Load Balancing with Task Division

[INTERNATIONAL JOURNAL FOR RESEARCH &
DEVELOPMENT IN TECHNOLOGY]

Volume-5,Issue-2, Feb - 2016
ISSN (O) :- 2349-3585

Load Balancing with Task Division
Ranjan Kumar Mondal1, Enakshmi Nandi2, Payel Ray3, Debabrata Sarddar4
University of Kalyani
Department of Computer Science and Engineering, Kalyani, India
Abstract— Cloud computing represents different ways to

automated to manage servers launching and shutting, load

design and manage remotely computing devices. Service

balancing, failure detection and handling, etc.

providers using customized design of cloud computing to

LOAD BALANCING

public network allows cloud users to establish a relation with
cloud computing. There are several heterogeneous nodes in
a cloud computing system. Namely, every node has different
capability to execute a particular task so only consider the
CPU remaining of the node is not enough when a node is
chosen to execute a task. Therefore, how to select an
efficient node to execute a task is very important in a cloud
computing.In this paper, we propose a scheduling algorithm,
LBTD, which combines minimum completion time and load
balancing strategies. For the case study, LBTD can provide
efficient utilization of computing resources and maintain the

Load balancing is a technique used to distribute the load
uniformly among all the network nodes. With

maximum

throughput and response time, and avoiding the overload
among the network nodes, optimum utilization of resources

is done by static or dynamic

load

balancing.

The load

balancing needs to be done properly because failure in any one
of the node can led to unavailability of data. Load balancing is
a generic term used for distributing a larger processing load to
smaller

processing

nodes

for

enhancing

theoverall

performance of system. An ideal load balancing algorithm
should avoid overloading or under loading on specific

load balancing in cloud computing environment.
Keywords — Cloud computing, Load Balance, Algorithm,

node.But, in case of a cloud computing environment the
selection of load balancing algorithm is not easy because

Scheduling.

it involves additional constraints like security, reliability,
INTRODUCTION
Cloud

computing

is

an e-technology,

throughput etc. So, the main goal of
where

virtual

algorithm

is

to

improve

a

load

balancing

the response time of job by

resources are provided as services over the Internet. Users

distributing the total load of system. The algorithm must also

need not have gain the technology knowledge or control over

ensure that it is not overloading any specific node.

the cloud that support the resources. Cloud Computing is high

balancers can work in two ways: cooperative and non-

utility software having the ability to change the software

cooperative. In cooperative, the nodes work simultaneously in

industry and making the software even more attractive.

order to achieve the common goal of optimizing the overall

Cloud computing has strong impact on software industry and

response time.

their business. The elasticity of resources is unprecedented

independently in order to improve the response time of local

in the history of information technology . The increase in

tasks.

web traffic and different services are increasing day by

A static load balancing algorithm does not take into account

day making load balancing a big research topic. Cloud

the

computing is a new technology which uses implicit machine

distributing the load. On the other hand, a dynamic load

instead of physical machine to host, store and network

balancing algorithm checks the previous state of a node

the different components. Cloud computing are highly

while distributing the load.

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previous

Load

In non-cooperative mode, the tasks run

state

or

behaviour

of

a

node while

83

Volume-5,Issue-2, Feb -2016

ISSN (O) :- 2349-3585

Paper Title:- Load Balancing with Task Division

The dynamic load balancing algorithm is applied either as

THE PROPOSED METHOD

a distributed or non-distributed. The advantage of using

There are several heterogeneous nodes in a cloud computing

dynamic load balancing is that if any node fails, it will not halt

system. Namely, each node has different capability to execute

the system; it will only affect the system performance. In a

task; hence, only consider the CPU remaining of the node is

dynamic load balanced system, the nodes can interact

not enough when a node is chosen to execute a task.

with each other generating more messages when compared to

Therefore, how to select an efficient node to execute a task is

a non-distributed

very important in a cloud computing.

environment.

However,

selecting

an

appropriate server needs real time communication with the

Due to the task that has different characteristic for user to pay

other

hence, generates

execution. Hence it is need some of the resources of specific,

morenumber of messages in the network. Load balancer uses

for instance, when implement organism sequence assembly; it

different technical methods for keeping track of updated

is probable have to big requirement toward memory

information.

remaining. And in order to reach the best efficient in the

The task of load balancing is shared among distributed nodes.

execution each tasks, so we will aimed by tasks property to

In a distributed system, dynamic load balancing can be done in

adopt a different condition decision variable in which it is

two different ways. In the distributed one, the dynamic

according to resource of task requirement to set decision

load balancing algorithm is executed by all nodes present

variable.

nodes

of

the

network

and

in the system and the task of scheduling is shared among all
nodes. Whereas, in the undistributed one, nodes work

A. Service Manager

independently in order to achieve a common goal. We will

The cloud computing environment is composed of

be using three criteria for comparing the load balancing

heterogeneous nodes, where the property of each node may

algorithms:

greatly differ. In other words, the computing capability
-It can be estimated by calculating the total

provided by the CPU, the available size of memory, and

number of jobs executed within a fixed span of time

transmission rate are different. In addition, cloud

without considering the virtual machine creation and

computing utilizes the resources of each node, so the

destruction time.

available resource of each node may vary in a busy

Execution time-It can be estimated by measuring the time

condition. From the perspective of task completion time,

taken for completing the followingprocesses time forformation

the available CPU capacity, the available size of memory

of virtual machines, processing time of a process and

and transmission rate are the three decisive factors for the

destruction time of a virtual machine.

duration of execution.

In general, any cloud computing algorithm performs the
following decision making steps:

To make the manager select appropriate nodes effectively,
all of the nodes (includes service manager and service
node) in the system will be evaluated by the threshold that
is derived from the demand for resource needed to execute

it to data centers.

also selects the algorithm, which will be used for scheduling
tasks.
In this paper, we will analyse the various static and dynamic
load balancing algorithms.

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the task. The service manager that passes the ―threshold of
service manager‖ considered effective, and will be the
candidate of effective nodes by manager. The service
nodes that pass the ―threshold of service node‖ considered
effective, and will be the candidate of effective nodes by
service manager.

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Volume-5,Issue-2, Feb -2016
ISSN (O) :- 2349-3585

Paper Title:- Load Balancing with Task Division

Step 2: It is to divide by the lowest subtask to all subtasks of
each nodes.

B. Proposed Method

Step 1: It is to calculate the lowest subtask from all nodes,
respectively.
Step 2: It is to divide by the lowest subtask to all subtasks
TABLE IIIII

of each nodes.
Step 3: It is to add all subtask of each the node
Step 4: It is to find the nodes that have lowest subtask
value i.e. 1. And select that nodes so only each node has
each subtask only.
Step 5: If there more than one same value then select
minimum value and ignore other.

Task
Node
t1
t2
t3
t4

C11

C12

C13

C14

1.2
1.2
2.1
1

1.3
1.8
2.6
1.4

1
1
1
1.1

1.4
1.9
2.7
1.8

Step 4: It is to find the nodes that have lowest subtask value
i.e. 1. And select that nodes so only each node has each
subtask only.

Step 6: If there is no lowest value then choose other lower

TABLE IVV

value for corresponding nodes.
Step 7: Repeat Step 6 until all tasks have been completed
totally.
Step 8: Then assign original task value of each node.

C. Case Study

In the following section, an example to be executed by
using the proposed algorithm is given.

Task

Node

C11
12
16
26
17

t1
t2
t3
t4

TABLE I
C12
13
24
31
24

C13
10
13
12
18

C14
14
25
33
31

Step 1: It is to calculate the lowest subtask from all nodes,
respectively.

Task
Node
t1
t2
t3
t4

C11

C12

C13

C14

1.2
1.2
2.1
1

1.3
1.8
2.6
1.4

1
1
1
1.1

1.4
1.9
2.7
1.8

Step 5: If there more than one same value then select
minimum value and ignore other.
TABLE V
Task
Node
t1
t2
t3
t4

C11

C12

C13

C14

1.2
1.2
2.1
1

1.3
1.8
2.6
1.4

1
1
1
1.1

1.4
1.9
2.7
1.8

Step 6: If there is no lowest value then choose other lower
value for corresponding nodes.
TABLE VI

TABLE III
Task
Node
t1
t2
t3
t4

C11

C12

C13

C14

1.2
1.2
2.1
1

1.3
1.8
2.6
1.4

1
1
1
1.1

1.4
1.9
2.7
1.8

Task
Node
t1
t2
t3
t4

C11

C12

C13

C14

1.2
1.2
2.1
1

1.3
1.8
2.6
1.4

1
1
1
1.1

1.4
1.9
2.7
1.8

Step 8: Then assign original task value of each node.

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Volume-5,Issue-2, Feb -2016
ISSN (O) :- 2349-3585

Paper Title:- Load Balancing with Task Division

scheduler in this paper is minimizing makespan and
TABLE VII
Task
Node
t1
t2
t3
t4

maximizes resources utilization.

C11

C12

1

C14

ACKNOWLEDGMENT

1.2
1.2
2.1
1(17)

1(13)
1.3(24)
2
1.1

1(10)
1(13)
1(12)
1.1

1(14)
1.3
1.9
1.4

We would like to thank our HOD Dr. kalyani Mali in
department

of

Computer

andEngineering

in

University of Kalyani for purpose of laboratory and university
infrastructure.

Final result following:

REFERENCES

TABLE VIII

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Hung, Che-Lun, Hsiao-hsi Wang, and Yu-Chen Hu.
"Efficient

Task
Node
t1
t2
t3
t4

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Load

Balancing

Algorithm

for

Cloud

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C12

C13

C14

Computing Network." In International Conference on

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16
26
17

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24
31
24

10
13
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14
25
33
31

Information Science and Technology (IST 2012), April,
pp. 28-30. 2012.
[2].

Armstrong, R., Hensgen, D., Kidd, T.: The relative
performance

of

various

mapping

algorithms

is

independent of sizable variances in run-time predictions.
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Ritchie, G., Levine, J. A Fast, Effective Local Search for
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[4].

Wang, S. C., Yan, K. Q., Liao, W. P., Wang, S. S.:
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MM

Our Approach

[5].

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Fig1 : The comparison of completion time of each task at
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CONCLUSIONS
In this paper we proposed an efficient scheduling algorithm,

[6].

International

computing nodes according to their resource capability.

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of

Computer

Science

and

ISSN 2348-120X

(online) Vol. 3, Issue 4, pp: (162-166), Month: October -

performance than other algorithms, such as MM and LBMM

newscheduling algorithm for scheduling. The goal of the

Journal

Information Technology Research

Similarly, our approach can achieve better load balancing and

In this paper, we have presented a

Ranjan Kumar Mondal, Debabrata Sarddar ―Load
Balancing with Task Subtraction of Same Nodes‖.

PMLB, for the cloud computing network to assign tasks to

from the case study.

Ranjan Kumar Mondal, Enakshmi Nandi, and Debabrata

December 2015.
[7].

Ranjan Kumar Mondal, Payel Ray, Debabrata Sarddar
―Load Balancing with Task Division and Addition‖.

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Volume-5,Issue-2, Feb -2016
ISSN (O) :- 2349-3585

Paper Title:- Load Balancing with Task Division

International Journal of Scientific Research Engineering

University of Technology, West Bengal, India.

& Technology (IJSRET), ISSN 2278 – 0882, Volume 5,

present, she is a Ph.D research scholar in Computer

Issue 1, January 2016: 15-19.

Science and Engineering from University of Kalyani. Her

AUTHORS PROFILE

research interests include Cloud Computing, Wireless
Adhoc and Sensor Network and Mobile

Ranjan Kumar Mondal received his

Engineering

M.Tech in Computer

Science

Engineering

University of

from

At

Communication

Systems.

and
Debabrata Sarddar is an Assistant

Kalyani, Kalyani, Nadia; and B.Tech in

Professor

Computer Science and Engineering

Computer

from

of

University of Kalyani, Kalyani, Nadia,

Berhampore,

West Bengal, India. He completed his

under West Bengal

PhD from Jadavpur University. He did

and

Government

Textile

Technology,

Murshidabad, West Bengal

College

University of Technology, West Bengal, India.

at

the Department

of

Science and Engineering,

At

his M. Tech in Computer Science & Engineering from

a Ph.D research scholar in Computer

DAVV, Indore in 2006, and his B.E in Computer Science

Science and Engineering from University of Kalyani. His

& Engineering from NIT, Durgapur in 2001. He has

research interests include Cloud Computing, Wireless and

published more than 75 research papers in different

Mobile Communication Systems.

journals and conferences. His research interests include

present, he is

Wireless and Mobile Systems and WSN and Cloud
Enakshmi Nandi received her M. Tech
in

VLSI

and

electronics from Techno
Lake,

West

computing.

MicroIndia,

Salt

and

B.

Bengal

Tech in Electronics

and

Communication Engineering from JIS
College of Engineering, West Bengal under West Bengal
University of Technology, West Bengal, India.

At

present, she is Research scholar in Computer Science and
Engineering from University of Kalyani. Her research
interests

include

Cloud

Computing,

Mobile

Communication system, Device and Nanotechnology.

Payel Ray received her M.Tech in
Computer
Engineering

Science
from

and
Jadavpur

University, Jadavpur, India; and
B.Tech in Computer Science &
Engineering

from

Murshidabad

College of Engineering and Technology, Berhampore,
Murshidabad, West Bengal

under West Bengal

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