Load balancing with switching mechanism in cloud computing environment
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The document discusses the implementation of load balancing mechanisms in a cloud computing environment, focusing on a new approach called switching mechanism that optimally distributes workloads across partitioned cloud servers. It identifies challenges with existing load balancing algorithms and proposes dividing the cloud into geographical partitions for better management and performance. The proposed methodology aims to enhance efficiency and reduce overhead in handling cloud resources while suggesting future work utilizing simulation tools for further development.
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![IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 03 Special Issue: 07 | May-2014, Available @ http://www.ijret.org 44
LOAD BALANCING WITH SWITCHING MECHANISM IN CLOUD
COMPUTING ENVIRONMENT
M. Aruna1
, R. Punithagowri2
1
Assistant Professor (Sr.G), Erode Sengunthar Engineering College, Erode, India.
2
Student, Computer Science and engineering, Erode Sengunthar Engineering College, India
Abstract
Cloud Computing provides computation, software applications, data access management and storage resources without knowing its
computing infrastructure. Cloud Computing is an emerging technology which has thousands of virtual machines consolidated to
provide different services to the end users. End users can submit jobs or tasks to the cloud environment and these jobs are termed as
Load which is executed by the cloud servers and the results are sent back to the end users. Load Balancing Algorithms are developed
to distribute the workload evenly across all the nodes so that all the nodes are available and no node should be heavily loaded.
Several Load Balancing algorithms are compared and the new approach called Switching Mechanism is applied to the partitioned
Cloud Environment.
Keywords— Cloud Computing, Load Balancing, Load Balancer, Main Controller, Partitions
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1. INTRODUCTION
All the large business and small business companies are
moving to cloud environment because of its scalability. The
jobs arriving to the Cloud Environment are executed by the
large data centers which have thousands of blade servers.
Cloud Computing is a Service Oriented Architecture (SOA). It
provides different types of services to the users. Users can get
the services with no need to know their infrastructure. That is,
users do not know where the service is originated and its
infrastructure. Users need to pay only for what they used from
cloud in the form of services. This is the simplicity of Cloud.
There are 4 different types of cloud environment. They are,
Public Cloud(Free of Cost, anyone can access)
Private Cloud(Pay for what you used, only for single
organization people)
Hybrid Cloud(Combined both public & private Clouds)
Community Cloud(For Communication purpose)
Users can access the cloud resources in the form of services.
There are 3 basic services provided by the Cloud
Environment. They are,
Platform as a Service (PaaS)
Software as a Service (SaaS)
Infrastructure as a Service (IaaS)
1.1 Virtualization
Cloud Computing is based on the Concept of virtualization
technology. Virtualization [1] means "something that is not
real but gives all the facilities as a real one". It is the software
implementation on the bare hardware so that the resources
under the hardware can be utilized more effectively. Cloud
Computing uses the virtualization technique to make use the
cloud resources efficiently. Two types of virtualization can be
used in Cloud Environment.
1. Full Virtualization
2. Para Virtualization
1.1.1 Full Virtualization
In Full Virtualization [1], the installation of one computer is
done on the other computer. It will result in a virtual machine
that have all the facilities and softwares that are present in the
actual machine.
1.1.2 Para Virtualization
In Para Virtualization [1], the hardware allows multiple
operating systems to be run on a single machine. For this,
VirtualBox tool is used. Here all the services are not fully
available, rather than the services are provided in a partial
manner.
2. LOAD BALANCING
Load Balancing is a technique to balance the load across cloud
environment. It is the process of transferring load from heavily
loaded nodes to low loaded nodes. As a result, no node should
be heavily loaded. Thereby it will increase the availability of
nodes. If all the jobs are arrived to the single node, then its
queue size is increased and it becomes overloaded. There is a
need to balance the load across several nodes, so that every](https://image.slidesharecdn.com/loadbalancingwithswitchingmechanismincloudcomputingenvironment-140824234347-phpapp01/85/Load-balancing-with-switching-mechanism-in-cloud-computing-environment-1-320.jpg)
![IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 03 Special Issue: 07 | May-2014, Available @ http://www.ijret.org 45
node is in running state but not in overloaded state. The goals
are as follows [2]:
To increase the availability
To increase the user satisfaction
To improve the resource utilization ratio
To minimize the waiting time of job in queue as well as
to reduce job execution time
To improve the overall performance of Cloud
environment
Based on the current state of the system, load balancing
algorithms can be divided into two types:
2.1 Static Schemes
The current status of the node is not taken into account [3]. All
the nodes and their properties are predefined. The algorithm
works based on the predefined information. Since it does not
use current system status information, it is less complex and it
is easy to implement.
2.2 Dynamic Schemes
This type of algorithm is based on the current system
information [3]. The algorithm works according to the changes
in the state of nodes. Dynamic schemes are expensive one and
are very complex to implement but it balances the load in
effective manner.
3. RELATED WORKS
Load Balancing is one of the main issues in Cloud Computing.
Load Balancing is needed to distribute the workload to all the
servers. There are several Load Balancing algorithms have
been developed. Here, some of the Load Balancing algorithms
are compared.
Table.1 Comparison of Load Balancing Algorithms
S.No. Title Description Limitation
1. A Load
Balancing
Model based
on Cloud
Partitioning
for Public
Cloud [4]
Divide the
Public Cloud
Environment
into several
Partitions
Does not
specify the
Cloud
Division
rules and
refreshing
time period
2. Enhanced
Equally
Distributed
Load
Balancing
Algorithm
[5]
Based on the
counter
variable
allocated to
each server,
the requests
are handled
Does not
consider the
availability of
the resources
in each server
3. A New
Approach for
Load
Balancing in
Cloud
Computing
[6]
Combination
of both Min-
Min & Max-
Min
algorithms
Two
algorithms
need to be
executed
4. Power Aware
Load
Balancing for
Cloud
Computing
[7]
Executed by
Cluster
Controller,
based on
threshold
value 75%
creates new
VMs on PMs
Does not
scale up for
large data
centers
5. Efficient VM
Load
Balancing
Algorithm
for Cloud
Computing
Environment
[8]
Enhances
AMLB by
assigning
weight to
each VM &
maintains the
index table
For every
request,
parses the
index table,
thereby
increase in
response time
6. Improved
Max-Min
Algorithm in
Cloud
Computing
[9]
Assigns task
with
maximum
executing
time to
resource
produces
minimum
completion
time
Smaller tasks
has to wait
for a long
time to
execute](https://image.slidesharecdn.com/loadbalancingwithswitchingmechanismincloudcomputingenvironment-140824234347-phpapp01/85/Load-balancing-with-switching-mechanism-in-cloud-computing-environment-2-320.jpg)
![IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 03 Special Issue: 07 | May-2014, Available @ http://www.ijret.org 46
7. Comparison
of Load
Balancing
Algorithms
in a Cloud
[10]
Cloud
Manager
receives all
requests &
places in a
queue then
check for the
availability
and allocate
to VM
Maintaining
all requests
by a Cloud
Manager
creates a
bottleneck
problem
8. Availability
and Load
Balancing in
Cloud
Computing
[2]
XMPP
servers &
XMPP clients
communicate
using MOM.
XMPP clients
sends its
current status
information to
XMPP
servers
Need to
improve
MOM for
Load
Balancing
9. Energy
Efficient
Virtual
Machine
Provision
Algorithm
for Cloud
System [11]
Proposes
Dynamic
Round Robin
Algorithm
which uses 2
rules to
consolidate
Virtual
Machines
Does not
scale up for
large data
centers
The Table.1 shows the comparison of different existing Load
Balancing techniques and their limitations. Each technique has
some limitation. Hence, the new approach need to be
developed that enhances the Cloud Environment in all aspects.
4. PROPOSED WORK
All the Load Balancing Algorithms are applied to the entire
Cloud Environment. Hence, it is difficult to manage the large
Cloud Environment. Dynamic Load Balancing algorithms
distribute the workload by checking the status of each server
which is maintained by a table called Status table. Maintaining
Status table for all the servers is a difficult one and it may not
be updated periodically hence it may not be consistent one.
The proposed work is to apply Load Balancing algorithms
only for the part of the cloud servers and not to the entire
Cloud Environment. So that, the overhead of maintaining
cloud servers is reduced. To distribute the workload in the
partitioned Cloud Environment, two main components are
needed [4]. They are,
1. Main Controller
2. Load Balancer
Before discussing about these components, the method for
partitioning the cloud environment is to be known. The entire
cloud environment is divided into partitions based on its
geographical locations. Data Centers are reside in the country
and each country has its own and unique co-ordinate value.
Based on this co-ordinate value, the Cloud Environment is
divided into partitions. The co-ordinate values are represented
as 35O
41''22.22' N 139O
41''30.12' E. Here, the 3 numeric
values represents the degree, minutes and seconds values and
it also represents the four directions East, West, North and
South.
The Cloud is divided into four partitions as depicted in Fig.1.
Partition-I includes the data centers that are reside in the
North-West direction. Partition II includes the datacenters that
are resided in the North-East direction. Partition III includes
the data centers that are reside in the South-West area and
Partition IV includes the data centers that reside in the South-
East area. For Example, the co-ordinate value of Tokyo is
35O
41''22.22' N 139O
41''30.12' E which reside in the North-
East direction, hence Tokyo belongs to the Partition-II. In the
representation of co-ordinate, it has three parts as degree,
minutes and seconds.
Fig 1 Cloud Partitions
End users from different locations submit their jobs to the
Cloud Environment. All these jobs are received by a Main
Controller which is a single node to manage all the partitions.
Nodes under each partition are managed by a Load Balancer.
Main Controller distributes the jobs to the Load Balancer by
checking its partition status. The partition may be in 3 states as](https://image.slidesharecdn.com/loadbalancingwithswitchingmechanismincloudcomputingenvironment-140824234347-phpapp01/85/Load-balancing-with-switching-mechanism-in-cloud-computing-environment-3-320.jpg)
![IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 03 Special Issue: 07 | May-2014, Available @ http://www.ijret.org 47
Idle, Normal and Overload states. The partition status is set by
the Load Balancer based on the parameters as Number of
CPUs, the CPU processing speeds, the available memory size,
the memory utilization ratio, the CPU utilization ratio and
network bandwidth etc.,
The jobs are received by the Load Balancers and the Load
Balancing algorithms are applied to the partitions. Here the
Switching Mechanism is applied. Switching Mechanism is the
process of switching over to the 2 different algorithms
according to the 2 different situations. Switching Mechanism
contains two different algorithms, one simple algorithm for
Idle state partitions and another one effective algorithm for
Normal state partitions. Round Robin is a simple and cheap
algorithm that can be used for Idle state partitions. An
effective algorithm for Normal state partition should prevent
the partitions becoming overloaded state.
5. CONCLUSIONS AND FUTURE
ENHANCEMENT:
By partitioning method, the overhead in the management of
cloud environment is reduced. Based on the co-ordinate
values, Cloud Environment is divided into partitions. Cloud
division rule [4] issue is resolved by using co-ordinate values
for partitioning. For Idle state partitions, simple existing
algorithm can be used but for Normal state partitions, a new
algorithm is needed to be developed. In future, the concept is
to be implemented using CloudSim tool and also to propose an
effective algorithm that will work for Normal state partitions.
REFERENCES
[1] Ratan mishra & anant jaiswal, "ant colony
optimization: a solution of load balancing in cloud",
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[2] Zenon chaczko, venkatesh mahadevan, shahrzad
aslanzadeh AND christopher mcdermid, "availability
and load balancing in cloud computing", international
conference ON computer AND software modeling,
VOL. 14, PP. 134-140, 2011.
[3] Venubabu kunamneni, "dynamic load balancing for the
cloud", international journal OF computer science
AND electrical engineering, VOL. 1, NO. 1, PP. 33-37,
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[4] Gaochao xu, junjie pang & xiaodong fu, “a load
balancing model based on cloud partitioning for public
cloud ”, ieee transactions ON cloud computing, VOL.
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[11] Ching-chi lin, pangfeng liu & jan-jan wu, "energy-
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computing, PP. 81-88, 2011.](https://image.slidesharecdn.com/loadbalancingwithswitchingmechanismincloudcomputingenvironment-140824234347-phpapp01/85/Load-balancing-with-switching-mechanism-in-cloud-computing-environment-4-320.jpg)
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Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...
Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...eSAT Publishing House 1) A cyclone with wind speeds of 175-200 kph caused massive damage to the green cover of Gitam University campus in Visakhapatnam, India. Thousands of trees were uprooted or damaged.
2) A study assessed different types of damage to trees from the cyclone, including defoliation, salt spray damage, damage to stems/branches, and uprooting. Certain tree species were more vulnerable than others.
3) The results of the study can help in selecting more wind-resistant tree species for future planting and reducing damage from future storms.
Wind damage to buildings, infrastrucuture and landscape elements along the be...
Wind damage to buildings, infrastrucuture and landscape elements along the be...eSAT Publishing House 1) A visual study was conducted to assess wind damage from Cyclone Hudhud along the 27km Visakha-Bheemli Beach road in Visakhapatnam, India.
2) Residential and commercial buildings suffered extensive roof damage, while glass facades on hotels and restaurants were shattered. Infrastructure like electricity poles and bus shelters were destroyed.
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Shear strength of rc deep beam panels – a review
Shear strength of rc deep beam panels – a revieweSAT Publishing House 1) The document reviews factors that influence the shear strength of reinforced concrete deep beams, including compressive strength of concrete, percentage of tension reinforcement, vertical and horizontal web reinforcement, aggregate interlock, shear span-to-depth ratio, loading distribution, side cover, and beam depth.
2) It finds that compressive strength of concrete, tension reinforcement percentage, and web reinforcement all increase shear strength, while shear strength decreases as shear span-to-depth ratio increases.
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Role of voluntary teams of professional engineers in dissater management – ex...
Role of voluntary teams of professional engineers in dissater management – ex...eSAT Publishing House 1) A team of 17 professional engineers from various disciplines called the "Griha Seva" team volunteered after the 2001 Gujarat earthquake to provide technical assistance.
2) The team conducted site visits, assessments, testing and recommended retrofitting strategies for damaged structures in Bhuj and Ahmedabad. They were able to fully assess and retrofit 20 buildings in Ahmedabad.
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Risk analysis and environmental hazard managementeSAT Publishing House This document discusses risk analysis and environmental hazard management. It begins by defining risk, hazard, and toxicity. It then outlines the steps involved in hazard identification, including HAZID, HAZOP, and HAZAN. The document presents a case study of a hypothetical gas collecting station, identifying potential accidents and hazards. It discusses quantitative and qualitative approaches to risk analysis, including calculating a fire and explosion index. The document concludes by discussing hazard management strategies like preventative measures, control measures, fire protection, relief operations, and the importance of training personnel on safety.
Review study on performance of seismically tested repaired shear walls
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Monitoring and assessment of air quality with reference to dust particles (pm...
Monitoring and assessment of air quality with reference to dust particles (pm...eSAT Publishing House This document summarizes a study on monitoring and assessing air quality with respect to dust particles (PM10 and PM2.5) in the urban environment of Visakhapatnam, India. Sampling was conducted in residential, commercial, and industrial areas from October 2013 to August 2014. The average PM2.5 and PM10 concentrations were within limits in residential areas but moderate to high in commercial and industrial areas. Exceedance factor levels indicated moderate pollution for residential areas and moderate to high pollution for commercial and industrial areas. There is a need for management measures like improved public transport and green spaces to combat particulate air pollution in the study areas.
Coastal zones – seismic vulnerability an analysis from east coast of india
Coastal zones – seismic vulnerability an analysis from east coast of indiaeSAT Publishing House This document summarizes an analysis of seismic vulnerability along the east coast of India. It discusses the geotectonic setting of the region as a passive continental margin and reports some moderate seismic activity from offshore in recent decades. While seismic stability cannot be assumed given events like the 2004 tsunami, no major earthquakes have been recorded along this coast historically. The document calls for further study of active faults, neotectonics, and implementation of improved seismic building codes to mitigate vulnerability.
Can fracture mechanics predict damage due disaster of structures
Can fracture mechanics predict damage due disaster of structureseSAT Publishing House This document discusses how fracture mechanics can be used to better predict damage and failure of structures. It notes that current design codes are based on small-scale laboratory tests and do not account for size effects, which can lead to more brittle failures in larger structures. The document outlines how fracture mechanics considers factors like size effect, ductility, and minimum reinforcement that influence the strength and failure behavior of structures. It provides examples of how fracture mechanics has been applied to problems like evaluating shear strength in deep beams and investigating a failure of an oil platform structure. The document argues that fracture mechanics provides a more scientific basis for structural design compared to existing empirical code provisions.
Assessment of seismic susceptibility of rc buildings
Assessment of seismic susceptibility of rc buildingseSAT Publishing House This document discusses the assessment of seismic susceptibility of reinforced concrete (RC) buildings. It begins with an introduction to earthquakes and the importance of vulnerability assessment in mitigating earthquake risks and losses. It then describes modeling the nonlinear behavior of RC building elements and performing pushover analysis to evaluate building performance. The document outlines modeling RC frames and developing moment-curvature relationships. It also summarizes the results of pushover analyses on sample 2D and 3D RC frames with and without shear walls. The conclusions emphasize that pushover analysis effectively assesses building properties but has limitations, and that capacity spectrum method provides appropriate results for evaluating building response and retrofitting impact.
A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...
A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...eSAT Publishing House 1) A 6.0 magnitude earthquake occurred off the coast of Paradip, Odisha in the Bay of Bengal on May 21, 2014 at a depth of around 40 km.
2) Analysis of magnetic and bathymetric data from the area revealed the presence of major lineaments in NW-SE and NE-SW directions that may be responsible for seismic activity through stress release.
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Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...
Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...eSAT Publishing House This document discusses the effects of Cyclone Hudhud on the development of Visakhapatnam as a smart and green city through a case study and preliminary surveys. The surveys found that 31% of participants had experienced cyclones, 9% floods, and 59% landslides previously in Visakhapatnam. Awareness of disaster alarming systems increased from 14% before the 2004 tsunami to 85% during Cyclone Hudhud, while awareness of disaster management systems increased from 50% before the tsunami to 94% during Hudhud. The surveys indicate that initiatives after the tsunami improved awareness and preparedness. Developing Visakhapatnam as a smart, green city should consider governance
Disaster recovery sustainable housing
Disaster recovery sustainable housingeSAT Publishing House This document summarizes a product called H-HUTS that aims to provide sustainable disaster recovery housing. H-HUTS uses cardboard and origami-inspired modular construction to quickly provide temporary shelters after disasters. The cardboard shelters are designed to be upgraded into permanent homes over time using low-cost materials like shotcrete or concrete. The modular design allows for flexibility and community participation in construction. Testing has shown the shelters can withstand wind loads and provide adequate insulation. The goal of H-HUTS is to not just provide initial shelter but help disaster-affected communities regain permanency in housing and recover in the long-term.
Impact of flood disaster in a drought prone area – case study of alampur vill...
Impact of flood disaster in a drought prone area – case study of alampur vill...eSAT Publishing House
Groundwater investigation using geophysical methods a case study of pydibhim...
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Effect of lintel and lintel band on the global performance of reinforced conc...
Effect of lintel and lintel band on the global performance of reinforced conc...eSAT Publishing House
Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...
Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...eSAT Publishing House
Wind damage to buildings, infrastrucuture and landscape elements along the be...
Wind damage to buildings, infrastrucuture and landscape elements along the be...eSAT Publishing House
Role of voluntary teams of professional engineers in dissater management – ex...
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A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...
A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...eSAT Publishing House
Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...
Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...eSAT Publishing House
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- 1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 03 Special Issue: 07 | May-2014, Available @ http://www.ijret.org 44 LOAD BALANCING WITH SWITCHING MECHANISM IN CLOUD COMPUTING ENVIRONMENT M. Aruna1 , R. Punithagowri2 1 Assistant Professor (Sr.G), Erode Sengunthar Engineering College, Erode, India. 2 Student, Computer Science and engineering, Erode Sengunthar Engineering College, India Abstract Cloud Computing provides computation, software applications, data access management and storage resources without knowing its computing infrastructure. Cloud Computing is an emerging technology which has thousands of virtual machines consolidated to provide different services to the end users. End users can submit jobs or tasks to the cloud environment and these jobs are termed as Load which is executed by the cloud servers and the results are sent back to the end users. Load Balancing Algorithms are developed to distribute the workload evenly across all the nodes so that all the nodes are available and no node should be heavily loaded. Several Load Balancing algorithms are compared and the new approach called Switching Mechanism is applied to the partitioned Cloud Environment. Keywords— Cloud Computing, Load Balancing, Load Balancer, Main Controller, Partitions -----------------------------------------------------------------------***------------------------------------------------------------------- 1. INTRODUCTION All the large business and small business companies are moving to cloud environment because of its scalability. The jobs arriving to the Cloud Environment are executed by the large data centers which have thousands of blade servers. Cloud Computing is a Service Oriented Architecture (SOA). It provides different types of services to the users. Users can get the services with no need to know their infrastructure. That is, users do not know where the service is originated and its infrastructure. Users need to pay only for what they used from cloud in the form of services. This is the simplicity of Cloud. There are 4 different types of cloud environment. They are, Public Cloud(Free of Cost, anyone can access) Private Cloud(Pay for what you used, only for single organization people) Hybrid Cloud(Combined both public & private Clouds) Community Cloud(For Communication purpose) Users can access the cloud resources in the form of services. There are 3 basic services provided by the Cloud Environment. They are, Platform as a Service (PaaS) Software as a Service (SaaS) Infrastructure as a Service (IaaS) 1.1 Virtualization Cloud Computing is based on the Concept of virtualization technology. Virtualization [1] means "something that is not real but gives all the facilities as a real one". It is the software implementation on the bare hardware so that the resources under the hardware can be utilized more effectively. Cloud Computing uses the virtualization technique to make use the cloud resources efficiently. Two types of virtualization can be used in Cloud Environment. 1. Full Virtualization 2. Para Virtualization 1.1.1 Full Virtualization In Full Virtualization [1], the installation of one computer is done on the other computer. It will result in a virtual machine that have all the facilities and softwares that are present in the actual machine. 1.1.2 Para Virtualization In Para Virtualization [1], the hardware allows multiple operating systems to be run on a single machine. For this, VirtualBox tool is used. Here all the services are not fully available, rather than the services are provided in a partial manner. 2. LOAD BALANCING Load Balancing is a technique to balance the load across cloud environment. It is the process of transferring load from heavily loaded nodes to low loaded nodes. As a result, no node should be heavily loaded. Thereby it will increase the availability of nodes. If all the jobs are arrived to the single node, then its queue size is increased and it becomes overloaded. There is a need to balance the load across several nodes, so that every
- 2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 03 Special Issue: 07 | May-2014, Available @ http://www.ijret.org 45 node is in running state but not in overloaded state. The goals are as follows [2]: To increase the availability To increase the user satisfaction To improve the resource utilization ratio To minimize the waiting time of job in queue as well as to reduce job execution time To improve the overall performance of Cloud environment Based on the current state of the system, load balancing algorithms can be divided into two types: 2.1 Static Schemes The current status of the node is not taken into account [3]. All the nodes and their properties are predefined. The algorithm works based on the predefined information. Since it does not use current system status information, it is less complex and it is easy to implement. 2.2 Dynamic Schemes This type of algorithm is based on the current system information [3]. The algorithm works according to the changes in the state of nodes. Dynamic schemes are expensive one and are very complex to implement but it balances the load in effective manner. 3. RELATED WORKS Load Balancing is one of the main issues in Cloud Computing. Load Balancing is needed to distribute the workload to all the servers. There are several Load Balancing algorithms have been developed. Here, some of the Load Balancing algorithms are compared. Table.1 Comparison of Load Balancing Algorithms S.No. Title Description Limitation 1. A Load Balancing Model based on Cloud Partitioning for Public Cloud [4] Divide the Public Cloud Environment into several Partitions Does not specify the Cloud Division rules and refreshing time period 2. Enhanced Equally Distributed Load Balancing Algorithm [5] Based on the counter variable allocated to each server, the requests are handled Does not consider the availability of the resources in each server 3. A New Approach for Load Balancing in Cloud Computing [6] Combination of both Min- Min & Max- Min algorithms Two algorithms need to be executed 4. Power Aware Load Balancing for Cloud Computing [7] Executed by Cluster Controller, based on threshold value 75% creates new VMs on PMs Does not scale up for large data centers 5. Efficient VM Load Balancing Algorithm for Cloud Computing Environment [8] Enhances AMLB by assigning weight to each VM & maintains the index table For every request, parses the index table, thereby increase in response time 6. Improved Max-Min Algorithm in Cloud Computing [9] Assigns task with maximum executing time to resource produces minimum completion time Smaller tasks has to wait for a long time to execute
- 3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 03 Special Issue: 07 | May-2014, Available @ http://www.ijret.org 46 7. Comparison of Load Balancing Algorithms in a Cloud [10] Cloud Manager receives all requests & places in a queue then check for the availability and allocate to VM Maintaining all requests by a Cloud Manager creates a bottleneck problem 8. Availability and Load Balancing in Cloud Computing [2] XMPP servers & XMPP clients communicate using MOM. XMPP clients sends its current status information to XMPP servers Need to improve MOM for Load Balancing 9. Energy Efficient Virtual Machine Provision Algorithm for Cloud System [11] Proposes Dynamic Round Robin Algorithm which uses 2 rules to consolidate Virtual Machines Does not scale up for large data centers The Table.1 shows the comparison of different existing Load Balancing techniques and their limitations. Each technique has some limitation. Hence, the new approach need to be developed that enhances the Cloud Environment in all aspects. 4. PROPOSED WORK All the Load Balancing Algorithms are applied to the entire Cloud Environment. Hence, it is difficult to manage the large Cloud Environment. Dynamic Load Balancing algorithms distribute the workload by checking the status of each server which is maintained by a table called Status table. Maintaining Status table for all the servers is a difficult one and it may not be updated periodically hence it may not be consistent one. The proposed work is to apply Load Balancing algorithms only for the part of the cloud servers and not to the entire Cloud Environment. So that, the overhead of maintaining cloud servers is reduced. To distribute the workload in the partitioned Cloud Environment, two main components are needed [4]. They are, 1. Main Controller 2. Load Balancer Before discussing about these components, the method for partitioning the cloud environment is to be known. The entire cloud environment is divided into partitions based on its geographical locations. Data Centers are reside in the country and each country has its own and unique co-ordinate value. Based on this co-ordinate value, the Cloud Environment is divided into partitions. The co-ordinate values are represented as 35O 41''22.22' N 139O 41''30.12' E. Here, the 3 numeric values represents the degree, minutes and seconds values and it also represents the four directions East, West, North and South. The Cloud is divided into four partitions as depicted in Fig.1. Partition-I includes the data centers that are reside in the North-West direction. Partition II includes the datacenters that are resided in the North-East direction. Partition III includes the data centers that are reside in the South-West area and Partition IV includes the data centers that reside in the South- East area. For Example, the co-ordinate value of Tokyo is 35O 41''22.22' N 139O 41''30.12' E which reside in the North- East direction, hence Tokyo belongs to the Partition-II. In the representation of co-ordinate, it has three parts as degree, minutes and seconds. Fig 1 Cloud Partitions End users from different locations submit their jobs to the Cloud Environment. All these jobs are received by a Main Controller which is a single node to manage all the partitions. Nodes under each partition are managed by a Load Balancer. Main Controller distributes the jobs to the Load Balancer by checking its partition status. The partition may be in 3 states as
- 4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 03 Special Issue: 07 | May-2014, Available @ http://www.ijret.org 47 Idle, Normal and Overload states. The partition status is set by the Load Balancer based on the parameters as Number of CPUs, the CPU processing speeds, the available memory size, the memory utilization ratio, the CPU utilization ratio and network bandwidth etc., The jobs are received by the Load Balancers and the Load Balancing algorithms are applied to the partitions. Here the Switching Mechanism is applied. Switching Mechanism is the process of switching over to the 2 different algorithms according to the 2 different situations. Switching Mechanism contains two different algorithms, one simple algorithm for Idle state partitions and another one effective algorithm for Normal state partitions. Round Robin is a simple and cheap algorithm that can be used for Idle state partitions. An effective algorithm for Normal state partition should prevent the partitions becoming overloaded state. 5. CONCLUSIONS AND FUTURE ENHANCEMENT: By partitioning method, the overhead in the management of cloud environment is reduced. Based on the co-ordinate values, Cloud Environment is divided into partitions. Cloud division rule [4] issue is resolved by using co-ordinate values for partitioning. For Idle state partitions, simple existing algorithm can be used but for Normal state partitions, a new algorithm is needed to be developed. In future, the concept is to be implemented using CloudSim tool and also to propose an effective algorithm that will work for Normal state partitions. REFERENCES [1] Ratan mishra & anant jaiswal, "ant colony optimization: a solution of load balancing in cloud", international journal OF web & semantic technology, VOL. 3, NO. 2, PP. 33-50, 2012. [2] Zenon chaczko, venkatesh mahadevan, shahrzad aslanzadeh AND christopher mcdermid, "availability and load balancing in cloud computing", international conference ON computer AND software modeling, VOL. 14, PP. 134-140, 2011. [3] Venubabu kunamneni, "dynamic load balancing for the cloud", international journal OF computer science AND electrical engineering, VOL. 1, NO. 1, PP. 33-37, 2012. [4] Gaochao xu, junjie pang & xiaodong fu, “a load balancing model based on cloud partitioning for public cloud ”, ieee transactions ON cloud computing, VOL. 18, NO. 1, PP. 34-39, 2013. [5] Shreyas mulay & sanjay jain, “enhanced equally distributed load balancing algorithm for cloud computing”, international journal OF research IN engineering AND technology, VOL. 02, NO. 06, PP. 976-980, 2013. [6] Mohana priya.s & subramani.b, "a new approach for load balancing in cloud computing", international journal OF engineering and computer science, VOL. 2, NO. 5, PP. 1636-1640, 2013. [7] Jeffrey m. Galloway, karl l. Smith & susan s. Vrbsky(2012), "power aware load balancing for cloud computing", international journal OF science AND research, VOL. 1, 2012. [8] Jasmin james & dr. Bhupendra verma, "efficient vm load balancing algorithm for cloud computing environment", international journal OF computer science AND engineering, VOL. 4, NO. 09, PP. 1658- 1663, 2012. [9] Elzeki.o.m, reshad.m.z & elsoud.m.a, "improved max- min algorithm in cloud computing", international journal OF computer applications, VOL. 50, NO. 12, PP. 22-27, 2012. [10] Jaspreet KAUR, "comparison of load balancing algorithms in a cloud", SINTERNATIONAL journal OF engineering research AND applications, VOL. 2, NO. 3, PP. 1169-1173, 2012. [11] Ching-chi lin, pangfeng liu & jan-jan wu, "energy- efficient virtual machine provision algorithm for cloud system", ieee 4TH international conference ON cloud computing, PP. 81-88, 2011.