An Efficient Virtual Machine Migration Algorithm Based on Minimization of Migration in Cloud Computing

Chien, Nguyen Khac; Dong, Vo Sy Giang; Sơn, Nguyễn Hồng; Loc, Ho Dac

doi:10.1007/978-3-319-46909-6_7

Cited by 12 publications

(6 citation statements)

References 3 publications

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“…This algorithm uses energy‐conscious resource utilisation techniques based on artificial bee colony to deliver the critical resources to user applications, to allow better use of resources, and to decrease the energy consumption in the cloud computing environment. The live migration technique for VM migration in cloud computing is implemented in [11 ]. Once resource rivalry turns into more oriented direct utilisation of resources crosses a convinced threshold, this technique of selecting VM requiring minimum migration time (MMT) and the left over available processor competence is minimal to trigger VM migration, resulting in less SLA breach.…”

Section: Related Workmentioning

confidence: 99%

Rough‐set and machine learning‐based approach for optimised virtual machine utilisation in cloud computing

Shylaja

Bhaskar

2020

IET Networks

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To meet rising demands for computing resources, information technology service providers need to select cloudbased services for their vitality and elasticity. Enormous numbers of data centres are designed to meet customer needs. Burning up energy by data centre is very high with the large-scale deployment of cloud data centres. Virtual machine consolidation strategy implementation reduces the data centre energy consumption and guarantees service level agreements. This study proposes a machine learning-based method in cloud computing for the automated use of virtual machines. Machine learningbased virtual machine selection approach integrates the migration control mechanism that enhances selection strategy efficiency. The experiment is performed with various real machine workload circumstances to provide proof and effectiveness of the proposed method. The exploratory outcome shows that the proposed approach streamlines the utilisation of the virtual machine and diminishes the consumption of energy and improves infringement of service level agreements to accomplish better performance.

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Section: Related Workmentioning

confidence: 99%

Rough‐set and machine learning‐based approach for optimised virtual machine utilisation in cloud computing

Shylaja

Bhaskar

2020

IET Networks

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“…There are some other approaches which have been proposed for improving the VM migration. [20][21]. VM migration technique is generally used to solve the load balancing problem in cloud environment.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment

Kushwaha*¹,

Amjad²,

Kumar³

2019

IJITEE

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Cloud computing is a framework which provides on-demand services to the user for scalability, security, and reliability based on pay as used service anytime & anywhere. For load balancing, task scheduling is the most critical issues in the cloud environment. There are so many meta-heuristic algorithms used to solve the load balancing problem. A good task scheduling algorithm should be used for optimum load balancing in cloud environment. Such scheduling algorithm must have some vital characteristic like minimum makespan, maximum throughput, and maximum resource utilization, etc. In this paper, a dynamic load balancing and task scheduling algorithm based on ant colony optimization (DLBACO) has been proposed. This algorithm assigns the task the VM which has highest probability of availability in minimum time. The proposed algorithm balances the whole system by minimizing the makespan of the task and maximizing the throughput. CloudSim simulator is used to simulate the proposed scheduling algorithm and results show that the proposed (DLBACO) algorithm is better than the existing algorithms such as FCFS, LBACO (Load balancing ACO), and primary ACO

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“…Experiments indicate that the proposed algorithm is shown to be able to balance network resource load in cloud computing effectively. Chien, et al [24] have proposed an efficient VM migration algorithm based on minimizing migrations in cloud computing to improve efficiency, meet user requirements, and prevent service level agreements (SLA) violations. The proposed algorithm was more effective than existing algorithms based on experimental results.…”

Section: Introductionmentioning

confidence: 99%

“…Fu, et al [23] propose a layered VM migration algorithm for network load balancing. Chien, et al [24] emphasize minimizing migrations to prevent SLA violations. Kaur and Sachdeva [25] propose a threshold algorithm for efficient task allocation.…”

Section: Introductionmentioning

confidence: 99%

Black Widow Optimization Algorithm for Virtual Machines Migration in the Cloud Environments

Zhou

2023

IJACSA

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Cloud data centers use virtualization technology to manage computing resources. Using a group of connected Virtual Machines (VMs), corresponding users can compute data efficiently and effectively. It improves the utilization of resources, thereby reducing hardware requirements. Repossession of affected services requires VM-based infrastructure overhaul schemes. Clarifications concerning dedicated routing are also desirable to improve the reliability of Domain Controller (DC) services. The migration of a VM experiencing a node failure challenges maintaining reliability. The selection of VMs is influential in limiting the number of VM migrations. Choosing one or more potential VMs for migration reduces the servers' workload. This paper presents an energy-aware VM migration method for cloud computing based on the Black Widow Optimization (BWO) algorithm. The proposed algorithm was implemented and measured using JAVA. Afterward, we compared our results against existing methodologies regarding resource availability, energy consumption, load, and migration cost.

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An Efficient Virtual Machine Migration Algorithm Based on Minimization of Migration in Cloud Computing

Cited by 12 publications

References 3 publications

Rough‐set and machine learning‐based approach for optimised virtual machine utilisation in cloud computing

Rough‐set and machine learning‐based approach for optimised virtual machine utilisation in cloud computing

Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment

Black Widow Optimization Algorithm for Virtual Machines Migration in the Cloud Environments

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