A Threshold Band Based Model for Automatic Load Balancing in Cloud Environment

Jain, Anubha; Yadav, Anagha; Namboodiri, Lohit; Abraham, Jibi

doi:10.1109/ccem.2013.6684425

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…Static approach is more apposite to cloud because user demand changes very frequently. To handle the overloaded and under loaded situation VM migration [9] is used which remap the VM from one PM to another PM. VM migration is the important feature of the virtualization and is used to deal with the overloaded, under loaded and hot spot situations.…”

Section: Fig1 Virtualization Technologymentioning

confidence: 99%

Enhanced Load Balancing Approach to Optimize the Performance of the Cloud Service using Virtual Machine Migration

Jain¹,

Sharma²

2017

IJEM

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Cloud computing is a fastest growing technology in the research and industry field.It provides the on demand resources to the customers on the rent basis. These resources are provided through the virtual machines. Resources required by the virtual machines can change dynamically. So load balancing in the cloud is more challenging task as compared to the traditional computing, where the resource requirements are not changed with time. Overall performance of the cloud system can be increased by the efficient load balancing approach. Three steps are involved in the load balancing method i.e., physical machine selection, virtual machine selection and destination physical machine selection. In the past few years a number of load balancing approaches have been proposed to increase the resource utilization and minimize the energy consumption. This paper has proposed a load balancing approach which uses the lower and upper threshold to select the physical machine (PM) for migrating the virtual machine (VM). Then place the selected VM to the PM which consumes minimum power to minimize the energy consumption.To create the cloud environment, CloudSim simulator is used which provides the interface to deal with the physical and virtual machines. To evaluate the performance, the proposed method is compared with already present load balancing approaches. Simulation result shows that proposed approach minimize the energy consumption, migrations and total simulation time.

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Section: Fig1 Virtualization Technologymentioning

confidence: 99%

Enhanced Load Balancing Approach to Optimize the Performance of the Cloud Service using Virtual Machine Migration

Jain¹,

Sharma²

2017

IJEM

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“…Hypervisor or VMM (Virtual Machine Manager) manages the creation, updation and deletion of VMs by allocating required resources. It also provides a duplicate image of the actual hardware to the VMs [5].…”

Section: Introductionmentioning

confidence: 99%

Balancing Load of Cloud Data Center using Efficient Task Scheduling Algorithm

Shaw¹

2017

IJCA

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Cloud computing is one of the most popular terms of today's computer world. The pay-as-you-use model of cloud permits users to pay only according to their requirement. The enormous increase in popularity of cloud is due to its ubiquitous use through common hardware only. So it must provide high performance gain to the user and at the same time must be beneficial for the Cloud Service Provider (CSP). To achieve this goal many challenges have to be faced. Load balancing is one of them. To distribute the load evenly in cloud the resources and workloads must be scheduled efficiently. A variety of scheduling algorithms are used by load balancers to determine which backend server to send a request to. The selected server allocates resources and schedules the job dynamically on some virtual machine (VM) located on the same physical machine. In this paper, we have proposed a task scheduling algorithm which will distribute the task among all the available virtual machines in a way such that none of them become overloaded. Further we have simulated our algorithm in CloudAnalyst and compared it with the existing load balancing algorithms. Results show that the proposed method not only balances the load more efficiently but also improves the response time.

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