Load Balancing with Task Division and Ad
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 5, Issue 1, January 2016
Load Balancing with Task Division and Addition
Ranjan Kumar Mondal1, Payel Ray2, Debabrata Sarddar3
Department of CSE, University of Kalyani, Kalyani, India
3
Assistant Professor, Department of CSE,
University of Kalyani, Kalyani, India
1, 2
ABSTRACT
Cloud Computing is an emerging computing paradigm.
It aims to share data, calculations, and service
transparently over a scalable network of nodes. Since
Cloud computing stores the data and disseminated
resources in the open environment.
So, the amount of data storage increases quickly. In the
cloud storage, load balancing is a key issue. It would
consume a lot of cost to maintain load information, since
the system is too huge to timely disperse load.
There are several heterogeneous nodes in a cloud
computing system. Namely, each node has different
capability to execute task; hence, 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 new scheduling algorithm
that choose a suitable node with its average task. It is
very easy way to select an appropriate node. This
approach can provide efficient utilization of computing
resources and maintain the load balancing in cloud
computing environment.
Keywords: Cloud Computing,
Distributed System, Threshold.
Load
Balancing,
I. INTRODUCTION
A Cloud computing is emerging as a new paradigm of
large scale distributed computing. It
has moved computing and data away from desktop and
portable PCs, into large data centers. It provides the
scalable IT resources such as applications and services,
as well as the infrastructure on which they operate, over
the Internet, on pay-per-use basis to adjust the capacity
quickly and easily. It helps to accommodate changes
in demand and helps any organization in avoiding
the capital costs of software and hardware. Thus, Cloud
Computing is a framework for enabling a suitable, ondemand network access to a shared pool of
computing resources (e.g. networks, servers, storage,
applications, and services). These resources can be
provisioned and de-provisioned quickly with minimal
management effort or service provider interaction.
This further helps in promoting availability. Due to the
exponential growth of cloud computing, it has been
widely adopted by the industry and there is a rapid
expansion in data-centers.
According to the National Institute of Standards and
Technology (NIST), cloud computing exhibits
several characteristics:
On-demand Self-service- A consumer can unilaterally
provision computing capabilities, such as server time
and network storage, as needed automatically without
requiring human interaction with each service provider.
Broad Network Access- Capabilities are available over
the network and accessed through standard mechanisms
that promote use by heterogeneous thin or thick client
platforms (e.g., mobile phones, tablets, laptops, and
workstations).
Resource
Pooling- The
provider’s
computing
resources are pooled to serve multiple consumers
using a multi-tenant model, with different physical and
virtual resources dynamically assigned and reassigned
according to consumer demand.
There is a sense of location independence in that the
customer generally has no control or knowledge over the
exact location of the provided resources but may be able
to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter). Examples of resources
include storage, processing, memory, and network
bandwidth.
Rapid Elasticity- Capabilities can be elastically
provisioned and released, in some cases automatically,
to scale rapidly outward and inward commensurate with
demand. To the consumer, the capabilities available for
provisioning often appear to be unlimited and can be
appropriated in any quantity at any time.
Measured Service- Cloud systems automatically control
and optimize resource use by leveraging a metering
capability at some level of abstraction appropriate to
the type of service (e.g., storage, processing,
bandwidth, and active user accounts). Resource usage
can be monitored, controlled, and reported, providing
www.ijsret.org
15
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 5, Issue 1, January 2016
transparency for both the provider and consumer of the
utilized service.
II. LOAD BALANCIN
Load balancing in cloud computing is a new challenge
now. Always a distributed solution is required because
it is not always practically feasible or cost efficient
to maintain one or more idle services just as to
fulfill the required demands. Jobs can’t be assigned
to appropriate servers and clients individually for
efficient load balancing as cloud is a very complex
structure and components are present throughout a
wide spread area.
Load balancing algorithms are classified as static and
dynamic algorithms. Static algorithms are suitable for
homogeneous and stable environments and can
produce very good results in these environments.
However, they are usually not flexible and cannot match
the dynamic changes to the attributes during the
execution time. Dynamic algorithms are more
flexible and take into consideration different types of
attributes in the system both prior to and during runtime[1]. Load balancing is the process of improving the
performance of system through a redistribution of load
among processor.
In general, load balancing algorithms follow two major
classifications:
Depending on how the charge is distributed and how
processes are allocated to nodes (the system load);
Depending on the information status of the nodes
(System Topology).
In the first case it designed as designed as centralized
approach, distributed approach or hybrid approach in the
second case as static approach, dynamic or adaptive
approach.
a) Classification According to the System Load
Centralized approach: In this approach, a single
node is responsible for managing the distribution
within the whole system.
Distributed approach: In this approach, each node
independently builds its own load vector by collecting
the load information of other nodes. Decisions are made
locally using local load vectors. This approach is more
suitable for widely distributed systems such as cloud
computing.
Mixed approach: A combination between the two
approaches to take advantage of each approach.
b) Classification According to the System Topology
Static approach: This approach is generally defined in
the design or implementation of the system.
Dynamic approach: This approach takes into
account the current state of the system during load
balancing decisions. This approach is more suitable for
widely distributed systems such as cloud computing.
Adaptive approach: This approach adapts the load
distribution to system status changes, by changing
their parameters dynamically and even their algorithms.
This approach is able to offer better performance when
the system state changes frequently. This approach is
more suitable for widely distributed systems such as
cloud computing.
III. THE PROPOSED METHOD
There are several heterogeneous nodes in a cloud
computing system. All nodes have no capability to
execute same task; hence, 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.
Due to the task maybe has different characteristic for
user to pay execution. Hence it is need some of the
resources of specific, for instance, when implement
organism sequence assembly, it is probable have to big
requirement toward memory remaining. And in order to
reach the best efficient in the execution each tasks, so we
will aimed tasks property to adopt a different condition
decision variable in which it is according to resource of
task requirement to set decision variable.
IV. METHOD
Step 1: It is to calculate the Min Task of each node for
all tasks, respectively.
Step 2: It is to divide with the Min Task to each node for
all tasks, respectively.
Step 3:Againit is to calculate the Min Task of each task
for all nodes, respectively.
Step 4:Again it is to divide with the Min Task to each
task for all nodes, respectively.
Step 5: It is to add the alltasks for all nodes,
respectively.
Step 6: It is to find the maximum added Task from all
node.
Step 6: It is to find the Min Task from selected node.
Step 7: It is to find the next maximum added Task from
all node.
Step 8: It is to find the unassigned Min Task from
selected node.
Step 9: Repeat Step 7 to Step 8, until all tasks have been
completed totally.
In the following section, an example to be executed by
using the proposed algorithm is given.
www.ijsret.org
16
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 5, Issue 1, January 2016
Step 6: It is to find the maximum added Task from all
node.
3 Case study
NodeTask
t1
t2
t3
t4
C11
12
16
26
17
C12
13
24
31
24
C13
10
13
12
18
C14
14
25
33
31
NodeTask
t1
t2
t3
t4
Total
Step 1: It is to calculate the Min Task of each node for
all tasks, respectively.
NodeTask
C11
C12
C13
C14
t1
t2
t3
t4
12
16
26
17
13
24
31
24
10
13
12
18
14
25
33
31
Min
Task
10
13
12
17
C11
1.2
1.3
2.2
1
C12
1.3
1.8
2.9
1.4
C13
1
1
1
1.1
C14
1.4
1.9
2.7
1.8
NodeTask
t1
t2
t3
t4
Total
C11
1.2
1.3
2.2
1
1
C12
1.3
1.8
2.9
1.4
1.3
C13
1
1
1
1.1
1
NodeTask
t1
t2
t3
t4
Total
C14
1.4
1.9
2.7
1.8
1.4
C11
1.2
1.3
2.2
1
C12
1
1.3
2.2
1.1
C13
1
1
1
1.1
C14
1
1.3
1.9
1.2
5.4
C11
1.2
1.3
2.2
1
5.7
C12
1
1.3
2.2
1.1
5.6
C13
1
1
1
1.1
4.1
C14
1
1.3
1.9
1.2
5.4
C11
1.2
1.3
2.2
1
5.7
C12
1
1.3
2.2
1.1
5.6
C13
1
1
1
1.1
4.1
C14
1
1.3
1.9
1.2
5.4
Step 8: It is to find the unassigned Min Task from
selected node.
NodeTask
t1
t2
t3
t4
Total
Step 4:Again it is to divide with the Min Task to each
task for all nodes, respectively.
NodeTask
t1
t2
t3
t4
C13
1
1
1
1.1
4.1
Step 7: It is to find the next maximum added Task from
all node.
Step 3:Againit is to calculate the Min Task of each task
for all nodes, respectively.
NodeTask
t1
t2
t3
t4
Min Task
C12
1
1.3
2.2
1.1
5.6
Step 6: It is to find the Min Task from selected node.
Step 2: It is to divide with the Min Task to each node for
all tasks, respectively.
NodeTask
t1
t2
t3
t4
C11
1.2
1.3
2.2
1
5.7
C14
1
1.3
1.9
1.2
C11
1.2
1.3
2.2
1
5.7
C12
1
1.3
2.2
1.1
5.6
C13
1
1
1
1.1
4.1
C14
1
1.3
1.9
1.2
5.4
C11
1.2
1.3
2.2
1
5.7
C12
1
1.3
2.2
1.1
5.6
C13
1
1
1
1.1
4.1
C14
1
1.3
1.9
1.2
5.4
Again
Step 5:It is to add the alltasks for all nodes, respectively.
NodeTask
t1
t2
t3
t4
Total
C11
1.2
1.3
2.2
1
5.7
C12
1
1.3
2.2
1.1
5.6
C13
1
1
1
1.1
4.1
NodeTask
t1
t2
t3
t4
Total
C14
1
1.3
1.9
1.2
5.4
www.ijsret.org
17
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 5, Issue 1, January 2016
Final Result
NodeTask
t1
t2
t3
t4
REFERENCES
C11
12
16
26
17
C12
13
24
31
24
C13
10
13
12
18
C14
14
25
33
31
V. COMPARISON
40
30
20
10
0
C11 LBMM
C12
MM
OurC13
Approach
C14
Fig 1.The comparison of completion time of each task at
different node for case study.
VI. CONCLUSION
In this paper, we proposed an efficient scheduling
algorithm, LBTSSN, for the cloud computing network to
assign tasks to computing nodes according to their
resource capability. Similarly, our approach can achieve
better load balancing and performance than other
algorithms, such as, MM and LBMM from the case
study.
In this paper, we have presented a new scheduling
algorithm for scheduling. The goal of the scheduler in
this paper is minimizing makespan and maximizes
resources utilization.
ACKNOWLEDGMENT
We would like to express our gratitude to Dr. Kalyani
Mali, Head of Department, Computer Science and
Engineering of University of Kalyani. Without her
assistance and guidance, we would not have been able to
make use of the university’s infrastructure and
laboratory facilities for conducting our research.
[1] Hung, Che-Lun, Hsiao-hsi Wang, and Yu-Chen Hu.
"Efficient Load Balancing Algorithm for Cloud
Computing Network." In International Conference on
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.
In: 7th IEEE Heterogeneous Computing Workshop, pp.
79—87, (1998)
[3] Freund, R., Gherrity, M., Ambrosius, S., Campbell,
M., Halderman, M., Hensgen, D., Keith, E., Kidd, T.,
Kussow, M., Lima, J., Mirabile, F., Moore, L., Rust, B.,
Siegel, H.: Scheduling resources in multi-user,
heterogeneous, computing environments with SmartNet.
In: 7th IEEE Heterogeneous Computing Workshop, pp.
184—199, (1998)
[4] Freund, R. F., Siegel, H. J. : Heterogeneous
processing. IEEE Computer, vol. 26, pp.13—17, (1993)
[5] Ritchie, G., Levine, J.: A Fast, Effective Local
Search for Scheduling Independent Jobs in
Heterogeneous Computing Environments. Journal of
Computer Applications, vol. 25, pp. 1190—1192, (2005)
[6] Braun, T. D., Siegel, H. J., Beck, N., Bölöni, L. L.,
Maheswaran, M., Reuther, A. I., Robertson, J. P., Theys,
M. D., Yao, B., Hensgen, D., Freund, R. F.: A
Comparison of Eleven Static Heuristics for Mapping a
Class of Independent Tasks onto Heterogeneous
Distributed Computing Systems. Journal of Parallel and
Distributed Computing, vol. 61, pp. 810—837, (2001)
[7] Wang, S. C., Yan, K. Q., Liao, W. P., Wang, S. S.:
Towards a Load Balancing in a threelevel cloud
computing network. In: Computer Science and
Information Technology, pp. 108—113, (2010).
[8] Ranjan Kumar Mondal, Enakshmi Nandi, and
Debabrata Sarddar. "Load Balancing Scheduling with
Shortest Load First." International Journal of Grid and
Distributed Computing 8.4 (2015): 171-178.
[9] Ranjan Kumar Mondal, Debabrata Sarddar ―Load
Balancing with Task Subtraction of Same Nodes‖.
International Journal of Computer Science and
Information Technology Research ISSN 2348-120X
(online) Vol. 3, Issue 4, pp: (162-166), Month: October December 2015.
www.ijsret.org
18
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 5, Issue 1, January 2016
AUTHORS PROFILE
Ranjan Kumar Mondal received
his M.Tech in Computer Science
and Engineering from University
of Kalyani, Kalyani, Nadia; and
B.Tech in Computer Science
and
Engineering
from
Government
College
of
Engineering and Textile Technology, Berhampore,
Murshidabad, West Bengal under West Bengal
University of Technology, West Bengal, India. At
present, he is a Ph.D research scholar in Computer
Science and Engineering from University of
Kalyani. His research interests include Cloud
Computing, Wireless and Mobile Communication
Systems.
Debabrata Sarddar is an Assistant
Professor at the Department of Computer Science and
Engineering from University of Kalyani, Kalyani, Nadia,
West Bengal, India. He completed his PhD
from Jadavpur University. He did his M. Tech in
Computer Science & Engineering from DAVV, Indore
in 2006, and his B.E in Computer Science &
Engineering from NIT, Durgapur in 2001. He has
published more than 102 research papers in different
journals and15 conferences. His research interests
include Cloud Computing, Wireless and Mobile
Communication Systems.
Payel Ray received her M.Tech in Computer Science
and Engineering from Jadavpur
University,
Jadavpur, India; and B.Tech in Computer Science
&Engineeringfrom Murshidabad Collage of Engineering
and Tehnology, Berhampore, Murshidabad, West Bengal
under West Bengal University of Technology, West
Bengal, India. At present, she is a Ph.D research
scholar in Computer Science and Engineering from
University of Kalyani. Her research interests include
Cloud Computing, Wireless Adhoc and Sensor Network
and Mobile Communication Systems.
www.ijsret.org
19
Volume 5, Issue 1, January 2016
Load Balancing with Task Division and Addition
Ranjan Kumar Mondal1, Payel Ray2, Debabrata Sarddar3
Department of CSE, University of Kalyani, Kalyani, India
3
Assistant Professor, Department of CSE,
University of Kalyani, Kalyani, India
1, 2
ABSTRACT
Cloud Computing is an emerging computing paradigm.
It aims to share data, calculations, and service
transparently over a scalable network of nodes. Since
Cloud computing stores the data and disseminated
resources in the open environment.
So, the amount of data storage increases quickly. In the
cloud storage, load balancing is a key issue. It would
consume a lot of cost to maintain load information, since
the system is too huge to timely disperse load.
There are several heterogeneous nodes in a cloud
computing system. Namely, each node has different
capability to execute task; hence, 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 new scheduling algorithm
that choose a suitable node with its average task. It is
very easy way to select an appropriate node. This
approach can provide efficient utilization of computing
resources and maintain the load balancing in cloud
computing environment.
Keywords: Cloud Computing,
Distributed System, Threshold.
Load
Balancing,
I. INTRODUCTION
A Cloud computing is emerging as a new paradigm of
large scale distributed computing. It
has moved computing and data away from desktop and
portable PCs, into large data centers. It provides the
scalable IT resources such as applications and services,
as well as the infrastructure on which they operate, over
the Internet, on pay-per-use basis to adjust the capacity
quickly and easily. It helps to accommodate changes
in demand and helps any organization in avoiding
the capital costs of software and hardware. Thus, Cloud
Computing is a framework for enabling a suitable, ondemand network access to a shared pool of
computing resources (e.g. networks, servers, storage,
applications, and services). These resources can be
provisioned and de-provisioned quickly with minimal
management effort or service provider interaction.
This further helps in promoting availability. Due to the
exponential growth of cloud computing, it has been
widely adopted by the industry and there is a rapid
expansion in data-centers.
According to the National Institute of Standards and
Technology (NIST), cloud computing exhibits
several characteristics:
On-demand Self-service- A consumer can unilaterally
provision computing capabilities, such as server time
and network storage, as needed automatically without
requiring human interaction with each service provider.
Broad Network Access- Capabilities are available over
the network and accessed through standard mechanisms
that promote use by heterogeneous thin or thick client
platforms (e.g., mobile phones, tablets, laptops, and
workstations).
Resource
Pooling- The
provider’s
computing
resources are pooled to serve multiple consumers
using a multi-tenant model, with different physical and
virtual resources dynamically assigned and reassigned
according to consumer demand.
There is a sense of location independence in that the
customer generally has no control or knowledge over the
exact location of the provided resources but may be able
to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter). Examples of resources
include storage, processing, memory, and network
bandwidth.
Rapid Elasticity- Capabilities can be elastically
provisioned and released, in some cases automatically,
to scale rapidly outward and inward commensurate with
demand. To the consumer, the capabilities available for
provisioning often appear to be unlimited and can be
appropriated in any quantity at any time.
Measured Service- Cloud systems automatically control
and optimize resource use by leveraging a metering
capability at some level of abstraction appropriate to
the type of service (e.g., storage, processing,
bandwidth, and active user accounts). Resource usage
can be monitored, controlled, and reported, providing
www.ijsret.org
15
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 5, Issue 1, January 2016
transparency for both the provider and consumer of the
utilized service.
II. LOAD BALANCIN
Load balancing in cloud computing is a new challenge
now. Always a distributed solution is required because
it is not always practically feasible or cost efficient
to maintain one or more idle services just as to
fulfill the required demands. Jobs can’t be assigned
to appropriate servers and clients individually for
efficient load balancing as cloud is a very complex
structure and components are present throughout a
wide spread area.
Load balancing algorithms are classified as static and
dynamic algorithms. Static algorithms are suitable for
homogeneous and stable environments and can
produce very good results in these environments.
However, they are usually not flexible and cannot match
the dynamic changes to the attributes during the
execution time. Dynamic algorithms are more
flexible and take into consideration different types of
attributes in the system both prior to and during runtime[1]. Load balancing is the process of improving the
performance of system through a redistribution of load
among processor.
In general, load balancing algorithms follow two major
classifications:
Depending on how the charge is distributed and how
processes are allocated to nodes (the system load);
Depending on the information status of the nodes
(System Topology).
In the first case it designed as designed as centralized
approach, distributed approach or hybrid approach in the
second case as static approach, dynamic or adaptive
approach.
a) Classification According to the System Load
Centralized approach: In this approach, a single
node is responsible for managing the distribution
within the whole system.
Distributed approach: In this approach, each node
independently builds its own load vector by collecting
the load information of other nodes. Decisions are made
locally using local load vectors. This approach is more
suitable for widely distributed systems such as cloud
computing.
Mixed approach: A combination between the two
approaches to take advantage of each approach.
b) Classification According to the System Topology
Static approach: This approach is generally defined in
the design or implementation of the system.
Dynamic approach: This approach takes into
account the current state of the system during load
balancing decisions. This approach is more suitable for
widely distributed systems such as cloud computing.
Adaptive approach: This approach adapts the load
distribution to system status changes, by changing
their parameters dynamically and even their algorithms.
This approach is able to offer better performance when
the system state changes frequently. This approach is
more suitable for widely distributed systems such as
cloud computing.
III. THE PROPOSED METHOD
There are several heterogeneous nodes in a cloud
computing system. All nodes have no capability to
execute same task; hence, 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.
Due to the task maybe has different characteristic for
user to pay execution. Hence it is need some of the
resources of specific, for instance, when implement
organism sequence assembly, it is probable have to big
requirement toward memory remaining. And in order to
reach the best efficient in the execution each tasks, so we
will aimed tasks property to adopt a different condition
decision variable in which it is according to resource of
task requirement to set decision variable.
IV. METHOD
Step 1: It is to calculate the Min Task of each node for
all tasks, respectively.
Step 2: It is to divide with the Min Task to each node for
all tasks, respectively.
Step 3:Againit is to calculate the Min Task of each task
for all nodes, respectively.
Step 4:Again it is to divide with the Min Task to each
task for all nodes, respectively.
Step 5: It is to add the alltasks for all nodes,
respectively.
Step 6: It is to find the maximum added Task from all
node.
Step 6: It is to find the Min Task from selected node.
Step 7: It is to find the next maximum added Task from
all node.
Step 8: It is to find the unassigned Min Task from
selected node.
Step 9: Repeat Step 7 to Step 8, until all tasks have been
completed totally.
In the following section, an example to be executed by
using the proposed algorithm is given.
www.ijsret.org
16
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 5, Issue 1, January 2016
Step 6: It is to find the maximum added Task from all
node.
3 Case study
NodeTask
t1
t2
t3
t4
C11
12
16
26
17
C12
13
24
31
24
C13
10
13
12
18
C14
14
25
33
31
NodeTask
t1
t2
t3
t4
Total
Step 1: It is to calculate the Min Task of each node for
all tasks, respectively.
NodeTask
C11
C12
C13
C14
t1
t2
t3
t4
12
16
26
17
13
24
31
24
10
13
12
18
14
25
33
31
Min
Task
10
13
12
17
C11
1.2
1.3
2.2
1
C12
1.3
1.8
2.9
1.4
C13
1
1
1
1.1
C14
1.4
1.9
2.7
1.8
NodeTask
t1
t2
t3
t4
Total
C11
1.2
1.3
2.2
1
1
C12
1.3
1.8
2.9
1.4
1.3
C13
1
1
1
1.1
1
NodeTask
t1
t2
t3
t4
Total
C14
1.4
1.9
2.7
1.8
1.4
C11
1.2
1.3
2.2
1
C12
1
1.3
2.2
1.1
C13
1
1
1
1.1
C14
1
1.3
1.9
1.2
5.4
C11
1.2
1.3
2.2
1
5.7
C12
1
1.3
2.2
1.1
5.6
C13
1
1
1
1.1
4.1
C14
1
1.3
1.9
1.2
5.4
C11
1.2
1.3
2.2
1
5.7
C12
1
1.3
2.2
1.1
5.6
C13
1
1
1
1.1
4.1
C14
1
1.3
1.9
1.2
5.4
Step 8: It is to find the unassigned Min Task from
selected node.
NodeTask
t1
t2
t3
t4
Total
Step 4:Again it is to divide with the Min Task to each
task for all nodes, respectively.
NodeTask
t1
t2
t3
t4
C13
1
1
1
1.1
4.1
Step 7: It is to find the next maximum added Task from
all node.
Step 3:Againit is to calculate the Min Task of each task
for all nodes, respectively.
NodeTask
t1
t2
t3
t4
Min Task
C12
1
1.3
2.2
1.1
5.6
Step 6: It is to find the Min Task from selected node.
Step 2: It is to divide with the Min Task to each node for
all tasks, respectively.
NodeTask
t1
t2
t3
t4
C11
1.2
1.3
2.2
1
5.7
C14
1
1.3
1.9
1.2
C11
1.2
1.3
2.2
1
5.7
C12
1
1.3
2.2
1.1
5.6
C13
1
1
1
1.1
4.1
C14
1
1.3
1.9
1.2
5.4
C11
1.2
1.3
2.2
1
5.7
C12
1
1.3
2.2
1.1
5.6
C13
1
1
1
1.1
4.1
C14
1
1.3
1.9
1.2
5.4
Again
Step 5:It is to add the alltasks for all nodes, respectively.
NodeTask
t1
t2
t3
t4
Total
C11
1.2
1.3
2.2
1
5.7
C12
1
1.3
2.2
1.1
5.6
C13
1
1
1
1.1
4.1
NodeTask
t1
t2
t3
t4
Total
C14
1
1.3
1.9
1.2
5.4
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17
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 5, Issue 1, January 2016
Final Result
NodeTask
t1
t2
t3
t4
REFERENCES
C11
12
16
26
17
C12
13
24
31
24
C13
10
13
12
18
C14
14
25
33
31
V. COMPARISON
40
30
20
10
0
C11 LBMM
C12
MM
OurC13
Approach
C14
Fig 1.The comparison of completion time of each task at
different node for case study.
VI. CONCLUSION
In this paper, we proposed an efficient scheduling
algorithm, LBTSSN, for the cloud computing network to
assign tasks to computing nodes according to their
resource capability. Similarly, our approach can achieve
better load balancing and performance than other
algorithms, such as, MM and LBMM from the case
study.
In this paper, we have presented a new scheduling
algorithm for scheduling. The goal of the scheduler in
this paper is minimizing makespan and maximizes
resources utilization.
ACKNOWLEDGMENT
We would like to express our gratitude to Dr. Kalyani
Mali, Head of Department, Computer Science and
Engineering of University of Kalyani. Without her
assistance and guidance, we would not have been able to
make use of the university’s infrastructure and
laboratory facilities for conducting our research.
[1] Hung, Che-Lun, Hsiao-hsi Wang, and Yu-Chen Hu.
"Efficient Load Balancing Algorithm for Cloud
Computing Network." In International Conference on
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.
In: 7th IEEE Heterogeneous Computing Workshop, pp.
79—87, (1998)
[3] Freund, R., Gherrity, M., Ambrosius, S., Campbell,
M., Halderman, M., Hensgen, D., Keith, E., Kidd, T.,
Kussow, M., Lima, J., Mirabile, F., Moore, L., Rust, B.,
Siegel, H.: Scheduling resources in multi-user,
heterogeneous, computing environments with SmartNet.
In: 7th IEEE Heterogeneous Computing Workshop, pp.
184—199, (1998)
[4] Freund, R. F., Siegel, H. J. : Heterogeneous
processing. IEEE Computer, vol. 26, pp.13—17, (1993)
[5] Ritchie, G., Levine, J.: A Fast, Effective Local
Search for Scheduling Independent Jobs in
Heterogeneous Computing Environments. Journal of
Computer Applications, vol. 25, pp. 1190—1192, (2005)
[6] Braun, T. D., Siegel, H. J., Beck, N., Bölöni, L. L.,
Maheswaran, M., Reuther, A. I., Robertson, J. P., Theys,
M. D., Yao, B., Hensgen, D., Freund, R. F.: A
Comparison of Eleven Static Heuristics for Mapping a
Class of Independent Tasks onto Heterogeneous
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Distributed Computing, vol. 61, pp. 810—837, (2001)
[7] Wang, S. C., Yan, K. Q., Liao, W. P., Wang, S. S.:
Towards a Load Balancing in a threelevel cloud
computing network. In: Computer Science and
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[8] Ranjan Kumar Mondal, Enakshmi Nandi, and
Debabrata Sarddar. "Load Balancing Scheduling with
Shortest Load First." International Journal of Grid and
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[9] Ranjan Kumar Mondal, Debabrata Sarddar ―Load
Balancing with Task Subtraction of Same Nodes‖.
International Journal of Computer Science and
Information Technology Research ISSN 2348-120X
(online) Vol. 3, Issue 4, pp: (162-166), Month: October December 2015.
www.ijsret.org
18
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 5, Issue 1, January 2016
AUTHORS PROFILE
Ranjan Kumar Mondal received
his M.Tech in Computer Science
and Engineering from University
of Kalyani, Kalyani, Nadia; and
B.Tech in Computer Science
and
Engineering
from
Government
College
of
Engineering and Textile Technology, Berhampore,
Murshidabad, West Bengal under West Bengal
University of Technology, West Bengal, India. At
present, he is a Ph.D research scholar in Computer
Science and Engineering from University of
Kalyani. His research interests include Cloud
Computing, Wireless and Mobile Communication
Systems.
Debabrata Sarddar is an Assistant
Professor at the Department of Computer Science and
Engineering from University of Kalyani, Kalyani, Nadia,
West Bengal, India. He completed his PhD
from Jadavpur University. He did his M. Tech in
Computer Science & Engineering from DAVV, Indore
in 2006, and his B.E in Computer Science &
Engineering from NIT, Durgapur in 2001. He has
published more than 102 research papers in different
journals and15 conferences. His research interests
include Cloud Computing, Wireless and Mobile
Communication Systems.
Payel Ray received her M.Tech in Computer Science
and Engineering from Jadavpur
University,
Jadavpur, India; and B.Tech in Computer Science
&Engineeringfrom Murshidabad Collage of Engineering
and Tehnology, Berhampore, Murshidabad, West Bengal
under West Bengal University of Technology, West
Bengal, India. At present, she is a Ph.D research
scholar in Computer Science and Engineering from
University of Kalyani. Her research interests include
Cloud Computing, Wireless Adhoc and Sensor Network
and Mobile Communication Systems.
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