A Hybrid Approach for Virtual Machine Migration in Cloud Computing Environment

A lot of research has been done in the field of cloud computing in computing domain. For its effective performance, variety of algorithms has been proposed. The role of virtualization is significant and its performance is dependent on VM Migration and allocation. More of the energy is absorbed in cloud; therefore, the utilization of numerous algorithms is required for saving energy and efficiency enhancement in the proposed work. In the proposed work, green algorithm has been considered with meta heuristic algorithms, ABC (Artificial Bee colony .Every server has to perform different or same functions. A cloud computing infrastructure can be modelled as Primary Machineas a set of physical Servers/host PM1, PM2, PM3… PMn. The resources of cloud infrastructure can be used by the virtualization technology, which allows one to create several VMs on a physical server or host and therefore, lessens the hardware amount and enhances the resource utilization. The computing resource/node in cloud is used through the virtual machine. To address this problem, data centre resources have to be managed in resource -effective manner for driving Green Cloud computing that has been proposed in this work using Virtual machine concept with ABC and Neural Network optimization algorithm. The simulations have been carried out in CLOUDSIM environment and the parameters like SLA violations, Energy consumption and VM migrations along with their comparison with existing techniques will be performed.

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A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem

The Scientific World JOURNAL ◽

10.1155/2013/369209 ◽

2013 ◽

Vol 2013 ◽

pp. 1-13 ◽

Cited By ~ 6

Author(s):

Gaochao Xu ◽

Yan Ding ◽

Jia Zhao ◽

Liang Hu ◽

Xiaodong Fu

Keyword(s):

Cloud Computing ◽

Virtual Machine ◽

Artificial Bee Colony ◽

Power Saving ◽

Bayes Theorem ◽

Virtual Machine Migration ◽

Cloud Data Center ◽

Vm Migration ◽

Cloud Data ◽

Bee Colony

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration’s ability and local exploitation’s ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

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A virtual machine migration policy for multi-tier application in cloud computing based on game theory

10.21203/rs.3.rs-261767/v1 ◽

2021 ◽

Author(s):

Hung Cong Tran ◽

Khiet Thanh Bui ◽

Hung Dac Ho ◽

Vu Tran Vu

Keyword(s):

Game Theory ◽

Cloud Computing ◽

Virtual Machine ◽

Virtual Machines ◽

Service Providers ◽

Learning Algorithm ◽

Energy Performance ◽

Cloud Infrastructure ◽

Virtual Machine Migration ◽

Extraction Phase

Abstract Cloud computing technology provides shared computing which can be accessed over the Internet. When cloud data centers are flooded by end-users, how to efficiently manage virtual machines to balance both economical cost and ensure QoS becomes a mandatory work to service providers. Virtual machine migration feature brings a plenty of benefits to stakeholders such as cost, energy, performance, stability, availability. However, stakeholder's objectives are usually conflicted with each other. Also, the optimal resource allocation problem in cloud infrastructure is usually NP-Hard or NP-Complete class. In this paper, the virtual migration problem is formulated by applying game theory to ensure both load balance and resource utilization. The virtual machine migration algorithm, named V2PQL, is proposed based on Markov Decision Process and Q-learning algorithm. The results of the simulation demonstrate the efficiency of our proposal which are divided into training phase and extraction phase. The proposed V2PQL policy has been benchmarked to the Round-Robin policy in order to highlight their strength and feasibility in policy extraction phase.

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Layered virtual machine migration algorithm for network resource balancing in cloud computing

Frontiers of Computer Science ◽

10.1007/s11704-016-6135-9 ◽

2017 ◽

Vol 12 (1) ◽

pp. 75-85 ◽

Cited By ~ 17

Author(s):

Xiong Fu ◽

Juzhou Chen ◽

Song Deng ◽

Junchang Wang ◽

Lin Zhang

Keyword(s):

Cloud Computing ◽

Virtual Machine ◽

Virtual Machine Migration ◽

Network Resource

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A Stackleberg Game Theory and Improved Fuzzy Based Intrusion Detection Approach for Virtual Machine Migration Timing Problem in Cloud Computing

EAI Endorsed Transactions on Cloud Systems ◽

10.4108/eai.12-10-2020.166552 ◽

2020 ◽

Vol 6 (19) ◽

pp. 166552

Author(s):

Balamurugan E ◽

Md. Almamum ◽

Md. Hasan ◽

Sangeetha K

Keyword(s):

Game Theory ◽

Cloud Computing ◽

Intrusion Detection ◽

Virtual Machine ◽

Virtual Machine Migration ◽

Migration Timing ◽

Detection Approach

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Modified Flower Pollination based Task Scheduling in Cloud Environment using Virtual Machine Migration

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i8325.078919 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1856-1860

Keyword(s):

Cloud Computing ◽

Virtual Machine ◽

Resource Selection ◽

Heuristic Algorithms ◽

Past History ◽

Fitness Function ◽

Task Type ◽

Flower Pollination Algorithm ◽

Flower Pollination ◽

Selection Of

Cloud computing allows users to use resources pay per use model by the help of internet. Users are able to do computation dynamically from different location by using internet resources. The major challenging task in cloud computing is efficient selection of resources for the tasks submitted by users. A number of heuristics and meta-heuristics algorithms are designed by different researchers. The most critical phase is the selection of appropriate resource and its management. The selection of resource include to identify list of authenticated available resources in the cloud for job submission and to choose the best resource. The best resource selection is done by the analysis of several factors like expected time to execute a task by user, access restriction to resources, and expected cost to use resources. In this paper, cloud architecture for resource selection is proposed which combines these factors and make the effective resource selection. In this paper a modified flower pollination algorithm is proposed to migrate the task on efficient virtual machine. The selection of the efficient virtual machine is calculated by the fitness function. By calculating the fitness function, the modified FPA algorithm is used to take the decision regarding VM migration is required to improve the resource efficiency or not. In this paper Virtual machine mapper maps the task as per knowledge base i.e. past history of the virtual machine, task type whether computational or communicational based. The results are compared with the existing meta-heuristic algorithms.

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