scholarly journals Virtualization In Cloud Computing : A Review

2020 ◽  
pp. 01-05
Author(s):  
Ramandeep Kaur ◽  
Sumit Chopra
Keyword(s):  
Cloud Computing ◽  
Computer Technology ◽  
Virtual Machines ◽  
Virtual Machine Migration ◽  
Shared Resources ◽  
Computing Power ◽  
Service Oriented ◽  
Complete Survey ◽  
The Cost ◽  
Generation Technology

Cloud computing is one of the well developing fields in Computer Technology. Now days cloud computing is one of the fast growing technology because of online, cheap and pay as use scheme. Cloud Computing involves the concepts of parallel processing and distributed computing in order to provide the shared resources by means of Virtual Machines(VMs) hosted by physical servers. It is a service oriented design that reduces the cost of access to gather the information of the clients offer greater flexibility and demand based services. Cloud computing is emerging fastly and no doubt it is the next generation technology where humans will be using anywhere and anytime. In this internet world cloud computing is raising high by providing everything incense the required resources, applications, software, hardware, computing power to computing infrastructure, business process to control collaboration. Apart of its popularity it has some concerns which are becoming huddles for its wider adoption. In this paper a study has been made on virtualization concerns. In this paper, we present a complete survey of cloud computing and virtual machine migration.

scholarly journals A Survey of The Various Techniques for Virtualization in Cloud Computing

2021 ◽  
Vol 9 (10) ◽  
pp. 52-55
Author(s):  
Er. Mandeep Kaur
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
The Internet ◽  
Shared Resources ◽  
Software Infrastructure ◽  
Storage Devices ◽  
Service Oriented ◽  
The Cost ◽  
And Storage ◽  
Technology Solution

Abstract: Cloud computing is used to describe the delivery of software, infrastructure and storage devices over the internet. After evolution of the internet, Cloud computing is the next stage. Cloud Computing can simply the way in which the business operates, particularly in terms of needs of hardware. One is able to access and connect the same information but it can be done from anywhere and a more streamed technology installation is enjoyed by organization. VCloud Computing involves the concepts of parallel processing and distributed computing in order to provide the shared resources by means of Virtual Machines(VMs) hosted by physical servers. It is a service oriented design that reduces the cost of access to gather the information of the clients offer greater flexibility and demand based services. The benefits of Cloud Computing are far reaching. It is not a technology solution or server stored in another location but it is business enhanced computing that affects the business positively. Apart of its popularity it has some concerns which are becoming huddles for its wider adoption. A survey of cloud computing and virtual migration is presented in this paper. Keywords: Cloud Computing, Virtualization, SaaS, PaaS, IaaS

scholarly journals Fibonacci Series based Virtual Machine Selection for Load Balancing in Cloud Computing

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1071
Author(s):  
Japman Kaur Dhaliwal ◽  
Mohd Naseem ◽  
Aadil Ahamad Lawaye ◽  
Ehtesham Husain Abbasi
Keyword(s):  
Cloud Computing ◽  
Execution Time ◽  
Large Scale ◽  
Virtual Machines ◽  
Fibonacci Sequence ◽  
Round Robin ◽  
Shared Resources ◽  
Process Data ◽  
Resource Saving ◽  
The Cost

The rapid advancement of the internet has given birth to many technologies. Cloud computing is one of the most emerging technology which aim to process large scale data by using the computational capabilities of shared resources. It gives support to the distributed parallel processing. Using cloud computing, we can process data by paying according to its uses which eliminates the requirement of device by individual users. As cloud computing grows, more users get attracted towards it. However, providing an efficient execution time and load distribution is a major challenging issue in the distributed systems. In our approach, weighted round robin algorithm is used and benefits of Fibonacci sequence is combined which results in better execution time than static round robin. Relevant virtual machines are chosen and jobs are assigned to them. Also, number of resources being utilized concurrently is reduced, which leads to resource saving thereby reducing the cost. There is no need to deploy new resources as resources such as virtual machines are already available.  

Receiver Initiated Deadline Aware Load Balancing Strategy (RDLBS) for Cloud Environment

2017 ◽  
Vol 8 (3) ◽  
pp. 53-73
Author(s):  
Raza Abbas Haidri ◽  
Chittaranjan Padmanabh Katti ◽  
Prem Chandra Saxena
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Load Balancing ◽  
Virtual Machines ◽  
Turnaround Time ◽  
Cloud Environment ◽  
Computing Technology ◽  
Trade Off ◽  
The Cost ◽  
State Of Art

The emerging cloud computing technology is the attention of both commercial and academic spheres. Generally, the cost of the faster resource is more than the slower ones, therefore, there is a trade-off between deadline and cost. In this paper, the authors propose a receiver initiated deadline aware load balancing strategy (RDLBS) which tries to meet the deadline of the requests and optimizes the rate of revenue. RDLBS balances the load among the virtual machines (VMs) by migrating the request from the overloaded VMs to underloaded VMs. Turnaround time is also computed for the performance evaluation. The experiments are conducted by using CloudSim simulator and results are compared with existing state of art algorithms with similar objectives.

scholarly journals Enhancing the Distribution of Idle Cost for Scheduling Tasks without Setup Cost in Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Redwan A. Al-dilami ◽  
Ammar T. Zahary ◽  
Adnan Z. Al-Saqqaf
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Setup Time ◽  
Setup Cost ◽  
Idle State ◽  
The Cost ◽  
Computing Infrastructure ◽  
Better Than

Issues of task scheduling in the centre of cloud computing are becoming more important, and the cost is one of the most important parameters used for scheduling tasks. This study aims to investigate the problem of online task scheduling of the identified job of MapReduce on cloud computing infrastructure. It was proposed that the virtualized cloud computing setup comprised machines that host multiple identical virtual machines (VMs) that need to be activated earlier and run continuously, and booting a VM requires a constant setup time. A VM that remains running even though it is no longer used is considered an idle VM. Furthermore, this study aims to distribute the idle cost of the VMs rather than the cost of setting up them among tasks in a fair manner. This study also is an extension of previous studies which solved the problems that occurred when distributing the idle cost and setting up the cost of VMs among tasks. It classifies the tasks into three groups (long, mid, and short) and distributes the idle cost among the groups then among the tasks of the groups. The main contribution of this paper is the developing of a clairvoyant algorithm that addressed important factors such as the delay and the cost that occurred by waiting to setup VM (active VM). Also, when the VMs are run continually and some VMs become in idle state, the idle cost will be distributed among the current tasks in a fair manner. The results of this study, in comparison with previous studies, showed that the idle cost and the setup cost that was distributed among tasks were better than the idle cost and the setup cost distributed in those studies.

scholarly journals A Decentralized Virtual Machine Migration Approach of Data Centers for Cloud Computing

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoying Wang ◽  
Xiaojing Liu ◽  
Lihua Fan ◽  
Xuhan Jia
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
Quality Of Services ◽  
Virtual Machine Migration ◽  
Power Models ◽  
Key Steps

As cloud computing offers services to lots of users worldwide, pervasive applications from customers are hosted by large-scale data centers. Upon such platforms, virtualization technology is employed to multiplex the underlying physical resources. Since the incoming loads of different application vary significantly, it is important and critical to manage the placement and resource allocation schemes of the virtual machines (VMs) in order to guarantee the quality of services. In this paper, we propose a decentralized virtual machine migration approach inside the data centers for cloud computing environments. The system models and power models are defined and described first. Then, we present the key steps of the decentralized mechanism, including the establishment of load vectors, load information collection, VM selection, and destination determination. A two-threshold decentralized migration algorithm is implemented to further save the energy consumption as well as keeping the quality of services. By examining the effect of our approach by performance evaluation experiments, the thresholds and other factors are analyzed and discussed. The results illustrate that the proposed approach can efficiently balance the loads across different physical nodes and also can lead to less power consumption of the entire system holistically.

scholarly journals DYNAMIC VIRTUAL MACHINE MIGRATION USING RATIO-BASED METHOD

2020 ◽  
pp. 1-4
Author(s):  
Haresh Damjibhai Khachariya ◽  
Jayesh N. Zalavadia
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Virtual Machines ◽  
Cloud Services ◽  
Virtual Machine Migration ◽  
Reduce Power Consumption ◽  
Efficient Resource ◽  
Cloud Datacenter ◽  
Save Energy

Cloud computing provides various services over the internet and its increasing day by day.Given the growing demands of cloud services, it requires a lot of computing resources to meet customer needs. So, the addition of energy consumption through cloud computing resources will increase day by day and become a key obstacle in the cloud environment.In cloud computing,data centers consume more energy and additionally release carbon dioxide into the atmosphere. To reduce energy consumption through the cloud datacenter, energy-efficient resource management is required. In this paper a specific technique for performing virtual machines through datacenter is given. Our goal is to reduce power consumption on the datacenter by reducing the host running in the cloud datacenter. To reduce power consumption, schedule the incoming task such a way that all the resources like ram,cpu(mips) and bandwidth utilize in equal weightage.Then after if any host is over utilized then migrate one or more vm from that host to another host as well as if any host is underutilize then migrate running vm of that host and switch off the under loaded host to save energy.

scholarly journals Efficient Resources Allocation and Energy Reduction with Virtual Machines for Cloud Computing

2021 ◽  
Vol 11 (2) ◽  
pp. 52-58
Author(s):  
Anand Mehta ◽  
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Support Vector ◽  
Virtual Machine Migration ◽  
Machine Learning Methods ◽  
Host Machine ◽  
Application Deployment ◽  
Consumption Energy ◽  
Best Fit

Cloud computing is an internet provisioned method for sharing the resources on demand by network management, storage, services, applications and the serves that necessitate management optimal effort. VMM (virtual machine migration) plays a major role in enhancing the resource utilization, application isolation, processing nodes, fault tolerance in VMs for enhancing nodes portability and for maximizing the efficiency of physical server. For balancing the clouds with resources for the enhanced performance, varied users are served with application deployment in the cloud environment is considered as the major task. The user can rent or request the resources when it becomes significant. The emphasis of this paper is on different energy VM energy efficient module as per machine learning methods. While allocating the VMs to the host machines, MBFD (Modified Best Fit Decreasing) is considered and the classification of host machine capability such as overloaded, normal loaded and underloaded is executed according to SVM (Support vector machine). SVM is utilized as a classifier for analyzing the MBFD algorithm and for the classification of the host as per the job properties. In this procedure, the numbers of jobs that are not allocated are examined via simulation which is computed by means of time consumption, energy consumption and a total number of migrations.

scholarly journals Design of a Fault Tolerant Strategy for Resource Scheduling in Cloud Environment

2019 ◽  
Vol 9 (1) ◽  
pp. 5121-5128
Keyword(s):  
Cloud Computing ◽  
Failure Rate ◽  
Virtual Machines ◽  
Fault Tolerant ◽  
Previous History ◽  
Resource Scheduling ◽  
Efficient Manner ◽  
Computing Power ◽  
Efficient Resource

Cloud computing supports the technological need of the industry supporting many other technologies. Also, the demand for computing power and storage by recent technologies is reasonably growing in a drastic way. Cloud computing, serving for these technologies are to be developed with advancements that lead to performance improvement both in support to the technologies like block-chain and big data. The allocation of cloud resources is an important strategy to be followed in a wiser manner to incorporate the needs of extra ordinary computing power. In this paper, an efficient resource allocation strategy (FTVMA) is introduced that involves the creation of effective virtual machines (VMs) and performs VM allocation in an efficient manner by considering the failure rates, previous history of failure of VM, execution efficiency as a part of effective scheduling. There exist many reasons for cloudlet failure in VMs. Some of them are overloading of VMs and non-availability of VMs. The introduced FTVMA algorithm considers the failure rate of the physical machine, load of virtual machines and the cost priority of the tasks in order to achieve Quality of Service (QoS) and Quality of Experience (QoE) of the user. The FTVMA methodology proposed in this paper works better for computation intensive VMs and is tested using CloudSim environment. The QoS metrics used to measure the performance of the proposed algorithm are Makespan and VM Utilization. The metric to measure QoE are Priority Miss Rate and Failure Rate. The proposed algorithm shows its improvement in terms of the QoS and QoE metrics. The results obtained are compared with the existing resource scheduling algorithms and it is inferred that the proposed algorithm performs better in terms of QoS and QoE.

scholarly journals Efficient Cost Optimization using Transportation Mechanisms for Cloud Computing Environment

2020 ◽  
Vol 9 (4) ◽  
pp. 1630-1639
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Low Cost ◽  
Cost Optimization ◽  
Transportation Cost ◽  
Cloud Service ◽  
Computing Environment ◽  
Cloud Service Provider ◽  
Cloud Computing Environment ◽  
The Cost

Efficient computations are increasing now a day, so their need is very high in the world. Infrastructure and computation techniques are not as much as efficient in conventionally or in present scenario, therefore the cloud computing is new to deal this type of problems. Sequencing of hardware and software technologies, for giving scalable and low cost computational understandings in cloud computing. The major focus of this research is to diminish the transportation cost of resource allocation along with various virtual machines in cloud computing environment. In this research paper, implementation of Vogel's Approximation Method (VAM) to obtain an Initial Basic Feasible Solution (IBFS) and an algorithm to optimize the cost of resource transportations for cloud service provider (CSP) as well as present an example also to understand the proposed method for total supply values and total demand values. Although the calculation of cost reduction until the iteration still has a non-negative values, and the calculation is done again until the last iteration. A comparison has been shown the cost of the proposed mechanism is much less from other technique.

Optimal Green Virtual Machine Migration Model

2013 ◽  
Vol 9 (3) ◽  
pp. 35-52 ◽  
Author(s):  
Noah Sabry ◽  
Paul Krause
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Carbon Footprint ◽  
Virtual Machines ◽  
Clean Energy ◽  
Cloud Services ◽  
Virtual Machine Migration ◽  
Migration Model ◽  
Data Centres

Cloud computing provides the opportunity to migrate virtual machines to “follow-the-green” data centres. That is, to migrate virtual machines between green data centres on the basis of clean energy availability, to mitigate the environmental impact of carbon footprint emissions and energy consumption. The virtual machine migration problem can be modelled to maximize the utility of computing resources or minimizing the cost of using computing resources. However, this would ignore the network energy consumption and its impact on the overall CO2 emissions. Unless this is taken into account the extra data traffic due to migration of data could then cause an increase in brown energy consumption and eventually lead to an unintended increase in carbon footprint emissions. Energy consumption is a key aspect in deploying distributed service in cloud networks within decentralized service delivery architectures. In this paper, the authors address an optimization view of the problem of locating a set of cloud services on a set of sites green data centres managed by a service provider or hybrid cloud computing brokerage. The authors’ goal is to minimize the overall network energy consumption and carbon footprint emissions for accessing the cloud services for any pair of data centres i and j. The authors propose an optimization migration model based on the development of integer linear programming (ILP) models, to identify the leverage of green energy sources with data centres and the energy consumption of migrating VMs.

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