A Review of Virtual Machine Allocation and Migration Techniques in Cloud Computing
1Loveleena Mukhija, 2Dr.Rohit Sachdeva
1Assistant Professor,PMN College
2Assistant Professor, MM Modi College
Abstract
Efficacious allocation of virtual machine (VM) and their migration contributes significantly in optimal amalgamation of servers. Virtual machine allocation is in fact allotment of collection of support systems known as virtual machines to the structure of tangible devices hosts or the physical machines positioned in the datacenter. This paper surveys the intended techniques suggested by the different authors for allocation and migration of virtual machine to render an energy efficient cloud computing. The core purpose of this survey is to explore and review the various techniques for VM allocation and migration for energy efficient data center architecture.
Keywords: Virtual machine allocation, Virtual machine migration, Server Consolidation 1. INTRODUCTION
It is necessitate to have data centers which employ integration of servers which can significantly minimize the considerable variety of physical machines which are operating or hosts in the data center. Cloud Computing has been set in the form of a model which offers reckoning resources for the user on pay per use basis by vigorously setting up the resources to accommodate distinct needs of the users workload .And it is all done feasible through the making use of vital technique virtualization, in which virtual machines are created which shares the physical resources of the data center. It is one of the extensively used technique in cloud environment in which multiple virtual machines are assigned to single server helping in dropping numerous cost of like hardware and many operating costs. So there is need to allocate virtual machine and in fact after that to migrate the virtual machine considering elasticity feature in resources of cloud.In this paper, we examined wide variety of algorithms and techniques for virtual machine allocation and migration techniques for Cloud Computing. and survey the literature for assessing these diverse techniques and identify the performance criteria’s. This paper is organized in the following sections. Section I provides a concise inception of allocation of Virtual Machine andVirtual Machine Migration. TheSection II outlines certain associated proceedings on VM allocation and migration techniques. Comparative analysis of both methods are observed in Section III and IV. We inferred from our study in section V with forthcoming path.
BACKGROUND
Virtualization:Virtualization is a technological advancement in which virtual instances of computer system are run on actual underlying hardware or software. Virtualization offers tremendous benefits like managing workloads and saving cost for more servers or workstations.
Virtual Machine (VM):A virtual machine (VM) is a software which besides functioning as a disparate PC, yet is moreover suitable for performing chores comprising of running different applications analogous to the core PC. A virtual machine, is therein employed atthe heart of data center also referred as a "host". Numerous supportive entities which are virtual machines can subsist within a individual physical machine or host at once.
Server Consolidation:It is the one of the technique in which multiple virtual machines are engaged on limited number of physical machines or resources which in turn prevents wastage of resources, saves energy and money and improves the availability of the system.
ISSUES WITH SERVER CONSOLIDATION
Though consolidation offers enormous benefits but there exist someissues with the consolidation of server.
1. Increased execution time due to suspended severs.As suspension of servers can increase the effective execution time which in fact leads to reduction in energy savings.
2. Overhead of Continuous live migration.
3. Another overhead of prediction of energy requirement of the servers which requires complicated techniques.
4. The major concern is to have effective server consolidation is NP –Hard problem Virtual Machine Allocation: The virtual machine allotment or allocation to thephysical machine is one of the major concernfor cloud. To complete provided number of tasks or jobs like storage or processing or retrieval of data, virtual machines are allocated to the physical machines .Physical machines are the processing units of cloud. So these physical machines are provided with support system machines called as virtual machines. The assignment or allocation of virtual machine to the best fit physical machine will help in effective server consolidation, also it is considered as NP-Hard problem.For consolidation of server multiple virtual machines are allocated and left ones physical machines are turned off which influences energy efficiency of cloud.VM allocation is termed as a mapping phase, involving mapping of VM to PM. This is also known as VM placement.
Some optimization objective always revolve around VM to PM mapping problem like reducing consumption of energy ,improved utilization of resource.Thus the aim of VM allocation is determining the most optimal selection of PM for allocating the given VM.
Virtual Machine Migration:The placement of VM to PM is considered as a continuous process which give rise to another major concern relating to data center, VM migration .VM migration is situation which can be invoked subject to some objectives and it allows guest operating system or support system which are the Virtual Machines so as to migrate or drift them from one physical server to further. This is also termed as VM reallocation.
Virtual machine migration favors in achieving some of objectives as for instance
Better Power Management
Sharing Of Resources
Efficient Load Balancing
Fault Tolerance
Optimized System Maintenance
Mobile computing
1.1 Virtual Machine Allocation Algorithms
Allocation of these assisting Virtual Machines to the actual physical machine or host is also called as mapping phase as it involves mapping from virtual machine to physical machine. This mapping phase itself involves two mapping phases
Users applications are mapped to VM
VM’s are mapped onto PM’s
Users applications are mapped to virtual machines: are handled by VM configuration Manager. VM configuration manger deals with the challenges concerning to VM allocation such as number of virtual machines to be allocated and capacity(size).
VM’s are mapped onto PM’s it isalso termed as placement of the virtual machine.The goal of mapping phase is to associate VM onto PM depending on some optimality criteria as an objective and by considering different technique.And this mapping is through going process .VM allocation is the approach of employing the best optimal PM for the available given VM. So an allocation algorithm for virtual machine intends to find the best fitting of VM to the PM mapping be it for initiatory VM allocation or for all over again which is termed as VM migration.
VM allocation algorithm can be classified based on two types:
Figure 1.Classification Of VM Allocation Algorithms
Classification of VM Allocation Algorithms
1) Goal Based: According to the objectiveof allocation, the algorithm for virtual machine can be broadly classified into two forms:
a. Based on Power Consumption: Aims to acquire mapping of VM onto PM considering minimize consumption of power so as to have energy efficiency.
b.Based on QOS parameters:Aims to obtain the mapping of VM onto PM considering maximal attainment of the quality of service parameters.
2) Approach Based:According to the type of the foremost approach which isacquainted to accomplish a desirable mapping of VM onto PM, the allocation techniques of virtual machines are primarily categorized as under:
a.Constraint Programming:To solve problem of optimal placement of VM this technique uses logic based programming rather than mathematical way.
b. Bin Packing:Consider VM as items to be loaded in least number of bins which are considered as tangible physical machines.
c. Stochastic Integer Programming:To solve problem of optimal placement of VM this technique uses mathematical technique like probability to ascertain future demands for VM .
Virtual Machine Allocation Algorithms
Goal Based Approach Based
Constraint Programming
Bin Packing
Stochastic Integer Programming
Genetic
G e n e t i c
b a s e d
Based on Power Consumption
QOS based
d. Genetic Algorithm:Employ genetic based techniques which are part of evolutionary algorithms in which solution isnaturally selected from all possible solutions.
1.2. Virtual Machine Migration Algorithms
For achieving migration of virtual machine the fringe benefit of virtualization scheme is that it comes with a layer of software known as Virtual Machine Monitor (VMM also termed as hypervisor. It monitors and co-ordinate various tasks such as controlling of mapping of VMs on single platform or to check for under or overutilization of server a VM migration. This migration process contributes in realizing various optimal objectives.Classification of VM Migration Algorithms
There are two types of VM migrations: Non-live Migration or Live Migration.
Figure 2.Classification Of VM Migration Algorithms
1) Non-live migration: In this type of migration during the operation of VM migration all the applications which were functioning on the virtual machine will be stopped.
2) Live migration: In this type of migration during the operation of VM migration all the applications which were functioning on the virtual machine will continue running without any interruption. Live migration refers to transparently transferring the virtual machines from the heart of data center that is from one physical server to another.The technique of virtual machine migration is found to be beneficial technique as it gives manifold benefits like enabling the systems more energy efficient. The migration of Virtual Machine could be done by using different algorithm likeRound Robin,first fit, Monte Carlo etc. Also some of the migration techniques are based on biological inspired algorithms like firefly based or inspired on the behavior of locusts. Some techniques are also based on resource utilization. Furthermore, there are numerous objectives which revolves around the migration of virtual machines like management of power, balancing of load and maintenance of system. With the management of power few servers which are underutilized will be power off ensure power saving. Moreover, migration helps in achieving balancing of load so as by migrating virtual machine from a heavy load host to an another host with lower load.Apart from that migration with the intend of system maintenance will upgrade the accessibility and credibility of the system.
II. EXISTING WORK ON VM ALLOCATION/MIGRATION
Almeida et al. [2010]proposed a policy for scheduling considering QoS requirements and provider’s profit. They proposed configuration of VM and initial placement by following FCFS. .
Beloglazov and Buyya [2010b]proposed a method termed as Modified Best Fit Decreasing (MBFD) heuristic so as to arrange the available hosts by in decreasing order of their capacity of utilization ofresources and then mapping each VM to the PM accordingly but this allocation strategy caused in the slighter rise in the usage of power.
VM Migrations Algorithms
Live VM Migration Non Live VM
Migration
Berral et al. [2011]proposed,an ordered Best Fit algorithm which exhibits low execution time and reduction in execution time results in affinity-to-optimal solutions.
Paul et al. [2012]evaluated the performance impact of placement of VM and the contention of resources.
Li et al. [2013]classified the placement of virtual machine in two methods and they are direct placement and based on migration
Camati et al. [2014]proposed a method which is concerned more about maximizing the placement ratio rather than focusing on approaches for saving the energy.
Dong et al. [2015]evaluated that there is tradeoff in the consumption requirement of energy while schedulingvirtual machine.
Farahnakianet al. [2016]proposed an approach for consolidating virtual machines by taking into consideration both existing and subsequent requirement of utilization of resources. They proposed a regression model which estimates the utilization of physical machines which further resulted in improving the rate of consumption of energy, rate of VM migrations and reduced certain variety of Service Level Agreement (SLA) violations .
Li et al. [2016]developed an algorithm of consolidation and migration of virtual machineswhich wasbased on energy efficient model for multiple resources. This algorithm proposed a method of twofold threshold with multiresource utilization so as to invoke the migration of VMs. These are improved by introducing algorithms such as Modified Particle Swarm Optimization (MPSO) which provides efficient energy consumption in data centers [20]
Kansal et al. [2016]presented a virtual machine migration technique with focus on energy usage for cloud and is based on Firefly algorithm. The least active nodes share the load without affecting the performance of the cloud environment. The developed mechanism improved the load sharing and central balance among the virtual machines.
Mi et al. [2016]presented a genetic based algorithm Genetic Algorithm Based Approach (GABA) which worked on heterogeneous PMs following a dynamic automatic restructuring of VM reallocation. It searches online for finest solutions. To predict the varying workloads, a forecasting module known as request forecastingfor predicting the request is used. GABA resulted in reduction and preserving of power along with other optimization formultipleobjectives.
Ferdaus et al.[2016]used an effective biological inspired algorithm ACO (Ant Colony Optimization) metaheuristic for balancing the utilization of resources of cloud and specify the allocation problem for VM as a problem of category of NP-Hard Multi-Dimensional Vector Packing Problem (mDVPP).It enabled in minimal computation time.
Gao et al. [2016]proposed a modification ofAnt Colony System (ACS) algorithm focusing on minimizing the consumption of power and reducing wastage of resources.This focus on balancing of resources considering different aspects on the servers. This compound problem is chased as a multi-objective algorithm named VMPACS.
Deshpande et al. [2017]proposed a method which is hybrid in nature especially for the VM migration and that too of VM’s which are collocated and this is also concerned about traffic infact for reducing VM traffic and network contention traffic. Author considered traffic of particular network and this became the criteria for selection of migration technique.
Kurdi et al. [2018]proposed an algorithm, Locust Inspired scheduling algorithm (LACE) to allocate VM by noticing the behavior of the locusts. In this algorithm, the scheduling load is dispersed among the servers instead being centralized in one component. LACE was compared with previous scheduling algorithms and was found that former outperforms.
Das et al. [2019] proposed a learning based algorithm for allocation of Virtual Machines (VM) .To resolve geographical based queries by using geospatial query template in cloud platform. The main aim was to enhance the capability of the framework that serves geographic queries using real-time geospatial survey modeling methodology. It resulted in increase in waiting time initially as due to the fact these queries were novel for the system but subsequently it becomes easy after learning once for the system to assign VM for analogous sort of geospatial queries.
III. COMPARATIVE ANALYSIS
Table 1. Comparison Of VM Allocation Techniques
Author Objective Technique Remarks
Beloglazov and Buyya [2010b]
To arrange the given hostsin declining order of requirement of the utilization of resource.
A heuristic based technique is used known as Modified Best Fit Decreasing
This allocation strategy of each VM to the PM resulted in slighter rise in the consumption of power.
Shi and Hong [2011]
To places an unallocated VM by considering the foremost PM in the list of machines which are at the disposaland which is also satisfying the SLA and power constraints
A heuristic based technique is used known as First Fit heuristic to solve the multi-level
generalized assignment
problemefficiently.
It satisfy the SLA
and power
constraints
Mishra, M. and Sahoo, A., [2011]
To evaluate existing practices for VM allocation and to unveil their defects and their causes.
Technique of
consolidation of servers and balancing of load were considered.
Energy Saving
Camati, R.S., Calsavara, A. and Lima Jr, L., [2014]
To aim for
optimizing the ratio for VM placement by aiming at density
Maximizing placement ratio.
Placement ratio was considered rather than approaches foe energy saving .
placement as their main concern.
Dong, J., Wang, H.
and Cheng, S.,[
2015]
To reveal the
tradeoff in
consumption of
energy and
performance in IaaS cloud.
Scheduling Of VM There is tradeoff observed between
energy and
performance.
Jing V et al. [2016] To find best fit allocation possible
Correlation based VM allocation criteria . This technique uses some computational formula to find best fit allocation possible
A high
computation and proper formula to find best fit , time is
required to
allocation, utilization.
machine. complete all iteration.
T.P.et al.[2017] To resolve VM allocation
metaheuristic allocation/ migration
ACO (Ant Colony Optimization )is used
(1)The algorithm provides maximum resource allocation.
(2)It significantly reduced the job completion time Kurdi et al. [2018] To propose
scheduling
algorithm (LACE) to allocate VM by noticing the behavior of the locust
With this algorithm, the scheduling load rather having centralized load at a point it is infact dispersed among the servers.
LACE was
compared with previous scheduling algorithms and was found that former outperforms Mishra et al.
[2019]
Topropose an algorithm based on the notion of automata.
Proposed an
algorithm known as ALOLA algorithm, in which learning
automata was
compared without learning automata.
The developed algorithm secured resource allocation using the Cloud simulator. Also, the makespan of the developed system is evaluated for noticeable results.
IV. COMPARATIVE ANALYSIS
Table 2. Comparison Of VM Migration Techniques
Approach Objective Technique Remarks
Huang et al. [2011] To give the best performance of live migration by selecting suitable the the software and hardware
Comparison of performance of virtual live migration was evaluated .
Effectiveness of different migration environment was compared
environments Kikuchi et al[2011] To frame the model
for concurrent live migrations based on performance.
PRISM model was used to represent the performance
trait live
migrations.
Various
Quantifiable traits related to live migration
performance were inspected.
Wu and Zhao [2011] To forecast delay time of migration
Used Regression method to get performance
Migration latency is dependent on availability of resources
Liu et al.[2011] To numerically anticipate the Cost of energy consumption and performance of migration
To estimate migration energy Used
high-level linear model
Beyond
90%accuracy was there in forecasting of costs, migration cost was reduced beyond 72.9% and an energy saving of 73.6%
Cerroni, and
Callegati[2014]
To propose an edge network based on cloud.
To consider scheduling
alternatives like sequential and parallel migration strategies
Efficiency resulted in reduced total migration time ,service downtime
and reduced
requirement of network bandwidth Deshpande et al.
[2014]
To evaluate the source server timings of switching to the offline mode .
Scatter-Gather method was used to find eviction time.
Resulted in reduced eviction time for VM
Xu et al [2014] To find the cost and performance interference during
managing VM
migration.
iAware was
designed and implemened, to escape from violations of SLAs
Resulted in twofold benefit Load balancing and power saving without compromising on performance of application.
Zhang et al. [2014] To migrate memory and storage WAN
Proposed a model for responsive and adaptable
migration.
Resulted in
improved
adaptiveness and effectiveness for diverse applications over the WAN Deshpande and
Keahey [2017]
To Reduce in contention for traffic coming for migration and also for VM traffic
Presented an approach based on traffic for migration of VM’s which are co located.
Reduced time of migration and which resulted in lesser degradation of application.
V. CONCLUSION AND FUTURE WORK
In this paper, we examined wide variety of algorithms and techniques for virtual machine allocation and migration techniques for Cloud Computing. We discussed the concerns that must be tackled to provide themore optimal and efficient techniques. As our future work, we are scheduling to elevate existing VM algorithms for both allocation and migration to make it more suitable for services and applications for a versatile cloud environment.
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