grid
computing still make it an interesting topic and many
research projects are under way. This is due to the
characteristics of Grid computing and the complex nature of
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the problem itself. Load balancing algorithms in classical
distributed systems, which usually run on homogeneous and
dedicated resources, cannot work well in the Grid
architectures [5].
Grids has a lot of specific characteristics, like
heterogeneity, autonomy, scalability, adaptability and
resources computation-data separation, which make the load
balancing problem more difficult[6]. First we propose a
dynamic tree-based model to represent Grid architecture in
order to manage workload. This model is characterized by
three main features: (i) it is hierarchical; (ii) it supports
heterogeneity and scalability; and (iii) it is totally
independent from any Grid physical architecture. Second, we
develop a hierarchical load balancing strategy and associated
algorithms based on neighborhood propriety. The goal of
this idea is to decrease the amount of messages exchanged
between Grid resources. As consequence, the
communication overhead induced by tasks transferring and
flow information is reduced.
2.Background Work
The structure of the Grid comprises characteristics of
homogeneous as well as heterogeneous systems, loosely
coupled as well as tightly coupled systems. Load balancing
strategies aim to adapt the load optimally to the environment.
However, they mainly consider the application running on a
parallel, homogeneous system. Only a few methods address
also the special characteristics of the underlying system. Zaki
et al. [10] consider different processor speeds and distribute
the load adequately. However, processor speeds are obtained
by a prolong run and they assume full connectivity among
the processors, with uniform latency and bandwidth.
Hendrickson and Devine [4] review the major classes of
dynamic load balancing (DLB) approaches. They point out
that for heterogeneous systems, different amounts of
computing power and memory should be considered.
Additionally, they emphasis that network connections with
different speeds are important for a DLB strategy. However,
a solution is not proposed. Kielmann et al. [5] emphasis that
a collection of clusters can be seen as a hierarchical system.
They use a tree topology to do load balancing for divide-
and-conquer applications. However, they do not take
different PE characteristics into account. Willebeek and
Reeves present in [9] a hierarchical balancing method
(HBM). HBM is an asynchronous, global approach which
organizes the system into a hierarchy of subsystems. The
strategy has been implemented on a hypercube system. Due
to its hierarchical structure, it is not necessary to perform any
analysis of the topology at the beginning as well as
modifications during runtime. Especially [5] and [9] show
the importance of considering the underlying network. We
did not found any methods in the literature that detect the
hierarchical structure of a Grid environment and use the
gained results to optimize middleware, as e.g. load
balancing, specifically for this structure.
Min-Min, Max-Min and Sufferage algorithms are
conventional scheduling algorithms and widely used in batch
mode scheduling [5][6]. The details of these algorithms are
as follows:
Min-Min: In the Min-Min scheduling algorithm, the task
with lower computation time has higher priority. And, the
task is assigned to the computing node which can finish
executing it first.
Max-Min: Max-Min is the same as Min-Min in that the
task is assigned to the computing node which can finish
executing it first. But the difference is the task needing more
computation time has higher priority.
Sufferage: The priorities of tasks in the Sufferage
scheduling algorithm are given according to the sufferage
value. This value is determined by the difference in
computation time between the best and second best
computing nodes. Then, the same as the above algorithms,
tasks are assigned to the computing nodes which can finish
them first.
Although the above three scheduling algorithm
performed better than the traditional random or sequential
scheduling approach, they ignore the importance of a
dynamic network status. Usually, each task in a batch has a
different computation and transmission time. The
transmission time includes both transmitting the task to the
computing node and returning the execution result after the
task is finished. Because of the above factors, the total time
for tasks will be effected by not only the computational
capabilities of computing nodes but also the network status
between these computing resources. However, not only above
three scheduling algorithms but also some studies only take the
computational capabilities into consideration and ignore the
importance of the network status [4][11].
The Genetic algorithm (GA) was developed by Holland [8].
It simulates the evolution of natural biology which is based on
Darwinian principles of natural selection. The GA operates on a
population of chromosomes which is encoded according to the
problem. Each chromosome in the population has a potential
solution from the search space. With each generation, the
chromosomes are operated by the reproduction, crossover, and
mutation operators. Through these operators, not only the
superior solutions can be preserved, but also an improved
solution may be generated. Because of the above advantages,
GA is widely used to solve heuristic problems by many
researchers [1][10]. Many researchers have investigated the use
of GA in homogeneous [2] and heterogeneous environments [7].
However, grid computing is a heterogeneous environment, so
the technique for a homogeneous environment is not suitable.
Although [7] proposed a GA based scheduling algorithm for
grid computing environments, the crossover and mutation
operators are controlled only by the fixed number of
generations. In the evolutional phase, it is hard to predict if the
fitness value is local optimization. So, controlling probabilities
by a fixed number of generations is not suitable. In the worst
case, if the mutation probability is gained with the fitness value
not being convergent, the chance that the fitness value
increasing may be lost.
3.PROBLEM DEFINITION
3.1 Existing system:
Load Balancing types:
A typical distributed system will have a number of
interconnected resources who can work independently or in
cooperation with each other. Each resource has owner
workload, which represents an amount of work to be
performed and every one may have a different processing
capability. To minimize the time needed to perform all tasks,
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the workload has to be evenly distributed over all resources
based on their processing speed. The essential objective of a
load balancing consists primarily in optimizing the average
response time of applications, which often means
maintaining the workload proportionally equivalent on the
whole resources of a system. Conceptually, load balancing
algorithms [3] can be classified into two categories: static or
dynamic [7].
The static load balancing problem for a mesh based
application involves partitioning into sub domains. The sub
domains can then be distributed over the processors and
calculation carried out in parallel. Different partitions may
result in different times to completion for the calculation. It
is therefore necessary to examine the quality of the
partitioning based on its effect on the application code.
There are a number of factors.
The computational work of each processor should be
balanced, so that no processor will be waiting for others to
complete. Assuming that the computational work per
processor is proportional to the number of mesh nodes in the
sub domain, and then to achieve load balance it is necessary
for the number of nodes in each sub domain to be the same.
When forming the discredited equations on a node of the
mesh, the contributions from its nearest neighbor nodes will
usually be needed. Depending on the order of the
discretization scheme, contributions from more distant
neighboring nodes may be necessary. On a parallel
computer, accumulating the contributions from nodes that
are not on the current processor will incur communication
cost. It is known that on distributed memory parallel
computers the cost of accessing remote memory is far higher
than that of accessing local memory (typically a ratio of
between 10 to 1000). It is therefore important to minimize
the communication cost.
Dynamic Load Balancing (DLB) is used to provide
application level load balancing for individual parallel jobs. It
ensures that all loads submitted through the DLB environment
are distributed in such a way that the overall load in the system
is balanced and application programs get maximum benefit from
available resources. Current version of the DLB has two major
parts. One is called System Agent that collects system related
information such as load of the system and the communication
latency between computers. The other is called DLB Agent
which is responsible to perform the load balancing. System
Agent has to run all configured machine on the environment
whereas DLB Agent is started by the user. Major components of
the DLB are System Agent, and DLB Agent. Both components
are written with Java. System requirements for DLB are
LINUX/UNIX Operating Systems, Java 1.4 for System Agent
(recommended) and DLB
 In static load balancing, a task is assigned to an
available resource when it is generated or admitted
to the system using a fixed schema. 
 In contrast to static load balancing, dynamic load
balancing allocate/reallocate tasks to resources at
runtime based on no priori task information, which
may determine when and whose tasks can be
migrated. In this way, imbalances load can be
resolved by redistributing tasks in real-time, thus
solving the shortcoming of static load balancing.
However, network traffic for transmitting load
information to the load balancing system would
increase too much due to the decision dynamicity.
Load balancing algorithms can be defined by their
implementation of the following policies [8]: 
 Information policy: specifies what load information
to be collected, when it is to be collected and from
where. 
 Triggering policy: determines the appropriate
moment to start a load balancing operation.
Resource type policy: classifies a resource as server
or receiver of tasks according to its availability
status and capabilities. 
 Location policy: uses results of the resource type
policy to find a suitable partner for a server or
receiver. 
 Selection policy: defines tasks that should be
migrated from overloaded resources to idlest ones. 

Load Balancing Problems:
Although load balancing methods in conventional
parallel and distributed systems has been intensively studied
[4], they do not work in Grid architectures because these two
classes of environments are radically distinct. Indeed, the
schedule of tasks on multiprocessors or multi computers
suppose that processors are homogeneous and linked with
homogeneous and fast networks[9]. The rationale behind this
approach is that:
1. The resources have same capabilities;
2. The interconnection bandwidth between processing
elements is high;
3. Input data is readily available at the processing site;
4. The overall time spent transferring input and output
data is negligible in comparison with the total
application duration. Given the distribution of
tremendous resources in a Grid environment and the
size of the data to be moved, it becomes clear that
this approach is not accurate because following
properties [5,6].
A. Heterogeneity exists in both of computational and
networks resources.
 First, networks used in Grids may differ significantly
in terms of their bandwidth and communication
protocols.
 Second, computational resources are usually
heterogeneous (processors, resource capabilities
memory size and so on). Also different software’s,
like operating systems, file systems; cluster
management software may be heterogeneous.
B. Autonomy: Because the multiple administrative
domains that share Grid resources, a site is viewed as an
autonomous computational entity. It usually has its own
scheduling policy, which complicates the task allocation
problem. A single overall performance goal is not feasible
for a Grid system since each site has its own performance
goal and scheduling decision is made independently of other
sites according to its own performances.
C. Scalability and adaptability: A Grid might grow
from few resources to millions. This raises the problem of
potential performance degradation as the size of a Grid
increases. If the pool of resources can be assumed fixed or
stable in traditional parallel and distributed computing
environments, in Grid dynamicity exists in the networks and
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computational resources.
 First, a network shared by many execution domains
cannot provide guaranteed bandwidth.
This is particularly true for Wide-Area Networks
like Internet.
 Second, both the availability and capability of
computational resources will exhibit dynamic
behavior. On one hand new resources may join the
Grid and on the other hand, some resources may
become unavailable. Resource managers must tailor
their behavior dynamically so that they can extract
the maximum performance from the available
resources and services.
D. Resource selection and computation: Data
separation: In traditional systems, executable codes of
applications and input/output data are usually in the same
site, or the input sources and output destinations are
determined before the submission of an application. Thus the
cost for data staging can be neglected or the cost is a
constant determined before execution and load balancing
algorithms need not consider it. But in a Grid the
computation sites of an application are usually selected by
the Grid scheduler according to resource status and some
performance criterion. Additionally, the communication
bandwidth of the underlying network is limited and shared
by a host of background loads, so the communication cost
cannot be neglected. This situation brings about the
computation-data separation problem: the advantage brought
by selecting a computational resource that can provide low
computational cost may be neutralized by its high access cost
to the storage site. These challenges pose significant
obstacles on the problem of designing an efficient and
effective load balancing system for Grid environments.
Some problems resulting from the above are not solved
successfully yet and still open research issues. Thus it is very
difficult to define a load balancing system which can
integrate all these factors.
3.2 Proposed system:
Tree-Based Balancing Model
In order to well explain our model, we first define
the topological structure for a grid computing.
Grid topology:
We suppose that a grid computing (see Fig. 1) is a finite
set of G clusters Ck , interconnected by gates gtk , k ∈
{0, ..., G − 1}, where each cluster contains one or more sites
Sjk interconnected by switches SWjk and every site contains
some Computing Elements CEijk and some Storage Elements
SEijk , interconnected by a local area network.
Load balancing generic model:
 Level 0: In this first level (top level of the
tree), we have a virtual node that corresponds
to the root of the tree. It is associated to the
grid and performs two main functions: (i)
manage the workload information of the grid; (ii)
decides, upon receiving tasks from users, where
these tasks can be launched, based on the user
requirements and the current load of the grid.
 Level 1: This level contains G virtual nodes, each
one associated to a physical cluster of the grid.
In our load balancing strategy, this virtual node
is responsible to manage workload of its sites.
 Level 2: In this third level, we find S nodes
associated to physical sites of all clusters of the
grid. The main function of these nodes is to
manage the workload of their physical computing
elements.
 Level 3: At this last level (leaves of the tree), we
find the M Computing Elements of the grid
linked to their respective sites and clusters.
Figure 2 shows the generic tree model associated to a
grid, with its three variants: 1/1/M, 1/S/M and
G/S/M.
Figure 1: Example of Grid Computing
Figure 2: Tree-based representation of a grid
Characteristics of the proposed model:
The main features of our proposed load balancing
generic model are listed below:
 It is hierarchical: this characteristic facilitate the
in-formation flow through the tree and well
defines the message traffic in our strategy.
 It supports heterogeneity and scalability of grids:
adding or removing entities (computing elements,
sites or clusters) are very simple operations in our
Cluster C1 Cluster Cm
Cluster Ck
Switch SWjk
Gate k
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model (adding or removing nodes, subtrees).
 It is totally independent from any physical
architecture of a grid: the transformation of a grid
into a tree is an univocal transformation. Each grid
corresponds to one and only one tree.
LOAD BALANCING STRATEGIES:
A. Intra-group load balancing: In this first level,
depending on its current load, each node manager decides to
start a load balancing operation. In this case, the node
manager tries in priority, to load balance its workload among
its computing elements.
B. Intra-region load balancing: In this second level, load
balance concerns region, for which some owner node
managers fail to achieve a local load balance. In this case,
the group manager transfers tasks from overloaded groups to
under loaded ones.
C. Intra-Grid load balancing: The load balance at this
level is used only if some group managers fail to load
balance their workload among their associated groups. If we
have such as case, tasks of overloaded regions are
transferred to under loaded regions by the Grid manager.
The main advantage of this strategy is to privilege local load
balancing in first (within a group, then within a region and
finally on the whole Grid). The goal of this neighborhood
strategy is to decrease the amount of messages between
groups and regions. As consequence of this goal, the
communication overhead induced by tasks transfer is
reduced.
D. Generic strategy: At any load balancing level, we
propose the following strategy:
1. Estimate the current workload of a group, a region or a
Grid: Here we are interested by the information policy to
define what information reflects the workload status of
group/region/Grid, when it is to be collected and from
where. Knowing the number of available elements under his
control and their computing capabilities, each group manager
estimates its own capability and performs the following
actions:
i. Estimates current group workload based on workload
information received periodically from its elements.
ii. Computes the standard deviation over the workload
index in order to measure the deviations between its
involved elements.
iii. Workload information to its manager.
2. Decision-making: In this step the manager decides
whether it is necessary to perform a load balancing operation
or not. For this purpose it executes the two following
actions:
i. Determines the imbalance/saturation state. If we
consider that the standard deviation measures the average
deviation between the processing time of an element and the
processing time of its group (group/region/Grid), we can say
that this element is in balance state when this deviation is
small. Indeed this implies that processing time of each
element converges to the processing time of its group. An
element can be balanced while being saturated. When the
current workload of the element cross its capacity, it is
obvious that it is useless to balance since all belonging
components are saturated.
ii. Partitioning. For an imbalance case, we determine the
overloaded elements (sources) and the under loaded ones
(receivers), depending on processing time of every element
relatively to average processing time of the associated group.
3. Tasks transfer: In order to transfer tasks from overloaded
elements to under loaded ones, we propose the following
heuristic:
i. Evaluate the total amount of load:”Supply”, available
on receiver elements.
ii. Compute the total amount of load:”Demand”,
required by source elements.
iii. If the supply is much lower than the demand (supply
is far to satisfying the request) it is not
recommended to start local load balancing. We
introduce a third threshold, called expectation
threshold , to measure relative deviation between
supply and demand.
iv. Otherwise performs tasks transfer regarding
communication cost induced by this transfer and
according to criteria selection.
4.EXPERIMENTAL STUDY
Modeling parameters:
In order to evaluate the practicability and the performance
of our model we have developed a grid simulator. This
simulator was built in Java and uses the following
parameters:
1) CE’s parameters: these parameters give information
about available CE’s during load balancing period such as:
(i) number of sites; (ii) number of CE’s in each site; (iii)
CE’s speeds; (iv) date to send workload information from
CE’s; and, (v) tolerance factor.
2) Tasks parameters: these parameters include: (i) number
of tasks queued at every CE; (ii) task submission date; (iii)
number of instructions per task; (iv) task size; and, (v)
priority.
3) Network parameter: bandwidth size.
4) Workload index: as workload for Computing Elements,
we have used their occupation ratio: workload=inst/speed,
where inst denotes the total number of instructions
queued on a given CE and speed is its speed.
5) Performance parameters: in our experimentations, we
focused on two performance parameters: tasks average
response time and cost communication.
Experimental results:
All the experiments were performed on PC Pentium IV
of 2.8 GHz, with a 256 MB RAM and running under
Windows XP. In order to obtain reliable results, we
reiterated the same experiments more than ten (10) times.
In the sequel, we will give the experimental results
relating to the response time according to the number of
tasks and according to the number of computing elements.
The following tables (see Tables I and II) show the
variation of the average response time before and after
execution of the intra-site load balancing algorithm.
In Tables I and II, Before and After represent mean
response time before and after load balancing is performed
and cost defines the communication cost expressed in
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seconds. From these tables, we remark that our strategy
leads to a good load balancing:
1) For a number of tasks fixed at 2000 and for a number of
CE’s varying from 50 to 250 by step of 50, we obtain a
gain varying between 10.65% and 21.43%, with negligible cost
of communication.
2) For a number of CE’s equal to 250 and for a number
of tasks varying from 1000 to 2000 by step of 250, the gain
varies from 14.93% to 15.93%.
3) During our experiments, we have remarked that the best
gains are obtained when the grid is in a stable state
(neither overloaded nor completely idle).
Table1: RESPONSE TIME VS NUMBER OF CE’S
(NUMBER OF TASKS=2000)
Table2: RESPONSE TIME VS NUMBER OF TASKS
(NUMBER OF CE’S=250)
Figure 3: Communication time Vs Number of CE’s and tasks
5.Conclusion and Future works
In this paper, we addressed the problem of load balancing
in grid computing. We proposed a load balancing
strategy based on a tree model representation of grid
architecture. The model allows the transformation of any grid
architecture into a unique tree with at most four levels. From
this generic tree, we can derive three sub-models depending on
the elements that compose a grid: one site, one cluster, or in
the general case multiple clusters. Using this model, we
defined a hierarchical load balancing strategy that gives
priority to local load balancing within sites The proposed
strategy leads to a layered algorithm which an prototype was
implemented and evaluated on a grid simulator developed for the
circumstance.The first results of our experimentations show that
the proposed model can lead to a better load balancing
between CE’s of a grid without high overhead. We have
observed that significant benefit in mean response time was
realized with a reduction of communication cost between
clusters.
The model presented in this paper raises a number of
challenges for further researches. First, we plan to test our
model on others grid simulators [11]. Second, we plan to
experiment our model on real grid environments like Globus
[13] and XtremWeb [12], using a realistic grid application, in
order to validate the practicality of the model.
References
[1] Buyya, R., D. Abramson, J. Giddy and H. Stockinger,
2002. Economic models for resource management and
scheduling in grid computing. J. Concurrency and
Computation: Practice and Experience, 14: 1507-1542.
[2] Foster, I., C. Kesselman and S. Tuecke, 2002. The
anatomy of the Grid: Enabling scalable virtual organizations.
Intl. J. High Performance Computing Applications, 15: 3.
[3] Xu, C.Z. and F.C.M. Lau, 1997. Load Balancing in
Parallel Computers: Theory and Practice. Kluwer, Boston,
MA.
[4] Berman, F., G. Fox and Y. Hey, 2003. Grid Computing:
Making the Global Infrastructure a Reality. Wiley Series in
Comm. Networking & Distributed System.
[5] Zhu, Y., 2003. A survey on grid scheduling systems.
Technical report, Department of Computer Science, Hong
#CE’s
Response time (sec)
Cost
(sec)Before After Gain(%)
50 817 730 10.65 41
100 409 353 13.69 50
150 273 230 15.75 49
200 126 99 21.43 47
250 164 139 15.24 49
#CE’s Response time (sec) Cost
(sec)
Before After Gain
(%)
1000 113 95 15.93 22
1250 134 114 14.93 33
1500 145 145 15.86 37
1750 156 156 15.38 47
2000 164 164 15.24 49
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Kong University of science and Technology.
[6] Houle, M., A. Symnovis and D. Wood, 2002.
Dimension-exchange algorithms for load balancing on trees.
In Proc. of 9th Int. Colloquium on Structural Information
and Communication Complexity, pp: 181-196.
[7] Kabalan, K.Y., W.W. Smar and J.Y. Hakimian, 2002.
Adaptive load sharing in heterogeneous
systems: Policies, modifications and simulation. Intl. J.
Simulation, 3: 89-100.
[8] Leinberger, W., G. Karypis, V. Kumar and R. Biswas,
2000. Load balancing across nearhomogeneous multi-
resource servers. In 9th
Heterogeneous Computing
Workshop, pp: 60-71.
[9] Buyya, R., A Grid simulation toolkit for resource
modelling and application scheduling for parallel and
distributed computing. www.buyya.com/gridsim/.
[10] Foster, I. and C. Kesselman, 1997. Globus: a
metacomputing infrastructure toolkit. Intl. J. Super-
Computer and High Performance Computing Applications,
11: 115-128.
[11] GridSim. A grid simulation toolkit for
resource modeling and application scheduling for
parallel and distributed computing.
www.buyya.com/gridsim/.
[12] XtremWeb.A global computing experimental
platform.http://www.lri.fr/fedak/XtremWeb/introduction.ph
[13] I.Foster and C.Kesselman. Globus: a
metacomputing infrastructure toolkit. Int. Jour. of Super-
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Author Profiles
Kavya.S.A received the B.E. degree in Computer Science and
Engineering from BTL Institute of Technology, Bangalore. At
present persuing the Master of Technology in Computer Science
and Engineering Department at BTL institute of Technology,
Bangalore.
M.V.Panduranga Rao is a Researcher at NITK, India. He received
the M.Tech degree in computer Science from Visvesvaraya
Technological University and B.E. degree in Electronics and
Communication from Kuvempu University, Karnataka. He was
Research Associate at JNNCE, during the period from August 1989
– April 2005. He received an award in Okinawa, Japan.
S Basavaraj Patil is the Founder & Principal Consultant;
Predictive Research He received the PhD in, Computer Science &
Engineering from Kuvempu Vishwavidyanilaya and M.Tech, Bio-
Medical Instrumentation from S J College of Engineering, Mysore.
He worked as Assistant Vice President at CIBM Research, and
HSBC Consultant at Manthan Systems and Technical Architect at
Aris Global. He is presently working as head of the department at
BTL institute of technology.
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Grid computing for load balancing strategies

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Grid computing for load balancing strategies