Research on Trigger Strategy of Virtual Machine Dynamic Migration in Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 2084-2087
Author(s):  
Chun Ling An ◽  
Chun Lin Li ◽  
You Long Luo ◽  
Su Jie He
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Historical Data ◽  
Experimental Results ◽  
Migration Process ◽  
Virtual Machine Migration ◽  
Trigger Strategy ◽  
Double Threshold

According to the trigger strategy of virtual machine dynamic migration based on features closed in the process of dynamic migration of virtual machines in the cloud computing, this paper puts forward a double threshold trigger strategy using timing prediction based on historical data (DTS Algorithms). Then simulation on the CloudSim platform, and analyze the results of the experiment. Experimental results showed that in the system virtual machine migration using DTS algorithm can reduce the number of migration and the energy consumption during the migration process.

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.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

Virtual Machines Scheduling Algorithm Based on Multi-Objective Optimization in Cloud Computing

2014 ◽  
Vol 1046 ◽  
pp. 508-511
Author(s):  
Jian Rong Zhu ◽  
Yi Zhuang ◽  
Jing Li ◽  
Wei Zhu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Machine Scheduling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Virtual Machine Scheduling ◽  
Set Up

How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.

scholarly journals Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2724 ◽  
Author(s):  
Yuan ◽  
Sun
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machines ◽  
Ant Colony ◽  
Virtual Machine Migration ◽  
Server Consolidation ◽  
Cloud Data ◽  
Cloud Data Centers

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.

Three Misuse Patterns for Cloud Computing

2013 ◽  
pp. 36-53 ◽  
Author(s):  
Keiko Hashizume ◽  
Nobukazu Yoshioka ◽  
Eduardo B. Fernandez
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Point Of View ◽  
Resource Usage ◽  
Migration Process ◽  
Virtual Machine Migration ◽  
On Demand ◽  
Computing Model ◽  
Is Security

Cloud computing is a new computing model that allows providers to deliver services on demand by means of virtualization. One of the main concerns in cloud computing is security. In particular, the authors describe some attacks in the form of misuse patterns, where a misuse pattern describes how an attack is performed from the point of view of the attacker. Specially, they describe three misuse patterns: Resource Usage Monitoring Inference, Malicious Virtual Machine Creation, and Malicious Virtual Machine Migration Process.

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 Low Power Consumption on Cloud Data Centers Using HSA Algorithm

2020 ◽  
pp. 78-84
Author(s):  
Louay Al Nuaimy ◽  
Tadele Debisa Deressa ◽  
Mohammad Mastan ◽  
Syed Umar
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Rapid Development ◽  
Electricity Consumption ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Cloud Data ◽  
The Right

The rapid development of knowledge and communication has created a new processing style called cloud computing. One of the key issues facing cloud infrastructure providers is minimizing costs and maximizing profitability. Power management in cloud centres is very important to achieve this. Energy consumption can be reduced by releasing inactive nodes or by reducing the migration of virtual machines. The second is one of the challenges of choosing the virtual machine deployment method to migrate to the right node. This article proposes an approach to reduce electricity consumption in cloud centres. This approach adapts Harmony's search algorithm to move virtual machines. Positioning is done by sorting nodes and virtual machines according to their priorities in descending order. Priority is calculated based on the workload. The proposed procedure is envisaged. The evaluation results show less virtual machine migration, greater efficiency and lower energy consumption.

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 Task Scheduling Algorithm Based on Bacterial Foraging Optimization (BFO) in Cloud Computing

2018 ◽  
Vol 7 (1) ◽  
pp. 16-19
Author(s):  
Anupama Gupta ◽  
Kulveer Kaur ◽  
Rajvir Kaur
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Execution Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Bacterial Foraging Optimization ◽  
Task Scheduling Algorithm ◽  
Steady Rate ◽  
Time And Energy

Cloud computing is the architecture in which cloudlets are executed by the virtual machines. The most applicable virtual machines are selected on the basis of execution time and failure rate. Due to virtual machine overloading, the execution time and energy consumption is increased at steady rate. In this paper, BFO technique is applied in which weight of each virtual machine is calculated and the virtual machine which has the maximum weight is selected on which cloudlet will be migrated. The performance of proposed algorithm is tested by implementing it in CloudSim and analyzing it in terms of execution time, energy consumption.

scholarly journals A novel energy efficient virtual machine configuration and migration technique

2018 ◽  
Vol 7 (4) ◽  
pp. 2391
Author(s):  
L Srinivasa Rao ◽  
I Raviprakash Reddy
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Energy Efficient ◽  
Virtual Machines ◽  
The Novel ◽  
Virtual Machine Migration ◽  
Machine Configuration ◽  
And Migration ◽  
The Cost ◽  
Migration Technique

The recent growth in the data centre usage and the higher cost of managing virtual machines clearly demands focused research in reducing the cost of managing and migrating virtual machines. The cost of virtual machine management majorly includes the energy cost, thus the best available virtual machine management and migration techniques must have the lowest energy consumption. The management of virtual machine is solely dependent on the number of applications running on that virtual machine, where there is a very little scope for researchers to improve the energy. The second parameter is migration in order to balance the load, where a number of researches are been carried out to reduce the energy consumption. This work addresses the issue of energy consumption during virtual machine migration and proposes a novel virtual machine migration technique with improvement of energy consumption. The novel algorithm is been proposed in two enhancements as VM selection and VM migration, which demonstrates over 47% reduction in energy consumption.  

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