![International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 4 Issue 1, December 2019 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD29556 | Volume – 4 | Issue – 1 | November-December 2019 Page 322
Virtual Machine Migration and Allocation in
Cloud Computing: A Review
Khushbu Singh Chandel1, Dr. Avinash Sharma2
1Research Scholar, 2Head and Professor,
1,2Department of CSE, MITS, Bhopal, Madhya Pradesh, India
ABSTRACT
Cloud computing is an emerging computing technology that maintains
computational resources on large data centers and accessedthroughinternet,
rather than on local computers. VM migration provides the capability to
balance the load, system maintenance, etc. Virtualization technology gives
power to cloud computing. The virtual machine migration techniques can be
divided into two categories that is pre-copy and post-copy approach. The
process to move running applications or VMs from one physical machine to
another is known as VM migration. In migration process the processor state,
storage, memory and network connection are moved from one host to
another.. Two important performance metrics are downtime and total
migration time that the users care about most, because these metrics deals
with service degradation and the time during which the serviceisunavailable.
This paper focus on the analysis of live VM migration Techniques in cloud
computing.
KEYWORDS: Cloud Computing, Virtualization, Virtual Machine, Live Virtual
Machine Migration
How to cite this paper: Khushbu Singh
Chandel | Dr. Avinash Sharma "Virtual
Machine Migration and Allocation in
Cloud Computing: A
Review" Publishedin
International Journal
of Trend in Scientific
Research and
Development(ijtsrd),
ISSN: 2456-6470,
Volume-4 | Issue-1,
December 2019, pp.322-325, URL:
https://www.ijtsrd.com/papers/ijtsrd29
556.pdf
Copyright © 2019 by author(s) and
International Journal ofTrendinScientific
Research and Development Journal. This
is an Open Access article distributed
under the terms of
the Creative
CommonsAttribution
License (CC BY 4.0)
(http://creativecommons.org/licenses/by
/4.0)
1. INRODUCTION
Cloud environment is latest scenario in IT industry. It
indicates a computer model where users are provided with
computing resources. These services includethreepartslike
as Software as a Service, Platform as a Service and
Infrastructure as a Service. Figure 1 shows the relationship
of these services.
Figure 1: Services in cloud computing
IaaS locates in bottom scale of cloud systems and it provides
virtualized possessions such as storage, bandwidth and
memory etc. PaaS provides a higher level of IaaS to create a
cloud securely programmable. SaaS is a software delivery
model [1]. As the importance of cloud computing is growing
bigger and bigger, there are many researches are beginning.
It is important to simulate the presentation of cloud system.
However, there are numerous factors of a cloud
infrastructure such as a hardware, software and services.
Therefore, it is hard to quantify the presentation of cloud
system.
Scheduling is the most efficient tasks that perform in the
cloud computing environment. To improve the efficiency of
the task load of cloud scenario, scheduling is most of the
works performed to obtain maximum benefits. The main
objective of the scheduling proceduresincloudscenarioisto
accept the resources properly while maintaining loads
among the resources so that to get the least execution time.
Figure 2: Types of Scheduling
2. RELATED WORK
Following are the scheduling procedures that are
implemented in cloud.
IJTSRD29556](https://image.slidesharecdn.com/66virtualmachinemigrationandallocationincloudcomputingareview-200107122248/85/Virtual-Machine-Migration-and-Allocation-in-Cloud-Computing-A-Review-1-320.jpg)
![International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD29556 | Volume – 4 | Issue – 1 | November-December 2019 Page 323
Scheduling procedure based onQoS[2]:Inthistechnique,the
concerned procedure is based on quality of service. It
evaluates the priority of works on the basis of multiple
elements of tasks and after that perform sorting on works
onto examine which can further complete the works.
User-precedence min-min scheduling procedure [3]: In this
technique, an improvedloadbalancedprocedureislaunched
on a basis of Min-min procedure insequencetominimize the
make span and get the most out of the consumption of
resource.
Improved value based procedure [4]: This procedure
increase the general value-based scheduling procedure for
creating suitable mapping of works to resources. It merged
works as per the processing ability of on hand possessions.
Optimized movement based costing procedure [5]: In this
procedure, experimentation of the optimized procedure is
contrast with the general task scheduling procedure. The
main objective of this optimized procedure istoobtain more
benefits as compare to the general general task scheduling
procedure.
Preempt table shortest task next procedure [6]: This
procedure is support in a private cloud. In this paper they
merge the pre-emption approach of Round-robinprocedure
with shortest task next. This procedure gives cost profit and
increase the response duration and execution duration.
Shortest task scheduling [7]: This procedures is approved in
a public cloud scenario. In this paper contains the
distribution of resources on multiplecloudsunderover-load
and under-load situation.
There are many numbers of procedures that are already
practical neither in a private cloud scenario nor in a hybrid
cloud scenario.
3. VIRTUALIZATION IN CLOUD COMPUTING
Virtualization is a framework ormethodologyofdividing the
resources of a computer into multiple execution
environments, by applying one or more concepts or (VM)
and technologies such as time-sharing, hardware and
software partitioning, partial or complete machine
simulation or emulation, quality of service, and many others
[4]. The approach of virtualization which empowers to
computing resources of a solitary physical machine (PM)
among various virtual machines (VM) ensuring execution
detachment, made ready for compelling and productive
resource utilization and management.
Figure 3: Virtualization
In a virtualized datacentre, every application segment
(server) of an enterprise customerapplicationisexemplified
in a virtual machine (VM) and a solitary physical machine
(PM) has different VMs. Virtualization gives an approach to
change resources assignedtoVMsdynamically(VMresizing)
and moving VMs starting with one physical machine then
onto the next. Furnished with an intelligent situation of
virtual machines on physical machines, this permits us to
take after the workload progressions of applications
consequently empowering successful use of resources.
Virtualization could be attained at the different type of
levels. The Sorts of virtualizations are Server Virtualization,
Storage Virtualization, Operating framework Virtualization
and Network Virtualization, System Virtualization is the
point at which a solitary physical host runs various VMs on
it. This VM has it applications that run on its OS (guest OS).
For the client, a VM carries on much the same as an
autonomous physical machine.
4. TOOLS RELATED TO VIRTUALIZATION IN CLOUD
There are various cloud computing tool can be used for
implement scheduling task.
A. CLOUDMIGXPRESS
CloudMIG Xpress addresses those types of challenges and
supports method provide for the evaluationandpreparation
phases to move around software techniques to PaaS orIaaS-
based clouds scenario. It supplies from a rationally model
and is make to provide research in cloud immigration. The
basic characteristics are as follows:
Extract code prototypes from jdk-based software
Reproduce many cloud deployment options
Compare the trade-offs
Evaluate future values, response times, and SLA
violations
Model the current technique deployment
Create artificial workload profiles
Model cloud scenarios with the help of cloud profiles
Model cloud atmosphere constraints
Perform a static analysis to detect cloud violations
Compare the suitability of different cloud profiles
Graph-based visualization of searched cloud violations
B. CLOUDSIM
CloudSim is an extensible simulation model that provides
prototyping and imitationofCloudcomputingtechniqueand
application provisioning atmosphere. The CloudSim
simulator provides both system and activities modeling of
clouds mechanism like as information centers, virtual
machines and resource provisioning rules. It experiments
generic application provisioning methods that can be
elaborated with simplicity and limited attempt. Currently, it
provides prototyping and simulation of cloud atmosphere
including of both unit and inter-networked cloud system.
Moreover, it showstypical interfacesfor experimentingrules
and provisioning approaches for allocation of virtual
machines belongs to inter-networked cloud systems. Many
researchers from organizations like as HP laboratory in US
are using CloudSim in their examination on cloud supply
provisioning and energy well-organized organization of
information center possessions. The convenience of
CloudSim is introduced by a case study consisting dynamic
condition of application services in the mixed federated
clouds atmosphere. The conclusions of this case studyprove
that the cloud computing scenario efficiently increases the](https://image.slidesharecdn.com/66virtualmachinemigrationandallocationincloudcomputingareview-200107122248/85/Virtual-Machine-Migration-and-Allocation-in-Cloud-Computing-A-Review-2-320.jpg)
![International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD29556 | Volume – 4 | Issue – 1 | November-December 2019 Page 324
application QoS requirements under swinging supply and
service insist patterns.
C. ICANCLOUD
Basically iCanCloud is a simulation place aimed toprototype
and simulates cloud computing approaches, which is
objected to those programmers who deal nearly with those
types of systems. The main objective of iCanCloud is to
assume the trade-offs between cost and effective
performance of a given set of applications performed in a
specific hardware and then support to programmers useful
data about such values. Therefore, iCanCloud can be usedby
a wide range of programmers and users, from general active
users to developers of more distributed applications. The
most desirable characteristics of the iCanCloud simulation
place consists the following:
Both existing and non-existing cloud architectures can
be prototyped and simulated.
A more flexible cloud hypervisor function supports an
easy technique for integrating and testing both newand
previous cloud brokering rules.
Custom VMs can be used to fast simulate uni-
core/multi-core systems.
iCanCloud supports a wide area of configurations for
repository systems which consist prototypes for local
storage systems, isolated storage systems like NFS and
parallel repository systems like parallel systems and
RAID systems.
Some other cloud computing tool is as follows:
1. SIMCLOUD
2. REALCLOUDSIM
3. SIMCLOUD
4. VIMCLOUD
5. APACHE-ANT
5. COMPARISON OF REVIEW TECHNIQUES
Scheduling
Algorithm
Scheduling
Parameters
Objective Tool
Scheduling
Factors
Environment
PSJN Cost and time
Effective and fast
execution of task
Private
cloud
Group task
Cloud
environment
Shortest Job
scheduling
Arrival duration,
process duration,
time limit and I/O
requirement
Effective resource
allocation under
defined parameters
MATLAB Group task
Cloud
environment
Optimized ABC
Algorithm
Cost, profit and
priority
Measure the cost and
performance more
accurately
SimGrid Array of task
Cloud
environment
Improved Cost
Based algorithm
Cost and task
grouping
Minimizing the cost
and completion time
Cloudsim Group task
Cloud
Environment
User-Priority
Guided Min-
Min scheduling
Algorithm
Makespan
To promised the
guarantee regarded the
provided resources.
MATLAB
Independent
task
Cloud
environment
Ant Algorithm
Pheromone
updating rule
Enhance the
performance of basic
ACO
Cloudsim
Independent
task
Cloud
environment
MACO
Pheromone
updating rule
Improve the
performance of grid
system
Gridsim
Independent
jobs
Grid
environmen
ACO for
scheduling data
intensive
application
Cost and time
Improves the
efficiency and
reliability in all
conditions
Gridsim Group task
Grid
environment
6. CONCLUSIONS
This paper presents a review of various live virtual machine
migration techniques in cloud computing. The live virtual
machine migration techniques can be broadly divided into
two categories that is pre-copyandpost-copyapproach.Few
techniques proposed by researchers other than these two
approaches are also discussed. The paper also discussedVM
migration techniques for cloudfederation.All thetechniques
discussed above try to minimize the total downtime of
migration and provide better performanceinlowbandwidth
and the memory reusing mechanism for VM consolidation
[16] reduces the amount of transferred memory and also
reduce total migration time. We present that the migration
approach which is used by the previous researchersisbased
on the past performance of the datacentres.
7. REFERENCES
[1] Rehana Begam, Hamidreza Moradi, Wei Wang and
Dakai Zhu, "Flexible VM Provisioning for Time-
Sensitive Applications with Multiple Execution
Options", IEEE IEEE 11th International Conference on
Cloud Computing, 2018.
[2] Hongyan Cui,1,2 Xiaofei Liu,1 Tao Yu,3 Honggang
Zhang,4 Yajun Fang,5 and Zongguo Xia, “Cloud Service
Scheduling Algorithm Research and Optimization”,
Hindawi Publishing Corporation Security and
Communication Networks Volume 2017.
[3] Shameer A.P and Dr. A C Subhajini, “Study on Different
Scheduling Algorithm for Cloud Computing”,
International Journal of Advanced Research in](https://image.slidesharecdn.com/66virtualmachinemigrationandallocationincloudcomputingareview-200107122248/85/Virtual-Machine-Migration-and-Allocation-in-Cloud-Computing-A-Review-3-320.jpg)
![International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD29556 | Volume – 4 | Issue – 1 | November-December 2019 Page 325
Computer ScienceandSoftwareEngineering,Volume6,
Issue 5, May 2016.
[4] Abdul Razaque, Nikhileshwara Reddy Vennapusa,
Nisargkumar Soni, Guna Sree Janapati and khilesh
Reddy Vangala, “Task Scheduling in CloudComputing”,
IJIER, 2016.
[5] Seema Vahora and RiteshPatel,“CloudSim-ASurveyon
VM Management Techniques”, International Journal of
Advanced Research in Computer and Communication
Engineering Vol. 4, Issue 1, January 2015.
[6] Yue Miao, Fu Rao and Luo Yu, “Research on the
Resource Scheduling of the Improved SFLA in Cloud
Computing”, International Journal of Grid Distribution
Computing Vol.8, No.1, 2015.
[7] Mandeep Kaur, Sugandha Sharma and Rajinder Kaur,
“Optimization of Job Scheduling in Cloud Computing
Environment”, International Journal of Advanced
Research in Computer Science and Software
Engineering, Volume 4, Issue 7, July 2014.
[8] Sumit Arora and Sami Anand, “Improved Task
Scheduling Algorithm in Cloud Environment”,
International Journal ofComputerApplications(0975–
8887) Volume 96– No.3, June 2014.
[9] Gabriele D’Angelo, “Parallel andDistributedSimulation
from Many Cores to the Public Cloud”, International
Conference on High Performance Computing and
Simulation, 2014.
[10] Dr. Amit Agarwal and Saloni Jain, “Efficient Optimal
Algorithm of Task Scheduling in Cloud Computing
Environment”, International Journal of Computer
Trends and Technology (IJCTT) – volume 9 number 7–
Mar 2014.
[11] M. A. Rodriguez and R. Buyya, “Deadline based
resource provisioning and scheduling algorithm for
scientific workflows on clouds,” IEEE Trans. Cloud
Comput., vol. 2, no. 2, pp. 222–235, Apr. 2014.
[12] Xiaocheng Liu, Chen Wang, Bing Bing Zhou, Junliang
Chen, Ting Yang and Albert Y. Zomaya, “Priority-Based
Consolidation of Parallel WorkloadsintheCloud”,IEEE
Transactions on Parallel and Distributed Systems, Vol.
24, No. 9, Sep 2013.
[13] Z. Wu, X. Liu, Z. Ni, D. Yuan, and Y. Yang, “A market-
oriented hierarchical scheduling strategy incloud
workflow systems,” J. Supercomputing, vol. 63, no. 1,
pp. 256–293, Jan. 2013.
[14] H. M. Fard, R. Prodan, and T. Fahringer, “A truthful
dynamic workflow scheduling mechanism for
commercial multicloud environments”, IEEE Trans
Parallel and Distrib. Syst., vol. 24, no. 6,pp.1203–1212,
June 2013.
[15] Z. Xiao, W. Song, and Q. Chen, “Dynamic resource
allocation using virtual machines for cloud computing
environment,” IEEE Trans. Parallel and Distrib. Sys.,
vol. 24, no. 6, pp. 1107–1117, June 2013.](https://image.slidesharecdn.com/66virtualmachinemigrationandallocationincloudcomputingareview-200107122248/85/Virtual-Machine-Migration-and-Allocation-in-Cloud-Computing-A-Review-4-320.jpg)
Virtual Machine Migration and Allocation in Cloud Computing: A Review
- 1. International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 4 Issue 1, December 2019 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD | Unique Paper ID – IJTSRD29556 | Volume – 4 | Issue – 1 | November-December 2019 Page 322 Virtual Machine Migration and Allocation in Cloud Computing: A Review Khushbu Singh Chandel1, Dr. Avinash Sharma2 1Research Scholar, 2Head and Professor, 1,2Department of CSE, MITS, Bhopal, Madhya Pradesh, India ABSTRACT Cloud computing is an emerging computing technology that maintains computational resources on large data centers and accessedthroughinternet, rather than on local computers. VM migration provides the capability to balance the load, system maintenance, etc. Virtualization technology gives power to cloud computing. The virtual machine migration techniques can be divided into two categories that is pre-copy and post-copy approach. The process to move running applications or VMs from one physical machine to another is known as VM migration. In migration process the processor state, storage, memory and network connection are moved from one host to another.. Two important performance metrics are downtime and total migration time that the users care about most, because these metrics deals with service degradation and the time during which the serviceisunavailable. This paper focus on the analysis of live VM migration Techniques in cloud computing. KEYWORDS: Cloud Computing, Virtualization, Virtual Machine, Live Virtual Machine Migration How to cite this paper: Khushbu Singh Chandel | Dr. Avinash Sharma "Virtual Machine Migration and Allocation in Cloud Computing: A Review" Publishedin International Journal of Trend in Scientific Research and Development(ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1, December 2019, pp.322-325, URL: https://www.ijtsrd.com/papers/ijtsrd29 556.pdf Copyright © 2019 by author(s) and International Journal ofTrendinScientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (CC BY 4.0) (http://creativecommons.org/licenses/by /4.0) 1. INRODUCTION Cloud environment is latest scenario in IT industry. It indicates a computer model where users are provided with computing resources. These services includethreepartslike as Software as a Service, Platform as a Service and Infrastructure as a Service. Figure 1 shows the relationship of these services. Figure 1: Services in cloud computing IaaS locates in bottom scale of cloud systems and it provides virtualized possessions such as storage, bandwidth and memory etc. PaaS provides a higher level of IaaS to create a cloud securely programmable. SaaS is a software delivery model [1]. As the importance of cloud computing is growing bigger and bigger, there are many researches are beginning. It is important to simulate the presentation of cloud system. However, there are numerous factors of a cloud infrastructure such as a hardware, software and services. Therefore, it is hard to quantify the presentation of cloud system. Scheduling is the most efficient tasks that perform in the cloud computing environment. To improve the efficiency of the task load of cloud scenario, scheduling is most of the works performed to obtain maximum benefits. The main objective of the scheduling proceduresincloudscenarioisto accept the resources properly while maintaining loads among the resources so that to get the least execution time. Figure 2: Types of Scheduling 2. RELATED WORK Following are the scheduling procedures that are implemented in cloud. IJTSRD29556
- 2. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD29556 | Volume – 4 | Issue – 1 | November-December 2019 Page 323 Scheduling procedure based onQoS[2]:Inthistechnique,the concerned procedure is based on quality of service. It evaluates the priority of works on the basis of multiple elements of tasks and after that perform sorting on works onto examine which can further complete the works. User-precedence min-min scheduling procedure [3]: In this technique, an improvedloadbalancedprocedureislaunched on a basis of Min-min procedure insequencetominimize the make span and get the most out of the consumption of resource. Improved value based procedure [4]: This procedure increase the general value-based scheduling procedure for creating suitable mapping of works to resources. It merged works as per the processing ability of on hand possessions. Optimized movement based costing procedure [5]: In this procedure, experimentation of the optimized procedure is contrast with the general task scheduling procedure. The main objective of this optimized procedure istoobtain more benefits as compare to the general general task scheduling procedure. Preempt table shortest task next procedure [6]: This procedure is support in a private cloud. In this paper they merge the pre-emption approach of Round-robinprocedure with shortest task next. This procedure gives cost profit and increase the response duration and execution duration. Shortest task scheduling [7]: This procedures is approved in a public cloud scenario. In this paper contains the distribution of resources on multiplecloudsunderover-load and under-load situation. There are many numbers of procedures that are already practical neither in a private cloud scenario nor in a hybrid cloud scenario. 3. VIRTUALIZATION IN CLOUD COMPUTING Virtualization is a framework ormethodologyofdividing the resources of a computer into multiple execution environments, by applying one or more concepts or (VM) and technologies such as time-sharing, hardware and software partitioning, partial or complete machine simulation or emulation, quality of service, and many others [4]. The approach of virtualization which empowers to computing resources of a solitary physical machine (PM) among various virtual machines (VM) ensuring execution detachment, made ready for compelling and productive resource utilization and management. Figure 3: Virtualization In a virtualized datacentre, every application segment (server) of an enterprise customerapplicationisexemplified in a virtual machine (VM) and a solitary physical machine (PM) has different VMs. Virtualization gives an approach to change resources assignedtoVMsdynamically(VMresizing) and moving VMs starting with one physical machine then onto the next. Furnished with an intelligent situation of virtual machines on physical machines, this permits us to take after the workload progressions of applications consequently empowering successful use of resources. Virtualization could be attained at the different type of levels. The Sorts of virtualizations are Server Virtualization, Storage Virtualization, Operating framework Virtualization and Network Virtualization, System Virtualization is the point at which a solitary physical host runs various VMs on it. This VM has it applications that run on its OS (guest OS). For the client, a VM carries on much the same as an autonomous physical machine. 4. TOOLS RELATED TO VIRTUALIZATION IN CLOUD There are various cloud computing tool can be used for implement scheduling task. A. CLOUDMIGXPRESS CloudMIG Xpress addresses those types of challenges and supports method provide for the evaluationandpreparation phases to move around software techniques to PaaS orIaaS- based clouds scenario. It supplies from a rationally model and is make to provide research in cloud immigration. The basic characteristics are as follows: Extract code prototypes from jdk-based software Reproduce many cloud deployment options Compare the trade-offs Evaluate future values, response times, and SLA violations Model the current technique deployment Create artificial workload profiles Model cloud scenarios with the help of cloud profiles Model cloud atmosphere constraints Perform a static analysis to detect cloud violations Compare the suitability of different cloud profiles Graph-based visualization of searched cloud violations B. CLOUDSIM CloudSim is an extensible simulation model that provides prototyping and imitationofCloudcomputingtechniqueand application provisioning atmosphere. The CloudSim simulator provides both system and activities modeling of clouds mechanism like as information centers, virtual machines and resource provisioning rules. It experiments generic application provisioning methods that can be elaborated with simplicity and limited attempt. Currently, it provides prototyping and simulation of cloud atmosphere including of both unit and inter-networked cloud system. Moreover, it showstypical interfacesfor experimentingrules and provisioning approaches for allocation of virtual machines belongs to inter-networked cloud systems. Many researchers from organizations like as HP laboratory in US are using CloudSim in their examination on cloud supply provisioning and energy well-organized organization of information center possessions. The convenience of CloudSim is introduced by a case study consisting dynamic condition of application services in the mixed federated clouds atmosphere. The conclusions of this case studyprove that the cloud computing scenario efficiently increases the
- 3. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD29556 | Volume – 4 | Issue – 1 | November-December 2019 Page 324 application QoS requirements under swinging supply and service insist patterns. C. ICANCLOUD Basically iCanCloud is a simulation place aimed toprototype and simulates cloud computing approaches, which is objected to those programmers who deal nearly with those types of systems. The main objective of iCanCloud is to assume the trade-offs between cost and effective performance of a given set of applications performed in a specific hardware and then support to programmers useful data about such values. Therefore, iCanCloud can be usedby a wide range of programmers and users, from general active users to developers of more distributed applications. The most desirable characteristics of the iCanCloud simulation place consists the following: Both existing and non-existing cloud architectures can be prototyped and simulated. A more flexible cloud hypervisor function supports an easy technique for integrating and testing both newand previous cloud brokering rules. Custom VMs can be used to fast simulate uni- core/multi-core systems. iCanCloud supports a wide area of configurations for repository systems which consist prototypes for local storage systems, isolated storage systems like NFS and parallel repository systems like parallel systems and RAID systems. Some other cloud computing tool is as follows: 1. SIMCLOUD 2. REALCLOUDSIM 3. SIMCLOUD 4. VIMCLOUD 5. APACHE-ANT 5. COMPARISON OF REVIEW TECHNIQUES Scheduling Algorithm Scheduling Parameters Objective Tool Scheduling Factors Environment PSJN Cost and time Effective and fast execution of task Private cloud Group task Cloud environment Shortest Job scheduling Arrival duration, process duration, time limit and I/O requirement Effective resource allocation under defined parameters MATLAB Group task Cloud environment Optimized ABC Algorithm Cost, profit and priority Measure the cost and performance more accurately SimGrid Array of task Cloud environment Improved Cost Based algorithm Cost and task grouping Minimizing the cost and completion time Cloudsim Group task Cloud Environment User-Priority Guided Min- Min scheduling Algorithm Makespan To promised the guarantee regarded the provided resources. MATLAB Independent task Cloud environment Ant Algorithm Pheromone updating rule Enhance the performance of basic ACO Cloudsim Independent task Cloud environment MACO Pheromone updating rule Improve the performance of grid system Gridsim Independent jobs Grid environmen ACO for scheduling data intensive application Cost and time Improves the efficiency and reliability in all conditions Gridsim Group task Grid environment 6. CONCLUSIONS This paper presents a review of various live virtual machine migration techniques in cloud computing. The live virtual machine migration techniques can be broadly divided into two categories that is pre-copyandpost-copyapproach.Few techniques proposed by researchers other than these two approaches are also discussed. The paper also discussedVM migration techniques for cloudfederation.All thetechniques discussed above try to minimize the total downtime of migration and provide better performanceinlowbandwidth and the memory reusing mechanism for VM consolidation [16] reduces the amount of transferred memory and also reduce total migration time. We present that the migration approach which is used by the previous researchersisbased on the past performance of the datacentres. 7. REFERENCES [1] Rehana Begam, Hamidreza Moradi, Wei Wang and Dakai Zhu, "Flexible VM Provisioning for Time- Sensitive Applications with Multiple Execution Options", IEEE IEEE 11th International Conference on Cloud Computing, 2018. [2] Hongyan Cui,1,2 Xiaofei Liu,1 Tao Yu,3 Honggang Zhang,4 Yajun Fang,5 and Zongguo Xia, “Cloud Service Scheduling Algorithm Research and Optimization”, Hindawi Publishing Corporation Security and Communication Networks Volume 2017. [3] Shameer A.P and Dr. A C Subhajini, “Study on Different Scheduling Algorithm for Cloud Computing”, International Journal of Advanced Research in
- 4. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD29556 | Volume – 4 | Issue – 1 | November-December 2019 Page 325 Computer ScienceandSoftwareEngineering,Volume6, Issue 5, May 2016. [4] Abdul Razaque, Nikhileshwara Reddy Vennapusa, Nisargkumar Soni, Guna Sree Janapati and khilesh Reddy Vangala, “Task Scheduling in CloudComputing”, IJIER, 2016. [5] Seema Vahora and RiteshPatel,“CloudSim-ASurveyon VM Management Techniques”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 1, January 2015. [6] Yue Miao, Fu Rao and Luo Yu, “Research on the Resource Scheduling of the Improved SFLA in Cloud Computing”, International Journal of Grid Distribution Computing Vol.8, No.1, 2015. [7] Mandeep Kaur, Sugandha Sharma and Rajinder Kaur, “Optimization of Job Scheduling in Cloud Computing Environment”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 7, July 2014. [8] Sumit Arora and Sami Anand, “Improved Task Scheduling Algorithm in Cloud Environment”, International Journal ofComputerApplications(0975– 8887) Volume 96– No.3, June 2014. [9] Gabriele D’Angelo, “Parallel andDistributedSimulation from Many Cores to the Public Cloud”, International Conference on High Performance Computing and Simulation, 2014. [10] Dr. Amit Agarwal and Saloni Jain, “Efficient Optimal Algorithm of Task Scheduling in Cloud Computing Environment”, International Journal of Computer Trends and Technology (IJCTT) – volume 9 number 7– Mar 2014. [11] M. A. Rodriguez and R. Buyya, “Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds,” IEEE Trans. Cloud Comput., vol. 2, no. 2, pp. 222–235, Apr. 2014. [12] Xiaocheng Liu, Chen Wang, Bing Bing Zhou, Junliang Chen, Ting Yang and Albert Y. Zomaya, “Priority-Based Consolidation of Parallel WorkloadsintheCloud”,IEEE Transactions on Parallel and Distributed Systems, Vol. 24, No. 9, Sep 2013. [13] Z. Wu, X. Liu, Z. Ni, D. Yuan, and Y. Yang, “A market- oriented hierarchical scheduling strategy incloud workflow systems,” J. Supercomputing, vol. 63, no. 1, pp. 256–293, Jan. 2013. [14] H. M. Fard, R. Prodan, and T. Fahringer, “A truthful dynamic workflow scheduling mechanism for commercial multicloud environments”, IEEE Trans Parallel and Distrib. Syst., vol. 24, no. 6,pp.1203–1212, June 2013. [15] Z. Xiao, W. Song, and Q. Chen, “Dynamic resource allocation using virtual machines for cloud computing environment,” IEEE Trans. Parallel and Distrib. Sys., vol. 24, no. 6, pp. 1107–1117, June 2013.