scholarly journals Virtual machines pre-copy live migration cost modeling and prediction: a survey

2021 ◽  
Author(s):  
Mohamed Esam Elsaid ◽  
Hazem M. Abbas ◽  
Christoph Meinel
Keyword(s):  
Cloud Computing ◽  
Cost Analysis ◽  
Virtual Machines ◽  
Essential Feature ◽  
Power Saving ◽  
Live Migration ◽  
Survey Paper ◽  
Migration Cost ◽  
Prediction Techniques ◽  
Migration Costs

AbstractLive migration is an essential feature in virtual infrastructure and cloud computing datacenters. Using live migration, virtual machines can be online migrated from a physical machine to another with negligible service interruption. Load balance, power saving, dynamic resource allocation, and high availability algorithms in virtual data-centers and cloud computing environments are dependent on live migration. Live migration process has six phases that result in live migration cost. Several papers analyze and model live migration costs for different hypervisors, different kinds of workloads and different models of analysis. In addition, there are also many other papers that provide prediction techniques for live migration costs. It is a challenge for the reader to organize, classify, and compare live migration overhead research papers due to the broad focus of the papers in this domain. In this survey paper, we classify, analyze, and compare different papers that cover pre-copy live migration cost analysis and prediction from different angels to show the contributions and the drawbacks of each study. Papers classification helps the readers to get different studies details about a specific live migration cost parameter. The classification of the paper considers the papers’ research focus, methodology, the hypervisors, and the cost parameters. Papers analysis helps the readers to know which model can be used for which hypervisor and to know the techniques used for live migration cost analysis and prediction. Papers comparison shows the contributions, drawbacks, and the modeling differences by each paper in a table format that simplifies the comparison. Virtualized Data-center and cloud computing clusters admins can also make use of this paper to know which live migration cost prediction model can fit for their environments.

An Optimal Way of VM Placement Strategy in Cloud Computing Platform Using ABCS Algorithm

2021 ◽  
Vol 12 (3) ◽  
pp. 16-38
Author(s):  
Pushpa R. ◽  
M. Siddappa
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Vm Placement ◽  
Migration Strategy ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Migration Cost ◽  
Chicken Swarm Optimization

In this paper, VM replacement strategy is developed using the optimization algorithm, namely artificial bee chicken swarm optimization (ABCSO), in cloud computing model. The ABCSO algorithm is the integration of the artificial bee colony (ABC) in chicken swarm optimization (CSO). This method employed VM placement based on the requirement of the VM for the completion of the particular task using the service provider. Initially, the cloud system is designed, and the proposed ABCSO-based VM placement approach is employed for handling the factors, such as load, CPU usage, memory, and power by moving the virtual machines optimally. The best VM migration strategy is determined using the fitness function by considering the factors, like migration cost, load, and power consumption. The proposed ABCSO method achieved a minimal load of 0.1688, minimal power consumption of 0.0419, and minimal migration cost of 0.0567, respectively.

scholarly journals Self - Adaptive Load Balancing Using Live Migration of Virtual Machines in Cloud Environment

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 735-745
Author(s):  
V. Lavanya ◽  
M. Saravanan ◽  
E.P. Sudhakar
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Live Migration ◽  
Management Activity ◽  
Cloud Computing Environment ◽  
Efficient Resource ◽  
Server Overload ◽  
Self Adaptive

In this paper, a self-adaptive load balancing technique is proposed using live migration of heterogeneous virtual machines (VM) in a Hyper-V based cloud environment. A cloud supported plugin as a management activity within the infrastructure as a service strategy. It is proposed to assist the load balancing process in such a way so that all hypervisors are almost equally loaded once the overload status gets triggered. In the cloud computing environment, load balancing plays a major role if the large number of events triggered has a high impact on the performance of the system. The efficiency of cloud computing is based on the efficient load balancing having a self-adjustable technique using live migration of VMs across clusters of nodes. The proposed load balancing model is efficient in performance improvement by efficient resource utilization and also it helps to avoid the situation occurrence of server hanging by the cause of server overload within the infrastructure of multiple Microsoft Hyper-V hypervisors environment.

scholarly journals EeadSelfCloud: Energy Efficient Adaptive Depth Self Cloud Mechanism for VM Migration in Data Centers

2019 ◽  
Vol 8 (10) ◽  
pp. 2236-2244
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Power Saving ◽  
Human Beings ◽  
Hop Count ◽  
Vm Migration ◽  
Self Organizing

Cloud computing, with its great potential in low cost and demanding services, is a good computing platform. Modern data centers for cloud computing are facing the difficulty of consistently increasing complexity because of the expanding quantity of clients and their enlarging resource demands. A great deal of efforts are currently focused on giving the cloud framework with autonomic behavior , so it can take decision about virtual machine (VM) management over the datacenter without intervention of human beings. Most of the self-organizing solutions results in eager migration, which attempts to diminish the amount of working servers virtual machines. These self-organizing resolution produce needless migration due to unpredictable workload. So also it consume huge amounts of electrical energy during unnecessary migration process. To overcome this issue, this project develop one novel VM migration scheme called eeadSelfCloud. The proposed schema is used to change the virtual machine in a cloud center that requires a lot of factors, such as basic requirements for resources during virtual machine setup, dynamic resource allocation, top software loading, software execution, and power saving at the Data Center. Data Center Utilization, Average Node Utilization, Request Rejection Ration, Number of Hop Count and Power Consumption are taken as constraint for measuring the proposed approach. The analysis report depicted that the proposed approach performs best than the other existing approaches.

scholarly journals Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers

2018 ◽  
Vol 10 (9) ◽  
pp. 86 ◽  
Author(s):  
Samah Alshathri ◽  
Bogdan Ghita ◽  
Nathan Clarke
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Data Storage ◽  
Resource Utilization ◽  
Data Centers ◽  
Virtual Machines ◽  
Energy Optimization ◽  
Optimization Method ◽  
Live Migration ◽  
Novel Algorithm

The cloud-computing concept has emerged as a powerful mechanism for data storage by providing a suitable platform for data centers. Recent studies show that the energy consumption of cloud computing systems is a key issue. Therefore, we should reduce the energy consumption to satisfy performance requirements, minimize power consumption, and maximize resource utilization. This paper introduces a novel algorithm that could allocate resources in a cloud-computing environment based on an energy optimization method called Sharing with Live Migration (SLM). In this scheduler, we used the Cloud-Sim toolkit to manage the usage of virtual machines (VMs) based on a novel algorithm that learns and predicts the similarity between the tasks, and then allocates each of them to a suitable VM. On the other hand, SLM satisfies the Quality of Services (QoS) constraints of the hosted applications by adopting a migration process. The experimental results show that the algorithm exhibits better performance, while saving power and minimizing the processing time. Therefore, the SLM algorithm demonstrates improved virtual machine efficiency and resource utilization compared to an adapted state-of-the-art algorithm for a similar problem.

ABDM : Agent Based Live Migration of Virtual Machines in Cloud Computing For Multimedia Data

2018 ◽  
pp. 9-17
Author(s):  
K. Syed Ibrahim ◽  
Dr. A. R. Mohamed Shanavas
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Exit Time ◽  
Multimedia Data ◽  
Migration Time ◽  
Source Node ◽  
Destination Node ◽  
Live Migration ◽  
Agent Based ◽  
Total Migration

Migration time is one of the metric to measure the performance of the algorithm for live migration. In this paper we have introduced a new parameter for live migration of virtual machines (VM) called the ‘Exit Time’ which is defined as the time to eject the state of one or more VMs from the source node. Exit Time defines how rapidly the VM can be taken out from the source node and its resources are freed for reallocating other tasks. We present an Agent Based Live Migration which disconnects the source node from the destination node during migration to reduce the exit time if the destination is slow. The source distributes the memory of VMs to multiple intermediate nodes organized by a middleware. Simultaneously, the destination collects and merges the VMs’ memory from the intermediate nodes. Thus exit from the source node is no longer resisted by the receiving speed of the destination. We support simultaneous live exit of multiple VMs and our ABDM implementation in the CloudSim platform reduces the exit time by a considerable amount against the traditional pre-copy and post-copy migration at the same time keeping the total migration time when the destination node is sluggish than the source

scholarly journals POWER SAVING LOAD BALANCING STRATEGY USING DVFS IN CLOUD ENVIRONMENT

2016 ◽  
Vol 15 (13) ◽  
pp. 7333-7341 ◽  
Author(s):  
Sakshi Grover ◽  
Mr. Navtej Singh Ghumman
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Virtual Machines ◽  
Research Work ◽  
Power Saving ◽  
Reducing Power ◽  
Total Power ◽  
Software Infrastructure ◽  
Other Information ◽  
Living Organisms

Cloud Computing is a technology that provides a platform for the sharing of resources such as software, infrastructure, application and other information. Cloud Computing is being used widely all over the world by many IT companies as it provides benefits to the users like cost saving and ease of use.  However with the growing demands of users for computing services, cloud providers are encouraged to deploy large datacenters which consume very high amount of energy resulting in carbon dioxide emissions.  Power consumption is a key concern in data centers. That type of critical issues not only reduces the profit margin, but also has effect on high carbon production which is harmful for environment and living organisms. Reducing power consumption has been an important requirement for cloud resource providers not only to reduce operating costs, but also to improve system reliability. In research work, we have arranged the virtual machines in ascending order of the load. Cloudlets would be assigned to that virtual machine that has lesser load. Cloudlets are divided into three categories like high, medium and low on the basis of their instruction length. Dvfs approach which has been implemented in the paper would scale the power according to the length of the cloudlets. Three modes of Dvfs have been implemented in the research work. Various parameters like processing time, processing cost and total power consumed by all the cloudlets at the data center have been computed and analyzed. Cloudsim a toolkit for modeling and simulation of cloud computing environment has been used to implement and demonstrate the experimental results.

scholarly journals A SURVEY FOR ENERGY EFFICIENCY IN CLOUD DATA CENTERS

2017 ◽  
Vol 5 (4RACSIT) ◽  
pp. 63-68
Author(s):  
Dinesh Raj Paneru ◽  
Madhu B. R. ◽  
Santosh Naik
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machines ◽  
Live Migration ◽  
Vm Migration ◽  
Cloud Data ◽  
Platform As A Service ◽  
Cloud Data Centers ◽  
And Storage ◽  
Migration Technique

Services such as Platform as a Service (PaaS), Infrastructure as a Service (IaaS) and Software as a Service (SaaS) are provided by Cloud Computing. Subscription based computing resources and storage is offered in cloud. Cloud Computing is boosted by Virtualization technology. To move running applications or VMs starting with one physical machine then onto the next, while the customer is associated is named as Live VM migration. VM migration is empowered by means of Virtualization innovation to adjust stack in the server farms. Movement is done fundamentally to deal with the assets progressively. Server Consolidation’s main goal is to expel the issue of server sprawl. It tries to pack VMs from daintily stacked host on to fewer machines to satisfy assets needs. On other hand Load balancing helps in distributing workloads across multiple computing resources. Also in the presence of low loaded machines it avoids machines from getting overloaded and maintains efficiency. To balance the load across the systems in various cases, live migration technique is used with the application of various algorithms. The movement of virtual machines from completely stacked physical machines to low stacked physical machines is the instrument to adjust the entire framework stack. When we are worried about the energy consumption in Cloud Computing, VM consolidation & Server Consolidation comes into scenario in Virtual Machine movement method which itself implies that there is low energy consumption.

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